esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Mean Field Power Control Game
### Mean Field Game  
#### 5.1.1 Mean Field and Player Interactions  
The definition of mean field is core to the design and solution of MFG and the mean field is defined as the statistical distribution of state behavior of the game as:  
\[m(t,S)=\lim_{K\rightarrow\infty}\frac{1}{K}\sum_{k=1}^{K}\mathbb{1}_{[S_{k}(t) =S]}, \tag{25}\]  
where \(\mathbb{1}\) represents an indicator function which is one if the given condition is true and zero elsewise. \(S_{k}(t)=[\,E_{k}(t),\,I_{k}(t)]\) is the state space dynamics of the power control game. Here, the mean field depicts the probability distribution of states over the player set at any given time instant.  
Conventional game theory designs are unable to withstand the demands of a densified user environment because of the dimensional difficulty associated with achieving a converged solution. An MFG design utilizes large user numbers to balance out their effect on the game and only provides brief information about individual players. This also simplifies the design as the player numbers continue to grow in a real-time environment. HJB equation is used to model an individual player's relation with the mean field and the collective behavior of all the players as per chosen actions is modeled by an FPK equation [40]. These coupled FPK and HJB equations are known as backward and forward equations, respectively according to the nature of their interactions with the mean field. The conversion of a differential power control game for MTCDs to an MFG power control design requires the fulfillment of the following necessary properties:  
* Individual player makes a small contribution towards mass of all the other players and the MFG. This property ensures that the players act in a more rational and independent manner while choosing the optimal control policy.
* The action selection by each player depends on its own interests and only interacts with the mean field of players instead of participating in individual interactions.
* The continuity property of the mean field model is ensured by the provision of the massive number of MTC devices for power control.
* When analyzing a K-player game, the MFA technique is used to model the actions of the players and prove the exchangeability property.  
Moreover, solutions to the MFGs can be obtained in a distributive manner and the behavior of all the players can be described by a single control strategy. These properties ensure that MFG can indeed provide an optimal transmit power control policy for a massive number of MTC users in the proposed network environment. The modelling of the mass effect of all players (the mean field) must be based on realistic parameters and it poses a major design challenge in application of mean field concept for solving problems in wireless communications.
# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 3 Detail Guided Multilateral Segmentation Network
### Network Structure
#### 3.2.4 Three-Path Feature Fusion Module  
The detail guided path and context path are different in the level of feature representation. The output of the detail guided path contains more low-level detail information, while the output of the context path is high-level semantic information. The two kinds of information complement each other well, and the output of the semantic information supplement module provides semantic information from another perspective. Therefore, in order to effectively fuse the information extracted from the three paths, the TFFM module is proposed, whose specific structure is shown in Figure 5.  
Firstly, the low-level semantic information output \(F_{D}\) via the detail guidance path and the high-level semantic information output \(F_{C}\) via the context path are fused. Referring to the idea of feature interaction and repeated use, the two features from different paths are refined by the FAM module. After fusion, the refined features pass through a convolution layer and are summed with the original input element by element to obtain \(F_{D}\)\({}^{\prime}\) and \(F_{C}\)\({}^{\prime}\). \(F_{D}\)\({}^{\prime}\) and \(F_{C}\)\({}^{\prime}\) are concatenated along the channel direction and convolved to obtain the feature fused by the two path features. Next, we fuse the features from the graph convolutional path in a similar scheme to obtain an output \(F_{T}\) of size \(1/16\). The decoder of the network is composed of four transposed convolution layers. After passing through one transposed convolution layer, the size of the feature map will be doubled. Finally, the output feature of the original size is obtained, and the image is segmented using this feature. The formula of the feature fusion module is expressed as follows:  
\[\begin{cases}F_{D}\text{${}^{\prime}$}=conv(concat(FAM(F_{D}),FAM(F_{C}))),\\ F_{C}\text{${}^{\prime}$}=conv(concat(FAM(F_{D}),FAM(F_{C})))\end{cases} \tag{9}\]  
\[F_{DC}=conv(concat(F_{D}\oplus F_{D}\text{${}^{\prime}$},F_{C}\oplus F_{C}\text {${}^{\prime}$})) \tag{10}\]  
\[\begin{cases}F_{DC}\text{${}^{\prime}$}=conv(concat(F_{DC},UpSample(FAM(F_{S}))) ),\\ F_{S}\text{${}^{\prime}$}=conv(concat(F_{DC},UpSample(FAM(F_{S}))))\end{cases} \tag{11}\]  
\[F_{T}=conv(concat(F_{DC}\oplus F_{DC}\text{${}^{\prime}$},F_{S}\oplus F_{S}\text {${}^{\prime}$}) \tag{12}\]  
where \(F_{D}\), \(F_{C}\) and \(F_{S}\) respectively represent the feature maps output via the CF-path, DIG-path and SIS-path, \(FAM(\cdot)\) represents the FAM channel submodule, and \(UpSample\) represents bilinear upsampling.
## 4 Results and Discussion  
### Instrument-Level Experiment Results  
#### 4.1.1 Instrument-Level Bench Stability Experiment Results  
This instrument-level bench stability experiment in a thermal vacuum environment was performed to verify the temperature control effect and radiation response stability of the PSAC. The results of the bench stability experiment are shown in Figure 10. The PSAC continuously observed the integrating sphere for approximately 9 min, and 544 sets of data were collected. Based on the dark reference provided by the chopper and the PSAC DCR function, the DN fluctuated by approximately 0.75%. The temperatures of the heat sink, photosensitive surface, and main light path components were stable. The results show that in the instrument-level thermal vacuum environment, the turning optical path and temperature control effect were relatively stable, thus providing a basis for Gins(T) measurement.  
Figure 10: Results of the instrument-level stability experiment. The DN fluctuated by approximately 0.75%, and the temperatures of the heat sink, photosensitive surface, and the turning optical path were stable.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 4 Active Barriers
### Iron-Based  
A form of oxidation that is well-known to anyone, is the rusting of iron. It therefore seems obvious that this mechanism could be employed as an oxygen scavenger in packaging as well. Iron particles are added to a packaging, mostly in a pouch form [148]. Added as a blend in the polymer, it will usually lead to the visual appearance of the particles in the packaging [149]. Recently however, there have been researches that claim to resolve this issue by adsorbing iron nanoparticles on nano-sized clays in order to provide both the active and passive barrier [150]. While for commercial systems, the discoloration will prove application in organic electronics highly unlikely, the development of this nano-technology may open up some possibilities in this field.  
The oxidation chemistry of iron is given by reactions (12)-(14). Metallic iron can donate 2 electrons in an anodic reaction. The layer of water surrounding the iron particles acts as the electrolyte to provide oxygen with the electrons and form hydroxides in water as the cathodic reaction. The released iron ions may then be ligated in solution to form iron(II) hydroxides.  
Further oxidation of the iron will ultimately provide trivalent hydroxide species that can recombine by dehydration, forming the red iron oxide Fe\({}_{2}\)O\({}_{3}\)[151].  
\[\mathbf{Fe^{(0)}+2\ H_{2}O\rightarrow\ Fe^{(II)}(OH)_{2}+\ 2\ e^{-}+2\ H^{+}} \tag{12}\]  
\[\mathbf{H_{2}O+\frac{1}{2}\ O_{2}+2\ e^{-}\rightarrow 2\ OH^{-}} \tag{13}\]  
\[\mathbf{2\ Fe^{(II)}(OH)_{2}+\ 2\ H_{2}O+\ \mathbf{O_{2}\rightarrow\ Fe_{2}O_{3} \cdot 3H_{2}O}} \tag{14}\]  
Eventually, the deposited oxide will envelop the metal particles, preventing oxygen diffusion to the reduced bulk iron particles and halting oxygen uptake [152].  
The scavenger composition can be altered to accommodate for a faster reaction rate. Reaction 13 can be favored by addition of an acidifying component. Oxygen reduction requires both a proton and an electron so adequate dissociation of water is preferred. Additionally, an extra electrolyte in contact with the iron will also accelerate the oxygen uptake by enhancing the electron transfer [153; 154; 155]. High surface areas are preferred in this reaction, increasing scavenging rate and time before deactivation. Nano-sized iron particles have been found favorable over the traditional micron-sized powders [151]. For food applications however, this is not accepted due to the toxicity of such nanoparticles. As an advantage to iron-based scavengers, the water requirement in the reaction will provide that the iron will achieve low activities during the empty shelf life period, but will start to oxidize mainly after the filling of the bottle.
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
###### Abstract  
Traffic on backbone communication networks is growing significantly every year. This results in an increase in both energy consumption and the carbon footprint they leave on the environment. As a response, research efforts are focused on reducing energy consumption in telecom networks. Wavelength division multiplexing (WDM) optical networks are key for addressing rising bandwidth demands in backbone networks, but this leads to a concurrent surge in energy usage. Additionally, regions with high seismic activity risk damage to backbone networks from earthquakes, causing significant bandwidth loss and service disruptions. This paper aims to reduce the energy consumption in a backbone network by implementing an algorithm that optimizes energy efficiency while preserving network connectivity and resistance to earthquake phenomena. The proposed algorithm redesigns and modifies a backbone network by deactivating the unnecessary links without affecting the network performance. The scheme is extensively evaluated through simulations using real seismic data from the Geodynamic Institute of the National Observatory of Athens, confirming earthquake resilience and energy efficiency goals, with an energy saving of up to 9% compared to existing solutions.  
earthquakes; network connectivity; energy efficiency; minimize energy consumption
###### Abstract  
An important development direction for the future of the automotive industry is connected and cooperative vehicles. Some functionalities in traffic need the cars to communicate with each other. In platooning, multiple cars driving in succession reduce the distances between them to drive in the slipstream of each other to reduce drag, energy consumption, emissions, and the probability of traffic jams. The car in front controls the car behind remotely, so all cars in the platoon can accelerate and decelerate simultaneously. In this paper, a system for vehicle-to-vehicle communication is proposed using modulated taillights for transmission and an off-the-shelf camera with CMOS image sensor for reception. An Undersampled Differential Phase Shift On-Off Keying modulation method is used to transmit data. With a frame sampling rate of 30 FPS and two individually modulated taillights, a raw data transmission rate of up to 60 bits per second is possible. Of course, such a slow communication channel is not applicable for time-sensitive data transmission. However, the big benefit of this system is that the identity of the sender of the message can be verified, because it is visible in the captured camera image. Thus, this channel can be used to establish a secure and fast connection in another channel, e.g., via 5G or 802.11p, by sending a verification key or the fingerprint of a public key. The focus of this paper is to optimize the raw data transmission of the proposed system, to make it applicable in traffic and to reduce the bit error rate. An improved modulation mode with smoother phase shifts is used that can reduce the visible flickering when data is transmitted. By additionally adjusting the pulse width ratio of the modulation signal and by analyzing the impact of synchronization offsets between transmitter and receiver, major improvements of the bit error rate (BER) are possible. In previously published research, such a system without the mentioned adjustments was able to transmit data with a BER of 3.46%. Experiments showed that with those adjustments a BER of 0.48% can be achieved, which means 86% of the bit errors are prevented.  
automotive applications; connected vehicles; vehicle safety; visible light communication; differential phase shift keying +
Footnote †: journal: Applied Soft. 2021, 11, 2195. [[https://doi.org/10.3390/app11052195](https://doi.org/10.3390/app11052195)]([https://doi.org/10.3390/app11052195](https://doi.org/10.3390/app11052195))
## 4 Case Analysis
### Accuracy Validation of Fault Prediction Models with Large Sample Cases  
This section verifies the reliability of the above state evaluation and fault prediction methods in large sample cases, and verifies the self-learning ability and self-improvement ability of digital twin technology.  
1. First, 600 records of fault handling of protection system equipment in a region of the Southern Power Grid from 2017-2019 were selected, and divided randomly and equally into four groups.
2. In each group, the real-time monitoring information of 24 h before each fault occurrence moment was intercepted. The 24 h was divided into six time periods, and moments 1 to 7 were set. The time \(N\) was selected when its status changed from \"attention\" to \"severe\", the characteristic sequence of the equipment at time \(N\) was extracted, the fault prediction algorithm according to the steps described in Section 3.1 was calculated, and the prediction results were compared with the actual defect processing records.
3. After each group of control verification, the actual results of this group of verification were added to the database, and the clustering center was updated to make updates and corrections to the database. The self-learning ability and growth of this research method was verified by observing the change of algorithm accuracy of four groups of data.  
Table 11 shows the results of the fault prediction for the first group of 150 fault records, at the moment when the status of the equipment changed from \"Attention\" to \"Severe\". A total of 136 correct predictions were made for the 150 fault records. The correct rate was 90.67%.  
Figure 10: Relative correlation degree with each fault type.  
The database was supplemented and improved by adding the 150 defect processing records of the first group to the database. Similarly, the data of the second, third and fourth groups were verified in turn, and the actual fault results were added to the database after each group was verified. The variation of the correct rate of the fault prediction algorithm is shown in the following Figure 11.  
As can be seen from Figure 11, the accuracy of the fault prediction method proposed in this paper could reach more than 90%, and the accuracy of the algorithm improved from 90.67% in the first set of data to 92.67% in the fourth set of data after the continuous improvement of the historical database. It was proved that this research method was able to optimize and improve the algorithm according to the actual fault results in actual operation, and had self-learning ability and growth.
# On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers
## 5 Design of the Road-Grip Aware Control Modules
### Autonomous Emergency Brake  
Road accidents and fatalities statistics are reported annually, showing the relation between accidents and drivers behaviour [3, 4]. Moreover, the authors in [57] showed that the collision risk increases with the degradation of road conditions. The Autonomous Emergency Brake is one of the most effective driving functionalities for collision prevention and social cost lowering linked to accidents. Nonetheless, EuroNCAP tests are being carried on roads with friction peaks of at least 0.9, even if in real situations a lower value reduces the safeness and the robustness of the whole system.  
In this perspective, the aim of the grip-aware AEB system proposed by the authors is to identify the collision risk depending on the actual road conditions and, hence, to take control of the brakes to avoid possible accidents or at least to reduce their severity. Here, we base the decision-making of AEB according to the Time-To-Collision (TTC) in Equation (4), where the detection threshold depends on the estimated road-grip as:  
\[TTC_{th}(t,\bar{\eta})=\frac{v(t)}{\bar{\eta}a_{brk}}, \tag{60}\]  
where \(a_{brk}\) is the deceleration value commanded to the ABS in case of emergency, i.e., \(9.8\,\mathrm{m/s^{2}}\).
## 2 Materials and Methods
### DNA Sequencing  
The internal transcribed spacer 1 (ITS1) region [20] was amplified using universal fungal primers ITS1F and ITS2 [21, 22] attached with the adapter sequences for Illumina MiSeq (Illumina, Inc., San Diego, CA, USA). Each polymerase chain reaction (PCR) mixture (30 mL) contained 1 mL DNA template, 0.33 mL of each primer, 2\(\times\) PCR Solution Premix TaqMan DNA polymerase (Takara Bio Inc., Otsu, Shiga, Japan), and ultrapure water. The thermal conditions were: 5 min at 95 \({}^{\circ}\)C for initial denaturation, followed by 35 cycles of 30 s at 95 \({}^{\circ}\)C for denaturation, 30 s at 55 \({}^{\circ}\)C for annealing, and 30 s at 72 \({}^{\circ}\)C for extension. The final elongation was conducted for 10 min at 72 \({}^{\circ}\)C. AMPure XP beads (Beckman Coulter, Inc., Brea, CA, USA) were used to remove remaining primers and primer dimers. Index PCR was performed with the purified PCR products and a Nextera \(\times\)T Index kit (Illumina). Each reaction mixture contained 5 mL of the purified PCR amplicons, 5 mL of the index primer pair, 25 mL of 2\(\times\) PCR Solution Premix TaqMan polymerase (Takara), and 15 mL of ultrapure water. The thermal conditions were: 3 min at 95 \({}^{\circ}\)C for initial denaturation, followed by 10 cycles of 30 s at 95 \({}^{\circ}\)C for denaturation, 30 s at 55 \({}^{\circ}\)C for annealing, and 30 s at 72 \({}^{\circ}\)C for extension. The final elongation was conducted for 5 min at 72 \({}^{\circ}\)C. The indexed PCR products were purified using the AMPure XP beads (Beckman, Pasadena, CA, USA), normalized to 4 nM with 10 mM Tris-HCl (pH = 8.5) and pooled with an internal control PhiX (30%). The pooled, heat-denatured amplicons were loaded onto the v3 600 cycle kit reagent cartridge (Illumina, San Diego, CA, USA). Illumina MiSeq was used for 2 \(\times\) 300 bp paired-end sequencing. The raw sequencing data were uploaded to the Sequence Read Archive of the NCBI under the accession number PRJNA513204.  
Figure 1: Locations of the homes sampled in an area of Trang Province in Thailand.
# A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching
###### Abstract  
The life prediction is crucial to guarantee the reliability and safety of the mechanical system. The current prediction methods predict the life only based on the historical usage pattern of the mechanical system, and do not consider the mission profile of the future working process. To realize the life prediction with considering the switching of mission profile, which is composed of different operating conditions, this paper proposes a new prediction scheme on the base of the similarity trajectory method (STM). Two main improvements are employed. First, the reference degradation models are constructed according to the predicted trend of each constant operating condition obtained by the relevance vector machine (RVM). Secondly, the life under specific mission profile is calculated through weighted aggregating the life of each constant operating condition. The proposed method is validated by a turbofan engine simulation data. The results show that the proposed method achieves an excellent predicted result, in which the predicted result is close to the actual result. In addition, the proposed method can deal with the problem of mission profile switching.  
mission profile; operating conditions; relevance vector machine; life prediction; similar trajectory +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
The main contributions of this research lie in: (1) an integrated algorithm for HRTF individualization combining correlation analysis, principle component analysis, multiple linear regressions and partial least square regression was presented; (2) two psychoacoustic experiments were conducted to explore the superiority of the individualized HRTFs in perceiving the spatialization cues of sound sources. All the results support that the individualized HRTFs obtained through the presented integrated method achieve preferable performance in perceiving the spatialization cues than the general non-individualized ones.  
Given the conclusions above, individualized HRTFs should be more necessary in practical applications, such as virtual hearing, tel-conference, etc. However, the practical applications involve not only spatialization cues, but also other attributes such as timbre. The use of HRTFs or even individualized HRTFs may possibly cause some other good or bad effects, which were not considered in this research. Thus, future work will need to investigate whether HRTFs and individualized HRTFs leads to some potential effects on binaural auditory in practical applications.  
Conceptualization, L.W. and X.Z.; methodology, L.W.; investigation, L.W.; writing--original draft preparation, L.W.; writing--review and editing, X.M.  
This research was funded by the Natural Science Foundation of China, grant number 11774291 and 51705421. And APC was funded by the Natural Science Foundation of China, grant number 11774291.  
The authors appreciate all the subjects in the subjective experiment for their participation.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\mathbf{p}\) & \(\mathbf{H_{0}}\) & Mean\_ind & Mean\_non \\ \hline
0 & Rejected & 35.9091 & 53.7860 \\ \hline \end{tabular}
\end{table}
Table 9: Post-hoc for two-way repeated ANOVA. Mean_ind and mean_non denote the mean localization errors of the individualized HRTFs and the non-individualized ones, respectively.  
Figure 14: The result of post-hoc for ANOVA. X-axis denotes the localization errors. Y-axis denotes the testing HRTF species of (1) individualized HRTFs and (2) non-individualized HRTFs. Red and blue lines denote the HRTF species of individualized one and non-individualized one, respectively. Circles on the lines denote the mean class of the corresponding HRTF species. The lengths of the lines denote the mean confidence intervals of the corresponding HRTF species.