esa-sceva/satcom-chunk-collection huggingface dataset:
# Heterologous Production of Flavour and Aroma Compounds in _Saccharomyces cerevisiae_
## 3 Heterologous Compounds Derived from Intermediates of the Mevalonate Pathway
### Isoprenoids
#### 3.1.1 Monoterpenes
**Sabinene** is another example of monoterpene that was reported to be produced in _S. cerevisiae_ by means of heterologous gene expression. This monoterpene can be naturally found and extracted from the essential oils of many plants, including _Hyptis pectinata_ Poit. (Lamiaceae) [58], or _Zornia diphyllla_ (L.) Pers, where it can reach more than 40% of the total oil extracted from whole plant [59]. In _S. cerevisiae_ production of sabinene was achieved by heterologous expression of highly specific sabinene synthase (SAS) from _Salvia pomifera_[60] (Figure 1). By introduction of fusion protein ERG20p(P96W-N127W)-_Sp_SASp nearly 1.9 mg/L of sabinene was produced by this recombinant yeast. Subsequent deletion of one copy of the wild type ERG20, and introduction of the second copy of fusion protein ERG20p(P96W-N127W)-_Sp_SAbS1 further improved sabinene production to 17.5 mg/L.
## 4 Simulation and Discussion
### Composite Scattering of Canyon/Valley Environment with Different Incident Angles  
This example investigates the bistatic composite scattering characteristics of the canyon and valley environment with different incident angles. The slope angle \(\alpha_{1}=30^{\circ}\), \(\alpha_{3}=30^{\circ}\), the RMS height \(h_{\text{rms}}=0.2\lambda\), and the wind speed \(v_{19.5}=0.2\) m/s. The incident pitch angles are \(-30^{\circ}\), \(-45^{\circ}\), \(-60^{\circ}\) respectively, while \(\varphi_{i}=90\), the observation angles are \((\theta_{s},\varphi_{s})=(-90^{\circ}\sim 90^{\circ},90^{\circ})\). The illustration of simulation geometry is the same as Section 4.3, which is shown in Figure 12.  
As shown in Figure 21, each curve has two peaks. One of the peaks is produced by the reflection of the incident wave from the bottom of the canyon/valley environment. The other one results from the multiple bounces of the EM waves in the semi-enclosed cavity structure. The main paths are shown in Figure 22. Please pay attention to Figure 21b; geometrically, there should be three peaks when the incident angle is \(45^{\circ}\); however, there are actually only two peaks, \(\theta_{s}=-15^{\circ}\) and \(\theta_{s}=45^{\circ}\). The reason may be that the paths marked by yellow reflect three times, and the scattering field decreases quickly after each reflection so that when the wave leaves the composite rough surface, the field is too weak to be recognized. Based on the results, the incident angle also decides the peak position. Though multiple bounces exist in the canyon/valley environment, the higher-order bounce is usually weak enough to ignore.  
Figure 20: Monostatic RCS simulation results: (**a**) canyon composite rough surface; (**b**) valley composite rough surface.  
Figure 21: Bistatic RCS simulation results: (**a**) canyon, HH-polarization; (**b**) canyon, VV-polarization; (**c**) valley, HH-polarization; (**d**) valley, VV-polarization.  
Figure 22: Cont.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 3 Opportunities Given by System-Level Testing
### Opportunities Given by SEE Tests
#### 3.2.2 New Insight into Details of the System's Radiation Performance  
Apart from the SET masking, there were also rare events observed during the CHARM UHE experiments, when SETs with the special signature (as on the right curve of Figure 4) were forcing power cycling of the device. The calculated cross-section for this event is between 1.3 \(\times\) 10\({}^{-8}\) and 2.3 \(\times\) 10\({}^{-8}\) cm\({}^{2}\), whereas the cross-section for SETs observed at the output of voltage reference chip is in order of \(\sim\)10\({}^{-5}\) cm\({}^{2}\). Detailed investigation of this phenomenon (presented fully in [6]) has shown that this specific signature of SETs is most probably due to double hits of ions at sensitive area of the chip, one hit after another shortly in time, therefore it is not something that should be expected in real operation conditions. Furthermore, it could be only observed after the significant degradation of the system due to cumulative effects, which was then leading to an increase of the voltage drop on the supply cables and a decrease of the supply voltage (as measured directly at PoL input). However, observation of such rare events enables to identify new failure modes (system-specific) and therefore to define error signatures for the system.  
The presented example of the \"rare event\" observation shows that the system level testing may also provide information on synergistic effects at the system level: observed propagation of the SET was enabled by system degradation due to cumulative effects. Such effect had not been observed earlier and was very difficult to predict by analysis or  
Figure 4: Example of SETs measured at voltage reference chip output, which caused power cycling (**left**) and which didn’t cause power cycling (**right**) during PoL heavy ion test at CHARM facility.  
component-level testing only. As for today, there is no procedure to evaluate synergistic effects and test at the system level seems to be the only way do to it.
## 4 Defense Against False Data Injection
### Experiments
#### 4.2.3 Results  
In this section, we evaluate the performance metrics of three different models: Logistic Regression, CNN, and CNN-GRU. These models were trained using the dataset described in the previous section.  
The training and validation accuracy and loss curves presented in Figure 7 illustrate the learning process of the CNN and CNN-GRU models. Both models demonstrate an improvement in accuracy throughout the training process, with CNN surpassing CNN-GRU in validation accuracy. The loss curves for both models exhibit a consistent decline, indicating effective learning.  
Figure 8 shows the ROC curves, which compare the performance of the three models in terms of their true positive and false positive rates across various thresholds. The results reveal that the CNN model achieved the highest AUC, indicating superior performance, followed by CNN-GRU and then Logistic Regression. The dotted diagonal line represents the performance of a random classifier.  
Table 6 presents the confusion matrices for the three models, offering a detailed view of how accurately each model classified positive and negative samples. These matrices help visualize the true and predicted classifications. Table 7 provides a summary of the key performance metrics--accuracy, precision, recall, F1-score, ROC-AUC, and FAR for the three models.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **True Negative** & **False Positive** & **False Negative** & **True Positive** \\ \hline Logistic Regression & 2800 & 907 & 283 & 3310 \\ \hline CNN & 3623 & 84 & 42 & 3551 \\ \hline CNN-GRU & 3489 & 218 & 98 & 3495 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion Matrices for Logistic Regression, CNN, and CNN-GRU models.  
Figure 8: ROC Curves for Logistic Regression, CNN, and CNN-GRU models.  
Figure 7: Training and Validation Accuracy and Loss Curves for CNN and CNN-GRU models.  
The results indicate that the CNN model consistently delivered the highest performance across most metrics, while Logistic Regression had the highest FAR. CNN outperformed the other models in terms of accuracy, precision, recall, and F1-score, and also maintained a low False Alarm Rate. The CNN-GRU model performed well but trailed slightly behind CNN, especially in precision and recall. Logistic Regression, although competitive in some areas, showed a higher FAR and generally lower performance than the deep learning models.
# The Value of Simple Heuristics for Virtualized Network Function Placement
## 3 Placement Heuristics
### Random Placement  
The first heuristic is the simplest algorithm that can be used for embedding the NFs of an SG into a physical network. The random placement heuristic can be divided into two parts: placing the NFs and connecting the NFs. To place a NF, the algorithm randomly chooses a node that has sufficient nodal resources. In this stage, if there is no node with sufficient nodal resource for any of the NFs the request will be rejected. If all NFs are placed successfully, the algorithm moves to the connecting stage. The algorithm starts from the source and connects it to the node used for placement of the first NF of the SG, finding a shortest hop path via Dijkstra algorithm. This process of connecting nodes continues until the node that contains the last NF of the SG connects to the destination. As the path from source to destination that passes all the NFs based on their order is identified, BW availability will be checked. BW consumption is based on the summation of the BW consumed by passing each link and due to interference.  
To describe this heuristic and the next ones better, consider the following example of an SG placement that will be solved by all of the heuristics. Assume we have a wireless multi-hop network with six nodes. Nodal resources of the nodes in the wireless network are \(C_{n}=\{12,11,10,20,14,8\}\) units, and the available BW of the links in the physical network are all 20 units. The nodal resource demand of a SG of 3 NFs is \(c_{f}=\{2,1,4\}\) and the BW demand of the request is 2 units. The SG's source is node 1 and its destination is node 5. Figure 1 shows the topology of the physical network and a random placement of the SG. As can be seen, although all three NFs could have been placed in the source node and the shortest path between source and destination is only two hops, the length of the chosen path is six hops, consuming significantly more BW.
# Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
## 2 Theory
### Drilling Parameters under Compound Drilling  
There are two sources of power for the bit: one is the surface drive, usually the rotary table (RT) or the top drive system (TDS), and the other is the positive displacement motor (PDM). Under compound drilling, the bit is driven by both the surface drive and the PDM (RT/TDS + PDM). Therefore, the torque of the bit consists of the torque of the RT and the PDM, and the rotary speed of the bit is calculated in the same way [34]. The PDM is a volumetric power drilling tool driven by high-pressure drilling fluid. It is characterized by an output shaft torque proportional to the pressure drop of the drilling fluid in it and a rotary speed proportional to the flow rate of the drilling fluid. The total rotary speed and total torque of the bit are calculated by Equation (26) and Equation (27), respectively.  
\[N_{\rm t}=N+\frac{Q}{q} \tag{26}\]  
\[T_{\rm t}=159.24q\Delta P+T_{0}-\sum_{i=1}^{n}\mu_{t}r_{i}|N_{i}| \tag{27}\]  
where \(N_{\rm t}\) is the total rotary speed under compound drilling (rpm); \(T_{\rm t}\) is the total torque under compound drilling (N-m); and \(T_{0}\) is the wellhead torque (N-m).  
The pressure drop of the PDM can be calculated by Equations (28)-(32) [43].  
\[\Delta P=\left(\frac{Q}{Q_{0}}\right)^{1.8}P_{0}+k\Big{(}aw+bw^{2}\Big{)}^{f} \tag{28}\]  
\[a=\frac{T_{1}-T_{\rm max}\Big{(}\frac{W_{\rm t}}{W_{\rm max}}\Big{)}^{2}}{W_ {1}-\frac{W_{1}^{2}}{W_{\rm max}}} \tag{29}\]  
\[b=\frac{T_{1}-aw_{1}}{W_{1}^{2}} \tag{30}\]  
\[f=\frac{\ln\frac{P_{1}-P_{0}}{P_{\rm max}-P_{0}}}{\ln\frac{T_{1}}{T_{\rm max}}} \tag{31}\]  
\[k=\frac{P_{1}-P_{0}}{T_{1}^{f}} \tag{32}\]  
where \(Q_{0}\) is the maximum permissible flow rate of PDM (L/min); \(P_{0}\) is the pressure drop at idle at a maximum flow rate of PDM (MPa); \(w\) is the weight on bit (kN); \(T_{1}\) is the output torque of PDM (kN-m); \(T_{\rm max}\) is the maximum torque of PDM (kN-m); \(W_{1}\) is the working weight on bit (kN); \(W_{\rm max}\) is the maximum permissible weight on bit for PDM (kN); \(P_{1}\) is the recommended working pressure drop of the PDM (MPa); \(P_{\rm max}\) is the maximum allowable pressure drop of the PDM (MPa).
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 3 Design and Implementation of Elliptic-Curve Cryptosystem F256 on FPGA
### Elliptic-Curve Point Addition  
Point addition uses three modular multiplier units to calculate point Q + P on the elliptic curve in parallel. Sixteen modular multiplications are done in seven stages as shown in Figure 2, so the latency of point addition will reduce to 7(n + 3) + 5 clock cycles.
## 3 Experimental Results
### Displacement Results under Thermal Load  
#### 3.4.1.  
Figure 17 shows the upper arch displacement of the beam along the longitudinal direction when the track slab top surface is heated up to 20 \({}^{\circ}\)C. It can be seen that the camber of the beam is symmetrical about the mid-span section of the beam (a half sine wave shape) under the temperature rising condition. With the increase of the thermal loading cycles, the displacement plot of the beam gradually shifts downward.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Thermal Load** & **Structural Parts** & **Fixed End** & **Mid-Span** & **Sliding End** \\ \hline \multirow{3}{*}{10 \({}^{\circ}\)C} & Track slab & 109\% & 32.4\% & 3.2\% \\  & CA mortar & \(-\)48.3\% & \(-\)89.5\% & 102.8\% \\  & Track bed & \(-\)52.9\% & \(-\)35.1\% & \(-\)18.1\% \\ \hline \multirow{3}{*}{20 \({}^{\circ}\)C} & Track slab & 120\% & 33.3\% & \(-\)0.6\% \\  & CA mortar & \(-\)52.0\% & \(-\)23.9\% & 125.7\% \\  & Track bed & \(-\)51.3\% & 32.5\% & \(-\)12.7\% \\ \hline \multirow{3}{*}{30 \({}^{\circ}\)C} & Track slab & 86\% & 35.5\% & 2.9\% \\  & CA mortar & \(-\)48.3\% & \(-\)22.1\% & 101.4\% \\ \cline{1-1}  & Track bed & \(-\)49.8\% & \(-\)30.9\% & \(-\)4.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strain variation of the track structure between the states before and after repeated thermal test.  
Figure 16: Variation of the track bed strain with the thermal loading cycles.  
Figure 18a shows the influence of the thermal loading cycle on the variation of the track slab top surface temperature with the concrete box beam mid-span camber. Under the repeated action of the thermal loading, the temperature-induced camber of the concrete box beam gradually decreases. In the initiation stage (1 st-6 th cycle) and the stabilization stage (13 th-18 th cycle), the plots are densely distributed, while in the expansion stage (7 th-12 th cycle), a sparse distribution of the plots is observed.  
Figure 18b shows the variation of the concrete box beam mid-span camber according to the thermal loading cycles. Under the same thermal load, the concrete box beam mid-span camber gradually decreases with the increase of the number of thermal cycles. In the initiation and stabilization stages of the gap, the displacement decreases at a slow rate.  
Because the track slab is the closest layer to the heat source, during the application of the thermal load to the track structure, the internal thermal load within the track slab is the largest. Before the occurrence of the gap between the layers, the thermal load effect is transferred downward layer by layer through the interlayer bond to achieve harmonized deformation of the track system structural layers. The bond strength between the track slab and the CA mortar interface gradually decreases or even disappears due to the initiation of the gap between the layers. Therefore, the load effect transferred to the lower layer decreases gradually, and the temperature induced camber decreases accordingly. The reduction rate of the displacement is substantially consistent with the expansion rate of the gap length.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 2 Literature Review
### FFR Network Layout  
Standard FFR architecture is split into the outer region, also referred to as partial re-use zone, and the inner region, popularly referred to as full re-use zone [10]. The received SINR values from the subscribers dictates how the threshold is set and which subscribers fall in the respective regions. In the event the received SINR value is higher than the threshold denoted as \(\Gamma_{th}\), the subscriber is then categorized in the inner region. If not, it is regarded to be in the outer region or the partial-reuse zone. The inner and outer regions are allocated disjoint frequencies. \(B_{T}\), which is the total system bandwidth and contains orthogonal subcarriers, \(R_{T}\) and \(B_{T}\), is divided into \(B_{I}\), which is a cluster of subcarriers in the inner region. Sub-carrier cluster, \(B_{O}=B_{T}\setminus B_{I}\), is allocated to outer region subscribers. \(B_{O}\) cluster is again split into three parts, \(B_{O1}\), \(B_{O2}\), and \(B_{O3}\)[10]. The three parts allocated to the MSs in the outer cell ensure every adjacent cell is operating at different sub-band frequencies.  
Hence,  
\[R_{T}=3R_{O}+R_{I} \tag{1}\]  
Inner region and outer regions are respectively assigned subcarriers, \(R_{I}\) and \(R_{O}\).  
\(\beta_{FR}\) is the ratio of subcarrier numbers that fall in the inner region. The outer region gets allocated, \(1-\beta_{FR}\), of all the available system bandwidth.
# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 4 Data Aggregation and Communication
### Data Embedding
#### 4.1.1 Eps  
The parse tree, \(EST(X)\), that is formed for data \(X:X\) is the breakdown structure of partial data analogous to the nodes of \(EST(X)\). The aim is to limit the data-editing operations. A clear EST contains all dynamic data of length \(2^{loc}\), namely \(X\big{[}loc_{2}2^{loc}\ldots\big{(}(loc_{2}+1)2^{loc}-1\big{)}\big{]}\in loc\) and \(loc_{2}\); it results in a complete binary tree. However, if \(X\) is updated using addition or removal to obtain \(X^{\prime}\), \(X\) and \(X^{\prime}\) will be construed using the same technique to two dissimilar hierarchical partial datasets; thus, the resultant embedding will not preserve approximation.  
Suppose we have data \(X\); the next step is to form an ESP tree in a hierarchal fashion with \(P_{i}(X)\) repetitions. Every repetition produces a different level of EST. At every repetition, \(i\), the process initiates with data, \(Xi\), and divides them into chunks of size two or three. We substitute every chunk analogous to \(X[loc_{3}\ldots loc_{2}]\) by name and refer to the pair \((loc,h(X[loc_{3}\ldots loc_{2}]))\). Moreover, \(h\) corresponds to a 1-1 hash function on partial data \(X\).  
Suppose \(X0=X\), and the repetitions until the length of data left become 1. The EST tree of \(X\) contains levels, and for every string of \(Xloc-1\), a node at level \(i\) must exist, and the children are the nodes in level \(loc-1\). Here, a leaf node is each data unit of \(X0=X\). More precisely, we can make the partitions of \(Si\) data into dissimilar non-overlapping units. The data can be divided into non-overlapping units in three ways:  
1. Maximum adjacent partial data of \(Xi\) that comprise a repetitive sign (\(X0\) shows in the form \(d^{l}\) for \(a\in\Sigma_{i}\) where \(l>1\)).
2. Length of partial data (Long) at least \(\mathit{log}^{*}\lvert\sum loc-1\rvert\) not of type 1 above.
3. Length of partial data (short) less than \(\mathit{log}^{*}\lvert\sum loc-1\rvert\) not of type 1.  
The general term \"meta block\" (mb) is used for all such partial data. To produce next-level parsing, we process each mb as demonstrated in the following subsections.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 4 Configurations
### WGA-Based OPAs
The 11-channel OPA enabled an FOV of 68.8\({}^{\circ}\)\(\times\) 77\({}^{\circ}\) and a beamwidth of 6.5\({}^{\circ}\)[113]. Typically, a 1D WGA-based OPA can be used to realize 2D dynamic beam forming and steering by phase modulation and grating dispersion in the azimuthal and polar directions, respectively [7, 8, 64]. This type of OPAs was widely demonstrated for its simple device configuration. \(N\) phase shifters and one tunable laser are needed for an \(N\)-channel OPA [12]. 1D long-WGA-based OPAs with narrow beamwidths enable excellent 2D dynamic beam forming and steering [11, 12, 65, 66]. However, 1D WGA-based OPAs are limited by wavelength-dependent performance and the price of a precise laser. In this sense, if more steering flexibility is required, one has to resort to 2D OPAs. Figure 8: WGA-based OPAs. (**a**) The first OPA introduced in Si photonics. Reprinted with permission from [7] © The Optical Society. (**b**) A 4 \(\times\) 4 WGA-based OPA. Reprinted with permission from [8] © The Optical Society. (**c**) SEM of a fabricated OPA after dry etching away the dielectric SiO\({}_{2}\). Reprinted with permission from [87] © The Optical Society. (**d**) An 8 \(\times\) 8 OPA receiver. Reprinted with permission from [43] © The Optical Society. (**e**) False-colored tilted SEM of an aperiodic Si/Si\({}_{3}\)N\({}_{4}\) grating. Reprinted with permission from [109] © The Optical Society. (**f**) A high contrast WGA-based OPA with high efficiency and low crosstalk. Reprinted with permission from [110] © The Optical Society. A 2D OPA based on WGAs generates a 2D pattern by phase modulations of WGAs [10; 58]. A 2D WGA-based OPA enables highly desirable flexible optical manipulation and dynamic control at a fixed wavelength. The scalability of the OPAs, which is limited by control complexities and power consumption, is the main issue to be solved. Main evaluation parameters of some WGA-based OPAs are given in Table 2.
## 2 Materials and Methods
### Instruments  
The data utilized in this study were obtained from the Hawk-Eye system [35] deployed during Davis Cup events. It consists of a group of ten cameras which are placed around the circumference of the court and capture and record the trajectory of the ball and the players' movement on the court [14]. The Hawk-Eye System is well-known for its high accuracy and reliability in tracking and analyzing tennis matches. It is widely used in professional tennis and has provided data for numerous research papers [6; 11; 14; 15].