esa-sceva/satcom-chunk-collection huggingface dataset:
# Variation in Mechanical Properties of Ti-13 Nb-13 Zr Depending on Annealing Temperature
## 2 Materials and Methods  
Extuded Ti-13 Nb-13 Zr rods with a diameter of 28 mm were used for this study. The chemical composition of main alloying elements was measured to be 13.0 wt.%Nb, 12.1 wt.%Zr, 0.086 wt.%O, 0.009 wt.%N, 0.0012 wt.%H, and balance Ti. These rods were soaked in a furnace at 1073 K for 1 h, followed by quenching in a water bath based on the ST process in the ASTM standard. Afterwards, they were soaked in a furnace again set to a given annealing temperature for 1 h, followed by the second water quenching. The employed annealing temperatures ranged from 573 to 973 K with an interval of 100 K.  
Mechanical properties of the investigated alloys were evaluated using a quasi-static uniaxial tensile test, Vickers hardness test, and ultrasonic measurement. The tensile tests were carried out at a strain rate of \(5\times 10^{-3}\) s\({}^{-1}\) with a 25-mm extensometer. Tensile specimens were machined with a gage meter of 25 mm and a gage diameter of 6 mm. The tests were repeated three times per condition, yielding an average and standard deviation. The Vickers hardness test was conducted 10 times under a force of 500 gf with dwell time of 10 s. The ultrasonic measurement was employed to measure Young's modulus based on the velocities of ultrasonic waves along longitudinal and transverse directions, separately [14]. The specimens for the last two experiments were mechanically polished using #400, #800, #1200, and #2400 emery paper before commencing a test.  
Microstructures of the alloys were investigated using X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD) analysis. All samples were water-abraded using the emery paper from #400 to #2400. XRD analysis was performed immediately after this procedure to minimize the effect of surface corrosion. SEM samples were additionally subjected to a chemical etching in an aqueous solution of 3% fluoric acid and 5% nitric acid. EBSD samples were prepared by an electropolishing at 22 V for 20 s in a solution of 6% perchloric acid, 35% 2-butoxy ethanol, and 59% methanol. EBSD data with a high confidence index (\(\geq\)0.1) were processed using TSL OIM Software Ver. 7.
## 3 Analysis of the Inter-Satellite Measurement Situation
### Constellation Configuration Analysis for Autonomous Orbit Determination  
When the number of established links between satellites to be assessed is greater than or equal to three, the corresponding position dilution of precision (PDOP) value can be calculated to reflect the quality of the geometric configuration of inter-satellite link establishment. Figure 2 presents the changing situation for the PDOP values of the PRN36 satellite during a consecutive 30-day period, and Table 2 shows the PDOP statistics of all 24 MEO satellites in BDS-3. The average minimum PDOP of all satellites is approximately 0.74, the average maximum PDOP is approximately 4.41, and the mean PDOP value is approximately 0.99. It can be concluded that the overall structure of the geometric figure for inter-satellite link establishment is improved [26].  
\begin{table}
\begin{tabular}{c c c c} \hline
**PRN** & **Maximum Number of Established Links** & **Minimum Number of Established Links** & **Mean** \\ \hline  
Mean & 19.96 & 0 & 14.45 \\ \hline \end{tabular}
\end{table}
Table 1: The statistical situation in the number of established links for each BDS-3 satellite.  
Figure 2: The changing situation of PDOP values for PRN36 satellites as the positioning objects of BDS-3.
# Impact of Yeast Selection on Volatile Phenol Levels of Wines Produced from Smoked-Exposed Juice
## 3 Results and Discussion
### Volatile Phenol Analyses
#### 3.2.1 Laboratory-Scale Yeast Trials
While NT 112, NT 202, RX60, FX10, D80, WE 372, VIN 13 and BDX displayed a negative correlation with most the volatile phenols. However, VIN 13, NT 112 and NT 202 showed a positive association with \(m\)-cresol. The lowest guaiacol concentrations were observed in wines fermented with NT 112 (6.46 ug/L), RX60 (6.55 ug/L) and WE 372 (6.55 ug/L), and contained significantly lower levels than wines produced with spontaneous fermentation and D254 (Table S2). Yeast strain D254 produced high guaiacol levels compared to the other yeasts used in this study, which is in agreement with the findings of [PERSON] et al. [11]. BDX did not produce high volatile phenol levels, which is also in agreement with the findings of [PERSON] et al. [11]. Figure 1: Principal component biplot of volatile phenols in laboratory-scale Chenin Blanc wines produced from juice exposed to smoke and fermented with different commercial wine yeasts. Mean values of three replicate fermentations and yeast details are included in the materials and methods. Abbreviation: Spon AF = Spontaneous alcoholic fermentation. Based on the two datasets it would appear that smoked juice can be used to screen yeasts for suitability for use in wine production from smoke affected grapes. The laboratory-scale trials indicate that the yeasts VIN 13 and X16 may be more suitable for wine production from smoke-affected Chenin Blanc grapes, than other yeast strains, while RX60 and NT 112 should be considered for wine production of smoke-affected Merlot grapes. However, further research is needed to support preliminary findings before definitive recommendations can be made.
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 3 Methodology
### Multi-Destination \(k\) Simple Shortest Paths
#### 3.1.2 Multi-Destination KSSP  
Let \(G(N,A)\) be a directed network. The objective of multi-destination KSSP is to find the sets of simple shortest paths from the origin \(s\in N\), \(\Phi_{si}\ \forall\ i\in N\setminus\{s\}\), if they exist, such that \(C(\gamma_{s,i})\leq C\big{(}\gamma_{s,i}^{\prime}\big{)}\), \(\forall\ \gamma_{s,i}\in\Phi_{si},\ \forall\ \gamma_{s,i}^{\prime}\
otin\Phi_{si},\ \forall\ i\in N\setminus\{s\}\). As explained in Section 3.1.1, it is to be noted that \(|\Phi_{si}|=k\) if \(k\) simple paths exist between \(s\) and \(i\). Otherwise, \(|\Phi_{si}|<k\).  
Henceforth, this problem will be referred to as the one-to-all KSSP (KSSP-OA) problem. In addition to this, the KSSP problem class also comprises two other important variants discussed below.  
_One-to-Many KSSP (KSSP-OM)_  
The objective is to find \(k\) simple shortest paths from one origin to every node from a set of destinations \(D\). Given a directed network \(G(N,A)\) and an origin \(s\in N\), find the sets of simple shortest paths \(\Phi_{si}\) from \(s\) to each node \(i\in D\subset N\setminus\{s\}\), if they exist, such that \(C(\gamma_{s,i})\leq C\Big{(}\gamma^{\prime}_{s,i}\Big{)},\ \forall\ \gamma_{s,i}\in\Phi_{si},\ \forall\ \gamma^{\prime}_{s,i}\
otin\Phi_{si},\ \forall\ i\in D \subset N\setminus\{s\}\). As explained in Section 3.1.1, it is to be noted that the cardinality of the set \(\Phi_{si}\) can be \(k\) or less.  
_One-to-All Cost Constrained Simple Shortest Paths (CCSSP-OA)_  
The objective here is to find all the simple shortest paths from a given origin to any possible destination that are within a cost budget \(\alpha\). Given a directed network \(G(N,A)\), an origin \(s\in N\), and a cost budget \(\alpha\), find the set of simple shortest paths \(\Phi_{S}\) from \(s\) to any node \(i\in N\setminus\{s\}\), if it exists, such that \(C(\gamma_{s,i})\leq\alpha\ \forall\ \gamma_{s,i}\in\Phi_{si}\) and \(C(\gamma^{\prime}_{s,i})>\alpha\ \forall\ \gamma^{\prime}_{s,i}\
otin\Phi_{s}\). This problem involves variable \(k\) as well as a variable number of destinations to be determined as the output.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 7 Location-Based Techniques for Enhancing PHY Layer Security
### Angle of Arrival--Empowered Methods  
The positioning of devices is recently at the center of interest of 3 GPP, expecting the exploitation of sounding reference signals as the known patterns for localization. The thorough study of various localization techniques such as Angle of Arrival (AOA) or Time Difference of Arrival (TDoA) was provided in [167]. The application of AOA for localization in ultra-dense networks was studied in [168]. Recent work [169] has exploited the AOA as an enabler for physical layer authentication by providing resistance to impersonation (spoofing) attacks.
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 2 System Model
### Outage Probability  
To guarantee the seamless relay transmission to \(U_{o}\), the outage probability of \(U_{o}\), where \(R_{U_{o}}\) is less than the minimum required data rate \(\eta\), can be defined and expanded as follows:  
\[P_{out}(R_{U_{o}}|\eta) = prob(R_{U_{o}}\leq\eta) \tag{8a}\] \[= 1-prob\Bigl{(}R_{U_{o}}^{(1)}\geq\eta,R_{U_{o}}^{(2)}\geq\eta \Bigr{)}\] (8b) \[\stackrel{{(a)}}{{=}} 1-prob\Bigl{(}R_{U_{o}}^{(1)}\geq\eta\Bigr{)}prob\Bigl{(}R_{U_{o}}^{(2)} \geq\eta\Bigr{)}, \tag{8c}\]  
where \(R_{U_{o}}^{(1)}=\frac{(1+\lambda)}{2}R_{xBS,UAV}^{1 st}\), \(R_{U_{o}}^{(2)}=\frac{(1-\lambda)}{2}R_{UAV,U_{o}}^{2 nd}\) from (7), and \((a)\) in (8c) follows the independence of two events.
## 3 Results
### Optical Conductivity  
Since the change in oxygen vacancies can be sensitively examined by optical spectroscopy [49, 50], the optical conductivities of the undecorated LSCO and LNO-decorated LSCO films were evaluated by spectroscopic ellipsometry. As shown in Figure 4, a single peak was observed at 1.5 eV for the undecorated LSCO film. The peak position shifted toward lower photon energy with decorating LNO on LSCO. Previous studies also demonstrated a similar trend in the peak shift toward lower photon energy with increasing vacancy contents in La\({}_{\text{2-x}}\)Sr\({}_{\text{x}}\)CuO\({}_{\text{4}}\)[51, 52]. Therefore, our optical conductivity results verify the aforementioned hypothesis of the increased oxygen vacancies by forming the heterointerface between LSCO and LNO. The increase in the concentration of oxygen vacancies by decorating LNO on LSCO can also be supported by the theoretical formation energies (E\({}_{\text{vac}}\)) for oxygen vacancies. The formation energy (E\({}_{\text{vac}}\)) for oxygen vacancies in LNO (1.23 eV) is known to be much lower than that in LSCO (2 eV) [24, 31]. Accordingly, the LNO-decorated LSCO films have a higher concentration of oxygen vacancies on the surface  
Figure 3: (**a**) Temperature and (**b**) \(p\)(O\({}_{\text{2}}\))-dependent \(k_{\text{chem}}\) values for undecorated LSCO and LNO-decorated LSCO films.  
compared with the undecorated LSCO film. Given the experimental and theoretical results, the increased oxygen vacancies obtained by constructing the heterointerface between LSCO and LNO facilitate oxygen dissociation and incorporation at the LSCO surface, resulting in the enhancement in ORR activity for the LSCO film.
## 6 System Application Verification
#### 6.1.3 Signal Correlation  
(1) IO Server signal creation and association: The signal variables required for synchronization with PQ Factory and the PLC are established within the IO Server management interface. Their fundamental attributes and acquisition parameters are meticulously defined to ensure that the signals accurately represent the state of the PLC. When adding signals, it is essential to ensure that the signal settings align with the IP address and port number used by PQ Factory to connect to the PLC, thereby facilitating precise communication and data exchange among the three components.  
(2) PQ Factory signal creation and association: The address matching process within PQ Factory software ensures consistency between the digital twin device and the controller variables. In PQ Factory, the signal interface of the digital twin device must accurately align with the PLC control variables, facilitating seamless functional integration between the digital model and the PLC for efficient collaboration. This matching mechanism synchronizes the PLC logic with the digital twin model in PQ Factory, enhances system controllability, and provides technical support for real-time monitoring in intelligent manufacturing.  
Figure 12: Schematic diagram of making an industrial robot running track.
## 4 Performance Evaluation  
We ran a set of experimental tests to validate the operations of the proposed approach and its performance. The RNM-SC approach is based on the Zigbee wireless technology that was designed for the IEEE 802.15.4 standard. It allows battery-powered sensors for information sensing and forwarding by ZigBee devices towards cloud systems in a multi-hop structure. This section presents the simulated execution of the RNM-SC approach using discrete-event simulator NS-3 [46, 47]. These experiments evaluated the security and routing resiliency of the RNM-SC approach against other solutions under dynamic conditions for observing medical healthcare. The results were compared with LEACH++ [34] and DSM [30]. The RNM-SC performance was tested using energy consumption, network throughput, packet drop ratio, data threats, and end-to-end delay under the number of jamming nodes. The role of jamming nodes was to obstruct the wireless channels, floods the route request packets and drop the sensors' data. The simulation parameters are defined in Table 1.
## 2 Related Work
\begin{table}
\begin{tabular}{l l l l l} \hline
**Protocol** & **Key Features** & **Strengths** & **Limitations** & **Reference** \\ \hline \multirow{2}{*}{UW-ALOHA-Q} & \multirow{2}{*}{RL-based slot selection} & Asynchronous & High energy & \multirow{2}{*}{[27]} \\  & & operation, adaptive & consumption, limited & \\  & & & scalability & \\ \hline \multirow{2}{*}{ED-MAC} & \multirow{2}{*}{Depth-based scheduling} & Collision avoidance, energy efficient & Static network & \multirow{2}{*}{[30]} \\  & & & assumption, complex & \\  & & & synchronization & \\ \hline \multirow{2}{*}{OCMAC} & \multirow{2}{*}{Duty-cycled RTS} & Low collision rate, reliable & High control overhead, limited mobility support & [29] \\ \hline \multirow{2}{*}{TDTSPC-MAC} & \multirow{2}{*}{Hybrid TDMA/CSMA} & Energy efficient, power control & Complex & \multirow{2}{*}{[13]} \\  & & & implementation, high & \\ \cline{1-1}  & & & synchronization & \\ \hline \multirow{2}{*}{RL-MAC} & \multirow{2}{*}{Q-learning adaptation} & Dynamic contention & High computational & \multirow{2}{*}{[33]} \\  & & adjustment & overhead, training & \\ \hline \multirow{2}{*}{RAP-MAC} & \multirow{2}{*}{Rate adaptation} & \multirow{2}{*}{Robust, field-tested} & Limited energy & \multirow{2}{*}{[34]} \\  & & & optimization, complex & \\ \cline{1-1}  & & & configuration & \\ \hline \end{tabular} Note: comparison highlights features most relevant to our proposed OAPC-MAC framework.  
\end{table} 
Table 2: Comparative analysis of UWSN MAC protocols.
## 3 Main Results
### Zeno Behavior Analysis  
In the preceding content, we presented evidence of convergence in the observation and control within the system design. Subsequently, we will conduct an analysis on Zeno behavior. The primary objective of event-driven sampling is to ensure a specific time interval between consecutive samples, thereby preventing the occurrence of Zeno behavior. Subsequently, we will delve into an analysis of this crucial aspect. Consequently, the ensuing theorem is presented. The proof of this theorem is presented in the appendix.  
**Theorem 3**.: _Let us consider the event-based estimation (14) for system (1); there will exist a minimum interval between every two samples with driven conditions (4)-(6)._  
**Remark 7**.: _Theorem 3 establishes the existence of a minimum sampling interval within our proposed driving condition, ensuring that sampling events do not occur continuously. This absence of Zeno behavior is a fundamental prerequisite for event-triggered design, and the event-based design is only meaningful under this premise. The introduction of this requirement did not impose any additional parameter constraints, as they were already ensured by the previous design._
# TBRNet: Two-Stream BiLSTM Residual Network for Video Action Recognition
## 3 Proposed Approach
### Res-C3D Network  
We built each stream on the basis of 3D CNN with residual blocks instead of 2D CNN. We utilized C3D [4] as the base model which was equipped with 3D convolution kernels to directly extract the hidden patterns from a stack of RGB or optical flow frames ordered by time sequences. Therefore, it was an effective feature learning network for modeling the spatial features together with the local short fixed-range temporal relationships.  
The original C3D model had five convolution blocks with eight convolution layers, five pooling layers, two fully connected layers, and a softmax loss layer. All the convolution layers had \(3\times 3\times 3\) convolution kernels with stride \(1\times 1\times 1\). The channel size for each convolution block was set to 64, 128, 256, 512, and 512 from the first block to the fifth block. The second to the fifth pooling layers all had \(2\times 2\times 2\) pooling kernels with stride 2 in both the spatial and temporal dimensions in order to downsample the feature maps by a factor of eight The first pooling layer had a kernel size of \(1\times 2\times 2\), which was designed for preserving the temporal signals in the early steps. The two fully connected layers had a 4096 dimensional output size, followed by a softmax classifier to obtain the prediction results.  
We generalized the idea of identical mapping with residual connections for the original C3D network and constructed an identical mapping \"shortcut\" by introducing residual connections between convolution blocks, which are depicted as the green curved arrows in Figure 6. These residual blocks are capable of preserving network performance by maintaining the learned features via identical mapping and integrating newly learned patterns to optimize the spatiotemporal representations.  
Finally, considering that the fully connected layer was capable of perceiving the entire model so as to obtain high-level semantic information, we merged the outputs of the fully connected layers from two streams together to be the short-term spatiotemporal features of encoder. Since the deeper fully connected layers contained stronger signals, fc6 of appearance stream and fc7 of motion stream were combined together, and this type of combination strategy achieves better result compared to others. It can be explained by the fact that it is better to contain appearance signals with less strength and make the motion signals predominate during the training process.
## 3 Ermost
### Server-Trace Analysis  
The _trace analysis_ application runs on the server (see Figure 1) and has the main function to extract information about the target network from the trace generated by local monitors. For this purpose, initially, the redundant packets that exist in the trace (database) are removed.  
In the MicaZ platform, redundant packets can be detected by analyzing the Destination Sequence Number (DSN) present in the header of the packets sent by sensor nodes. The DSN has a size of 8 bits and it is incremented by the source node for each packet sent. When the DSN reaches the value of 255, the DSN of the next packet sent by the sensor node will have a value of 0 [28]. Therefore, if two or more packets have the same source address, the same DSN, and the difference between their timestamps is less than \(\Delta\)t, it means that they are the same packet. In this work, we assume \(\Delta\)t is  
Figure 5: Format of the packet sent by sniffers using aggregation of headers.  
equal to 10 s, because in our implementation a given sensor node does not send more than 255 packets in this time interval.  
Figure 6 shows a portion of the trace with the packets sent by the target node whose address (_source_addr_) is 1. As shown in Figure 6, there are two packets with DSN equal to 119 and they have the same timestamp (_exp_datetime_). In this case, one of these packets is removed from the trace.  
After removing the redundant packets, the trace is analyzed to extract information about nodes and paths of the target network. Figure 7 illustrates an example of the _trace analysis_ application. Note that it exemplifies information about the nodes of a given target network used to validate this application. Table 2 (Section 3.4.1) shows the description of each variable displayed in Figure 7.  
In this case, as shown in Figure 7, only the node with addresses (_nodeId_) of **0** receives data packets, because it is the sink node. Nodes with addresses from **1** to **21** send data packets with information collected from sensors to the sink node. The address (_nodeId_) **65535** corresponds to the broadcast address of the network, where all packets of the routing protocol used by the target network are sent. The scenario used to obtain this information is described in Section 4.1.  
Figure 6: Example of trace with redundant packets.  
Figure 7: Example of information about the nodes of the target network.  
The screen of the _trace analysis_ application shown in Figure 8 exemplifies information about some paths of the target network. Table 4 (Section 3.4.1) shows the description of each variable displayed in Figure 8. Note that each sensor node is the source of two paths, with one for the broadcast address (address 65535) and the other for the sink node (address 0). The _trace analysis_ application was implemented using the Java programming language.
## 4 PCA-SRP Implementation in ANN  
The proposed framework addresses the limitations of the ANN in processing multidimensional datasets through the use of PCA with the sample reduction process (PCA-SRP). PCA is used to analyse the multidimensional data, and if there exists a significant relationship between the features of a dataset, it is arranged from the most significant samples to the least to be visualised and the multidimensional data are put into perspective. It focuses on implementing dimension reduction by removing data from the multidimensional dataset feature columns that do not have high inter-feature covariance; however, the proposed framework using the loading scores can dissect all of the samples or rows in the multidimensional dataset. Figure 6 provides a graphical description of the proposed PCA-SRP-based ANN solution.  
The PCA-SRP Python program generates a new set of multidimensional data with fewer samples based on the screen plot to identify the most significant samples. It is used in the Python ANN program as an input in order to ensure correct classification.  
Figures 7 and 8 show the algorithm used in Python in the implementation and analysis of the concept. The implementation starts by extracting the data in.csv format and by injecting it into the PCA-SRP process. Major principal components are extracted through the threshold mentioned above based on the input dataset, resulting in a new set of multidimensional datasets used in ANN.  
The proposed PCA-SRP utilised in the ANN consists of two hidden layers consisting of 32 neurons and 16 neurons. The learning rate was set at 0.1 and trained for 100 epochs. The input parameters in the PCA-SRP-based ANN are shown in Table 3 [42].  
Figure 8: Detailed breakdown of the steps in ANN.  
Figure 6: Conceptual flowchart of the proposed approach.  
Figure 7: Detailed breakdown of the steps in PCA.  
The accuracy of the PCA-SRP-based ANN was compared against a model trained using ANN alone on different datasets.