esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Policy Network Model
The output range of each control parameter is defined as follows:  
\[c_{st} \in[-1.0,1.0], \tag{1}\] \[c_{th} \in[0.0,1.0],\] (2) \[c_{br} \in[-1.0,0.0], \tag{3}\]  
where \(c_{st}\) is defined as a normalized value ranging from \(-1.0\) to \(1.0\), corresponding to a steering angle between \(-70^{\circ}\) and \(+70^{\circ}\). When considering a conventional vehicle operating below 9000 Revolutions Per Minute (RPM), the throttle value \(c_{th}\) is mapped to a propulsion torque of \(c_{th}\times 743.0\) Nm. Similarly, the brake value \(c_{br}\) is mapped to a braking torque of \(c_{br}\times 700\) Nm. It is important to note that when the brake value is activated, the throttle is automatically set to zero, and vice versa, ensuring mutual exclusivity between the throttle and braking operations. This information is integrated into the vehicle state information \(\mathcal{S}\) and provided to the DRL module. The DRL module then utilizes the vehicle state information \(\mathcal{S}\) to compute an optimal control action, which is passed through an action smoothing process to mitigate abrupt behavior changes before being applied to the environment. Subsequently, the next state and corresponding reward are received as feedback. This interaction constitutes a single step, and the system operates at a frequency of 15 steps per second (SPS), where each step corresponds to approximately 66.7 ms. At each step, a driving decision is made and an associated reward is assigned accordingly. Figure 3 illustrates the temporal structure of the experiment. Each experiment is conducted in episode-based units, where a predefined driving route is assigned per episode, and each episode comprises a sequence of multiple steps. If the ego vehicle violates a traffic regulation, a penalty, represented as a negative reward, is imposed, and the episode is immediately terminated. The details of the reward function and its components are discussed further in Section 3.4.
## 3 Results and Discussion  
### Synthesis of the PCA-GLU  
The polymer was tested using FT-IR (Fourier Transform Infrared). As shown in Figure 2, the peaks at 1050 cm\({}^{-1}\) and 1634 cm\({}^{-1}\) in Figure 2a represent the bending vibration of -N-H and the stretching vibration of -C=O, and the peak at 1108 cm\({}^{-1}\) in Figure 2b is the stretching vibration peak of -C-O in the hydroxyl group, while 1754 cm\({}^{-1}\), 1724 cm\({}^{-1}\), and 1686 cm\({}^{-1}\) correspond to the stretching vibration peak of -C=O in the carboxyl group. PCA-GLU was synthesized by the polymerization of glutamic acid and citric acid using the method described in reference [16] and characterized by infrared spectroscopy to obtain Figure 2c. The absorption band at 3364 cm\({}^{-1}\) is associated with the -C-N in the secondary amine group and the stretching vibration peak of -N-H and -C=O on the amide is at 1581 cm\({}^{-1}\) and 1694 cm\({}^{-1}\), while the peak at 1388 cm\({}^{-1}\) represents the stretching vibration absorption peak of C-O-C.  
In summary, the PCA-GLU has been synthesized successfully.
## 5 Performance Evaluation
### Discussion
#### 5.3.1 Scalability  
In this paper, we focus on the scenario where the number of available cores is limited compared with the number of FSs required by newly established lightpaths. The proposed method is expected to be effective with the current procedure even as the number of cores increases. On the other hand, more appropriate grouping and core-priority assignment strategies could lead to further performance improvement. In addition, since more diverse traffic patterns could affect the FS range related to the weight parameter \(W_{f}(R_{p})\), it is necessary to implement a new mechanism, e.g., periodically collecting traffic patterns and adjusting the FS range accordingly. We leave the implementation of these mechanisms for future work.
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 6 Applications for Crowd-IoT
### Specific Applications
#### 6.2.2 Transportation & Smart Cities
The author suggested that the in-built computational resources (together with smartphones and other IoT devices) contained inside vehicles trapped in city traffic can be harnessed to aid transportation authorities with dispersing congestion via large-scale signal re-timing. VACCS will support transportation authorities to improve vehicular movement at the level of smart city by dynamically re-timing signals, using vehicular network probe data, instead of only providing signals re-timing at the corridor level. VACCS will make traffic lights more sensitive to real situations instead of being reliant on historical volume counts. By designing  
Figure 34: Parking space framework using mobile sensors [102]. timing plans that adapt to actual traffic circumstances, general vehicular movement will be boosted, and carbon emissions level would be decreased. Financial implications associated with congestion, with regard to high amount of fuel usage as well as the number of missed productivity hours will be minimized. [PERSON] [105] proposed a collaborative IoT architecture named SenSquare that integrates disparate data sources and makes them accessible to end user through an open interface. Figure 35 illustrates the architecture of Sensquare Machine learning methods used to classify and homogenize heterogeneous data streams, which are often not well-formed. It then provides the users the option of creating their own services using a web interface or a mobile application. A variety of service templates are available to the end user, who decides to utilize them or begin afresh in developing his or her own template. As a result, such a service is tied to a specific domain for each user, who will eventually be alerted of developments about it. The data required to satisfy the service requirement are acquired from reputable sources if accessible and other untrusted sources. Any template service developed by one user for a particular area could be extended for use in another area using appropriate data. Finally, user-created services are made available to the whole community, allowing any member to make use of them or expand on them. [PERSON] [106] developed IoT-based contextualization approaches that take into account the whole spectrum of data generated in smart cities and utilize such data to give hyperpersonalized information to each user. Their proposed contextualization solution was exemplified in a smart parking spot recommender application/service. The experimental evaluation of their system demonstrates that contextualization of IoT data decreases query durations for IoT services (for example smart parking spot recommender) by more than three times when compared to a context-agnostic query.
## 4 Sensor Network Design
### WSN Architecture  
At each sensor nodes we have a soil moisture sensor and rainfall sensor attached to it. Our sensor network is based on Libeliium Waspmotes sensor nodes and ATmega1281 rds Microcontroller [34]. It is connected to following components:  
* Wireless Interface (Waspmote XBee based on IEEE 802.15.4 standard)
* Soil moisture sensor (Watermark 200 SS)
* Rainfall sensor (Libeliium Weather Station WS 3000)
* USB-PC Interface (Waspmote Gateway)
* Solar Panel (Waspmote rigid solar panel 7 V-500 mA)
* 6600 mA Li-Ion rechargeable batteries  
All the sensor nodes are connected to the base that is a computer using a USB-PC gateway. All the nodes act as sampling nodes and hence have both soil moisture sensor as well as rainfall sensor attached to them. The current network deployment has four sampling nodes, but more generally the architecture supports many nodes [34].  
Zigbee RF nodes are used as communication models to transmit the sampled data to the gateway, these can have the best average lifetime of 8 years using dual batteries 3 V, at 3000 mAh. Some studieslike [31], show that ZigBee model based on IEEE 802.15.4 standard with correct topology can last up to 10 years. Hence we are using ZigBee communication model for our research.
###### Abstract  
Human factors play a critical role in underground mine evacuations, significantly impacting performance during emergencies. Traditional mine evacuation strategies rely on predetermined routes and static signs, but these methods do not account for the dynamic and unpredictable nature of real emergencies or the diverse behaviors of individuals. This research addresses these limitations by using agent-based simulations to explore evacuation behavior and performance in underground mines. The study involved three key steps. First, agent-based simulations were deployed to model both individual and group behaviors during emergencies. Second, evacuation performance was compared across three scenarios: miners following traditional passive signage, those making decisions in chaotic conditions, and miners equipped with smart evacuation devices. Finally, evacuation times were quantified to assess the effectiveness of each approach. The results revealed that miners using smart devices improved evacuation efficiency by 35% compared with those relying on passive signage and by 37% compared with chaotic decision-making scenarios. The median evacuation time was reduced from 10.8 min with passive signage to 7 min when using smart devices, taking varying stamina levels into account. These findings underscore the importance of integrating intelligent systems that account for pre-evacuation and wayfinding behaviors, offering new insights and setting a higher standard for emergency protocols in underground mining.  
m 202420202411773  
mine evacuation; human factors; agent-based simulation; dynamic evacuation
# Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms
## 3 Solution Algorithms
### Variable Neighborhood Search Algorithm  
The VNS algorithm was initially introduced by [PERSON] et al. [32]. It employs a dynamic adjustment of the neighborhood structure during the search process, with the goal of finding optimal solutions that balance between focused local area search and diverse exploration of larger areas. VNS is particularly effective in tackling local optimization problems associated with NP-hard problems, including constrained optimization problems [33,34]. Algorithm 1 presents the pseudocode of VNS.  
```
1 Initialization Parameters: maximum iterations of VNS (Maxiter), neighbourhood structures size \(N_{l}(l=1,\ldots l_{max})\) Receive/generate population with size \(P\) and assess them Pick up the best solution as initial solution \(X^{*}\)
2 Foriter\(\leq\)Maxiterdo
3 For\(N_{l}\leq\)\(l_{max}\)do
4\(X^{\prime}\) = shaking (\(X^{*},N_{l}\))
5\(X^{\prime\prime}\) = local search (\(X^{\prime}\))
6 If\(f(X^{*}\leq X^{*})\)
7\(X^{*}\gets X^{\prime\prime}\)
8\(l=1\);
9 Else
10\(l=l+1\);
11 Endif
12 Endfor
13 Endfor
14 Report the best solution;
15 End
```  
**Algorithm 1**Pseudocode of VNS
## 3 Principles of Signal Transmission and Processing
### Implementation of Multi-Thread Clock Synchronization  
To overcome the challenge of clock synchronization in weak light communication, a methodology is employed whereby the \(P_{c}(t)\) array from the preceding step undergoes low-pass filtering to generate the \(y_{c}(t)\) data sequence. This sequence is then matched with the symbol synchronization header at the receiver, facilitating precise clock synchronization. Specifically, researchers utilize the local link header at the receiver end as the reference signal, conducting matching operations with the received data sequence. Through second-order correlation solving, accurate determination of the transmission frame's starting position is achieved, effectively addressing timing synchronization issues during the transmission process.  
\[Y(t)=\begin{bmatrix}y_{1,1}(t)&y_{2,1}(t)&y_{3,1}(t)&y_{4,1}(t)\\ y_{1,2}(t)&y_{2,2}(t)&y_{3,2}(t)&y_{4,2}(t)\\ \vdots&\vdots&\vdots&\vdots\\ y_{1,L}(t)&y_{2,L}(t)&y_{3,L}(t)&y_{4,L}(t)\end{bmatrix} \tag{17}\]  
The investigators integrate the envelope outputs \(y_{1}\), \(y_{2}\), \(y_{3}\), and \(y_{4}\) from the four frequency channels into a matrix \(Y\), where \(L\) denotes the count of local link symbol synchronization headers. This methodology effectively harnesses the frequency characteristics of photon pulses, enabling precise control of clock synchronization in weak light communication. The local symbol synchronization header is represented as \[\beta=\begin{bmatrix}\beta_{1,1}&\beta_{2,1}&\beta_{3,1}&\beta_{4,1}\\ \beta_{1,2}&\beta_{2,2}&\beta_{3,2}&\beta_{4,2}\\ \vdots&\vdots&\vdots&\vdots\\ \beta_{1,L}&\beta_{2,L}&\beta_{3,L}&\beta_{4,L}\end{bmatrix} \tag{18}\]  
where \(\beta\) comprises four columns, \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\), and \(\beta_{4}\), corresponding to the link frame headers of four carrier frequencies. Each column consists of L data points. Subsequently, the elements of the two matrices are calculated to obtain \(z(t)\):  
\[z(t)=\sum_{i=1}^{4}\sum_{j=1}^{L}y_{i,j}(t)\cdot\beta_{i,j} \tag{19}\]  
Find the time \(t_{max}\) corresponding to the maximum matching degree:  
\[t_{\max}=\arg\max_{t}(z(t)) \tag{20}\]  
This formula represents the time point corresponding to the highest correlation degree, \(z(t)\), providing theoretical support for solving the frame synchronization problem in frequency-modulated communication systems.
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 2 Challenges in Traditional XRD Data Analysis
### Phase Identification and Crystallographic Analysis  
Identifying and distinguishing between multiple phases present in a complex sample can be challenging, especially when diffraction peaks overlap or exhibit broadening due to microstructural effects. Additionally, crystallographic analysis to determine lattice parameters and symmetry requires meticulous peak indexing and fitting.  
The successful accomplishment of this task often requires experienced personnel. In terms of procedures, several strategies were reported. Manual search was first implemented by [PERSON] [79] and consisted of comparisons among the three most intense characteristic _d_-spacings determined from the patterns. Crystallographic analysis of new materials can be performed using a full-pattern fitting algorithm like Rietveld refinement [79], which compares a measured profile with a calculated one from crystal structure data. This method accounts for several contributions to the XRD pattern: scale factor, multiplicity, Lorentz polarization factor, structure factor, absorption, and extinction. Thus, simultaneous phase identification and crystallographic analysis are enabled.
## 3 Real-Building Case Study  
### Sensors from a Real Campus Building  
The sensors from Nesbitt Hall are used to demonstrate the developed framework. Nesbitt Hall (Figure 2) is a campus building located at Drexel University in Philadelphia. It is a seven-story, 7246 m\({}^{2}\) commercial building that includes classroom, office, and laboratory space. It has one chiller and two steam-to-hot-water heat exchange systems. It has three air handling units (AHUs) located in the basement and on the roof. The chiller system is in the basement with the corresponding cooling tower on the top of the building. More details about heating, ventilation, and air conditioning (HVAC) systems and equipment of Nesbitt Hall can be found in Appendix A.  
Among the 539 sensors from Nesbit Hall, 18 sensors with missing data listed in Table 1 are selected to test the framework. There are five considerations of choosing these sensors for the case study: (1) the sensors have adequate missing data; (2) the sensors are typical sensors in BAS; (3) the sensors cover various sensor types, including an energy meter, flow meter, thermometer, humidity sensor, ampere meter, valve position, and differential pressure sensors; (4) the sensors cover various equipment, including a chiller, AHU, and a variable-air-volume (VAV) box, and (5) the sensors have various patterns and characteristics of missing data, including missing data size and randomness. The data are downloaded via the Nesbitt Hall BAS web interface from 1 st January to 16 th August in 2017, which covers summer, winter, and shoulder seasons. Data are collected every 5 min (i.e., with a 5-min sample interval).
## 6 The Role of miRNAs in IDD
In a recent study, the role of exosomal circRNA_0000253 was explored, suggesting that it may competitively absorb miRNA-141-5p and downregulate SIRT1 promoting IDD progression [140]. Further research is required to optimize the effects of MSCs-derived exosomes and to confirm the association of their miRNAs with the pathogenesis of IDD. It will be crucial to know the active components responsible for their actions, underlying mechanisms, pharmacokinetic aspects, and safety of this approach. Table 3 summarizes the different miRNA studied in IVD research. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **miRNA** & **Target Gene** & **Results** \\ \hline [PERSON] et al. [127] & miR-21 & PTEN & miR-21 alleviated TNF-\(\alpha\) induced NPC apoptosis by inhibiting PTEN \\ \hline [PERSON] et al. [128] & miR-210 & HOXA9 & miR-210 significantly decreased NPC apoptosis via downregulation of HOXA9 \\ \hline [PERSON] et al. [129] & miR-223 & - & miR-223 may be a predictive biomarker associated with persistent lumbar radicular pain \\ \hline [PERSON] et al. [130] & miR-146a & TRAF6/IRAK-1 & An increase in miR-146a protects against IL-1 induced IVD degeneration and inflammation \\ \hline [PERSON] et al. [131] & miR-494-5p & TIMP-3 & A dysregulation of miR-494-5p could decelerate the progression of IDD via promoting the expression of TIMP3 \\ \hline [PERSON] et al. [132] & miR-142-3p & MLK3 & Inhibition of inflammation, cell apoptosis, and MAPK signaling activation in NPCs through the downregulation of MLK3 \\ \hline [PERSON] et al. [133] & miR-199 & MAP3K5 & TNF-\(\alpha\) induced NPC apoptosis by downregulating MAP3K5 \\ \hline [PERSON] et al. [134] & miR-199a & GREM1 & miR-199a carried by BM-MSC EVs promotes IVD repair by targeting GREM1 and downregulating the TGF-\(\beta\) pathway \\ \hline [PERSON] et al. [135] & miR-27a & PIJK/MMP-13 & Upregulation of miR-27a promotes apoptosis in human IVD by targeting PI3K \\ \hline [PERSON] et al. [136] & miR-27a & PI3K/MMP-13 & miR-27a suppressed ECM degradation and MMP-13 expression induced by IL-1\(\beta\) in NP cells \\ \hline [PERSON] et al. [137] & miR-532-5p & RASSF5 & Suppression of TNF-\(\alpha\)-induced apoptosis, ECM degradation, and fibrosis in NPCs \\ \hline [PERSON] et al. [138] & miR-140 & NLRP3 & miR-410 delivered by MSC exosomes may inhibit NLRP3 inflammasome-mediated pyroptosis in NPCs \\ \hline [PERSON] et al. [139] & miR-31-5p & ATF6 & MSC exosomes negatively regulated ER stress, apoptosis and calcification in CEP through miR-31-5p \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of available evidence on the role of miRNA in IDD.
# Energy Efficient Secure Communication Model against Cooperative Eavesdropper
## 4 Conclusions  
In this research work, we study a cooperative eavesdropper model in a wire-tap channel and derive the SINR of the jamming signal and information signal for the cooperative eavesdroppers network. We specifically consider that the eavesdropper has the CSI of the nearest node with negligible channel estimate error in order to study the optimum \(\beta\) (i.e., power ratio between the jamming signal and information signal). The simulated results of the expected jamming signal estimate error at the nearest eavesdropper (i.e., E2) to the jammer shows that, by increasing the total of number of antennas at the jammer, the jammer can degrade the performance of E2, even if E2 has an equal number of receiving antennas. Therefore, jamming the signal SNR degradation at E2 can degrade the performance of the MIMO IBDFE receiver.  
The BER results of the information signal at the nearest eavesdropper (i.e., E1) to the transmitter show that the BER of E1 can be degraded by optimizing the \(\beta\) value at E1 to 1. The optimization of \(\beta\) leads to energy efficient and secure communication, since \(\beta=1\) at E1  
Figure 7: The performance of IBDFE versus ZF at E1, where the feedback of jamming signal estimate is included to reduce interference.  
is better for the legitimate network than for using a \(\beta\) greater than 1. Therefore, we conclude that, under a severely restricted environment, a legitimate network can improve the secrecy rate and can achieve better energy efficiency by increasing the number of antenna at a jammer and by optimizing \(\beta\).  
Conceptualization, A.R. and R.D.; investigation, A.R., M.B. and R.D.; software, A.R.; supervision, R.D. and D.N.K.J.; validation, D.N.K.J. and R.D.; visualization, A.R.; writing--original draft, A.R., M.B., D.N.K.J. and R.D.; writing--review and editing, A.R. and R.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by FCT/MCTES Copelabs (UIDB/04111/2020) and Instituto de Telecomunicaceles (UIDB/EEA/50008/2020). The research work is developed in part with Universidade Lusofona/ILIND funding and supported, in part, by the Russian Foundation for Basic Research (RFBR) grant N* 19-37-50083-19. Also, in part, by the framework of Competitiveness Enhancement Program of the National Research Tomsk Polytechnic University, Russia.  
Not applicable.  
The authors declare no conflict of interest.
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
## 2 Numerical Method
### Non-Dimensional Parameters  
In this section the non dimensional groups used along the paper are introduced.  
**Reynolds number**  
\[Re=\frac{U_{\infty}C}{\
u} \tag{11}\]  
where \(U_{\infty}\) is the free-stream velocity, \(C\) the chord length and \(\
u\) the fluid kinematic viscosity.  
**Lift coefficient**  
\[C_{l}=\frac{2F_{l}}{\rho CL_{\infty}^{2}} \tag{12}\]  
where \(\rho\) is the free-stream fluid density, and \(F_{l}\) the dimensional lift force.  
\begin{table}
\begin{tabular}{c c} Reynolds number (\(Re\)) & 68.5 \(\times\) 10\({}^{3}\) \\ Freestream velocity (\(U_{\infty}\)) & 1 m/s \\ Kinematic Viscosity (\(\
u\)) & 1.4599 \(\times\) 10\({}^{-5}\) m\({}^{2}\)/s \\ Density (\(\rho\)) & 1.225 kg/m\({}^{3}\) \\ Pulsating flow fluid density (\(\rho_{j}\)) & 1.225 kg/m\({}^{3}\) \\ Angle of attack (\(\alpha\)) & 15\({}^{\circ}\) \\ Chord length (\(C\)) & 1 m \\ \end{tabular}
\end{table}
Table 1: Physical conditions.  
Figure 2: Sketch of the groove location and injection angle over the airfoil.
# Unveiling Biological Activities of Marine Fungi: The Effect of Sea Salt
## 2 Materials and Methods
### FTIR-ATR Analysis of Mycelium  
FTIR analyses provide a spectrum that allows for differentiating the functional groups of molecules, including those from fungi [45]. Lyophilized mycelia grown with and without 3% sea salt were analyzed by FTIR-ATR using a Perkin Elmer (Norwalk, CT, USA) Spectrum BX FTIR instrument. The mycelia were analyzed at a 4 cm\({}^{-1}\) resolution within the 4000-400 nm range. Air was used for the background spectrum.