esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunable non-Hermiticity in Coupled Photonic Crystal Cavities with Asymmetric Optical Gain
## 3 Results and Discussion
### Asymmetric Gain in the Doubly Coupled PhC Cavity
This result directly confirms that our analysis of the non-Hermitian optical system based on Equation (4) is effectively valid for describing the asymmetrically coupled cavity system and for explaining the control of non-Hermitian properties. Taken together, the asymmetric optical gain introduced to the doubly coupled PhC cavity can enable controllable interplay between \(\Delta\gamma\) and \(J\). This helps in simultaneously tuning the eigenfrequency and field profile in a fixed cavity structure, and it provides an unprecedented active tuning mechanism for coupled cavity-based optical devices. Figure 5: Eigenfrequencies of doubly coupled PhC cavity with vertical airhole shifting. (**a,c**) The calculated eigenfrequencies of the coupled cavity modes in a doubly coupled PhC cavity with (**a**) one and (**c**) two vertical airhole shifts. The optical gain in component cavities 1 and 2 followed the trajectory shown in Figure 3b. The real (black squares) and imaginary parts (red circles) of eigenfrequencies obtained from the numerical full-wave simulation were co-plotted with analytical solutions (black and red lines) obtained from Equation (4). (**b,e**) The cavity mode profiles of the antibonding and bonding modes at \(\gamma_{1}=\gamma_{2}=\gamma_{\text{max}}\) in the doubly coupled PhC cavity with (**b**) one and (**d**) two vertical airhole shifts. The field distributions clearly show the in-phase and out-of-phase characteristics in the coupled modes. (**c,f**) The intensity profiles of the amplifying and decaying modes at \(\gamma_{1}=\gamma_{\text{max}}\) and \(\gamma_{2}=0\) in the doubly coupled PhC cavity with (**c**) one and (**f**) two vertical airhole shifts.
## 3 Digital Signage Systems' Design and IoT Technologies Comparison
### Comparison of IoT Protocols
#### 3.2.1 Message Queue Telemetry Transport (MQTT) Protocol  
MQTT, invented by IBM, is designed to be a notably lightweight protocol at the application layer. It is therefore suited for IoT, featuring a fixed header size of only 2 bytes, and extended for Wireless Sensors Networks (WSN) and M2M communications. MQTT is based upon Publish/Subscribe, whereas the publisher and the subscriber operate as MQTT clients and MQTT devices, while the MQTT broker operates as the MQTT server. Clients publish and subscribe messages to defined topics which are stored in the broker, pictured in the MQTT Flow Diagram (Figure 4).  
MQTT has been designed to send messages from 0-256 Mb to one or multiple devices with low latency and is suitable for messaging over fragile networks. It is highly recommended to be used in wireless networks where we w  
Figure 4: MQTT flow diagram.  
sages without high bandwidth demands, as bandwidth issues can induce latency. As far as security is concerned, clients and MQTT devices validate the MQTT server using an SSL/TLS certificate, stored in the server. In sequence, clients and devices provide their own certificates to the MQTT broker, which is used for authentication [30; 31]. This bidirectional MQTT functionality makes the protocol favourable in industrial IoT applications, where it is commonly used.  
Taking it further, MQTT features include QoS (Quality of Service), LWT (Last Will and Testament), and Retain Flag [31; 32]. QoS defines the way messages are communicated between the client and the server. More specifically, with QoS 0 a message is broadcasted without guarantee of the delivery (mostly used in UDP networks), with QoS 1 the message is broadcasted until there is an acknowledgement from the receiver, and with QoS 2 the message is broadcasted precisely once until there is receiver acknowledgement [31; 32]. LWT is a feature which notifies the subscribers of a sudden disconnection of the publisher like e.g., network failure [31]. With the Retain Flag, the broker stores the last retained message and broadcasts it again to update to the newly connected subscribers, a feature that proves useful in reconnecting situations. For our evacuation scenario, QoS 1 proposes that the message will be received reliably and quickly, and the passengers will be notified. LWT ensures that the clients will be notified in case the server crashes and \"Retain Message\" ensures that the clients will be immediately updated after a reconnection, both of which are necessary in a situation such as ship wreckage.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 5 Conclusions  
In this study, we investigated how flood extents could be observed using SAR small-satellite constellation on a spatial and temporal scale. In the first step, we designed constellations using the Walker Constellation method, with parameters of \(T\) (total number of satellites), \(P\) (number of equally spaced orbit planes), and \(F\) (relative phase difference between satellites in adjacent planes). Then, the designed SAR small-satellite constellations were applied in a virtual flood monitoring experiment involving numerical simulations of two case studies of flood events in Japan.  
Each constellation's observation behavior was successfully presented, and an original performance index was introduced to evaluate the flood monitoring observation performance. The results demonstrated that a SAR small-satellite constellation with sun-synchronous orbit at 570 km altitude, 30 km swath, 15-30\({}^{\circ}\) incidence angle, and 20 satellites can achieve 87% acquisition of the damage extent (cumulative flood extent) in a time series observation in one case. Comparing the results of two cases, it was found that the observation performance depends on each flood's characteristics and it is related to some features of the SAR small-satellite observation system.  
The results of two case studies in Japan under a condition with a specific satellite and orbit, which are shown in this paper, would be insufficient to conclude definitively the specific requirements of a SAR small-satellite constellation in terms of flood disaster monitoring. However, it was suggested that an individual assessment would be needed for each flood case in various regions with several kinds of satellite and orbit (e.g., a lower orbital inclination which is focusing on low and middle latitudes), since the optimal parameter settings are different in each case. In this regard, our proposed approach can be applied to any flood cases in regions all over the world, so this study should be useful in that it built the first framework and established the evaluation method for future analysis. Therefore, based on this research, more comprehensive analyses in other areas with other small SAR satellites should be performed to determine the universal applications as needed.  
Conceptualization and methodology, N.K., R.S., and S.K.; software and formal analysis, N.K.; resources, D.Y., X.Z., and W.M.; writing--original draft preparation, N.K.; writing--review and editing, R.S. and S.K.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by MEXT/JSPS KAKENHI 16H06291.  
Today's Earth research products used in this paper were supplied by Japan Aerospace Exploration Agency and Institute of Industrial Science, The University of Tokyo.  
The authors declare no conflict of interest.
## 3 Results
### The Headspace Composition of Unifloral Bee Pollen
vulgaris_ bee pollen were composed of the following major compounds: heptan-2-ol (30.63%), acetic acid (9.27%), nonanal (4.60%) and lilac aldehyde B (5.20%). The major compounds of _R. lutea_ bee pollen HS were: acetic acid (19.09%), 6-methylhep-5-en-2-one (9.56%), benzaldehyde (5.60%), phenylacetaldehyde (11.79%) and nonanal (8.14%). _C. biennis_ bee pollen HS VOCs contained acetoin as the major compound (19.41%). Another group of abundant compounds were lilac aldehydes (A 5.61%, B 9.09% and D 3.28%). Lower aliphatic aldehydes were also present (pentanal 5.83%, hexanal 5.22% and nonanal 3.65%) as well as 2-ethylhexan-1-ol (5.41%). The bee pollen of _P. mahaleb_ among major HS constituents contained lilac aldehydes (A 14.69%, B 25.04% and D 14.51%), accompanied by lilac alcohol A (2.99%). Another abundant compound was nonanal (5.19%). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**No.** & **Compound** & \multicolumn{8}{c}{**Sample \({}^{*}\)**} \\ \hline  & & RI \({}^{*}\) & A & B & C & D & E & F \\ \cline{3-8}
1. & Acetaldehyde & \(<\)900 & 5.63 & - & - & - & - & - \\
2. & Ethanol & \(<\)900 & 4.39 & 3.01 & 3.14 & 2.99 & - & - \\
3. & Dimethyl sulfide & \(<\)900 & - & 4.30 & - & 6.48 & - & - \\
4. & Acetic acid & \(<\)900 & 6.93 & 9.30 & 9.27 & 19.09 & - & - \\
5. & 3-Methylbutanal & \(<\)900 & 2.87 & 1.86 & - & - & - & - \\
6. & 3-Hydroxy-butan-2-one (acetoin) & \(<\)900 & 5.05 & 3.99 & 2.68 & 3.67 & 19.41 & - \\
7. & Pentanal & \(<\)900 & - & 1.46 & 1.24 & - & 5.83 & - \\
8. & Butane-1,3-diol & \(<\)900 & 0.53 & 1.36 & 0.27 & 1.17 & - & - \\
9. & Butane-2,3-diol & \(<\)900 & 0.62 & 1.23 & 0.43 & 1.12 & - & - \\
10. & Hexanal & \(<\)900 & 4.88 & 3.56 & 2.54 & 2.45 & 5.22 & 1.44 \\
11. & 4-Methylpentan-1-ol & \(<\)900 & - & - & 2.75 & - & - & - \\
12. & (_E_)-Hex-2-enal & \(<\)900 & 0.30 & - & 0.24 & 0.52 & - & - \\
13. & Hexan-1-ol & \(<\)900 & - & 0.67 & 0.15 & - & - & - \\
14. & Heptran-2-one & \(<\)900 & - & - & 2.08 & - & - & - \\
15. & Heptran-2-ol & 902 & - & - & 30.63 & - & - & - \\
16. & Heptran & 904 & 4.46 & 2.19 & - & 1.50 & - & - \\
17. & \(\gamma\)-Butylolactone & 922 & - & - & 2.13 & 0.34 & 0.93 & 0.30 \\
18. & Methyl hexanoate & 928 & - & - & - & 0.45 & 0.70 & - \\
19. & \(\alpha\)-Pineine & 942 & - & 0.39 & - & 0.19 & - & - \\
20. & Benzaldehyde & 969 & 4.88 & 6.36 & 3.62 & 5.60 & 3.14 & 2.09 \\ \hline \end{tabular}
\end{table} 
Table 5: Volatile headspace compounds (%) from the bee pollen samples collected from Adriatic region.
## 1 Introduction
### Related Work
The stability level of the DC-MG is extracted and derived using both the average dwell-time scheme and the piecewise Lyapunov method. A resiliency enhancement is covered in Reference [21] by proposing a dynamic MG system that is mainly utilized to recover the installed critical load after abnormal conditions, such as natural disasters. This process is performed using the installed Renewable Energy Recourses (RESs), storage systems, and the demand response scheme. The paper also specified the needed budgets to restore the normal operation of the distribution system in case of an emergency. Unlike the aforementioned works, as far as our knowledge, our work introduces a new framework for determining the resiliency of MG systems. This is done by taking into consideration various aspects as opposed to just one aspect (e.g., the recovery time proposed by References [8,12], and the fraction of load unaffected by distortion used by Reference [11]). Our proposed framework is more general as it includes the aspects of voltage sag percentage, load served, recovery time, and time to reach the power balance state. Furthermore, our framework can allow the improvement of MG scheme as it can provide feedback to the design and development process. In addition to that, References [7,9,10,11] did not consider enabling resiliency as opposed to our work. In our framework, a resiliency enhancement process is achieved by implementing load-shedding algorithm to meet power balance state. Thus, maintaining the service of critical loads.
## 5 Multiple Interval Optimization by Dynamic Programming
### The Multiple Interval Motion Optimization Algorithm
#### 5.2.2 Find the Optimal Motion Path  
Since \(\mathcal{V}(T)=0\), when the cost graph is constructed, the costs of middle layer nodes can be calculated based on (16). Then, the optimal motion plan can be found from \(X_{0}\) to \(T\), which is the path that generates the cost for \(\mathcal{V}(X_{0})\). The detail of the algorithm is given in Algorithm 3.  
```
Input:\(X_{0},T,\lambda,\lambda_{1},\lambda_{2}\), interval length \(\Delta_{t}\) Output:\(\{X_{1},X_{2},\cdots,X_{n}\}\), i.e., the optimal motion plan of the MASS
1 Step 1: Construct the cost graph:
2 Initialize \(\mathcal{V}(T)=0\); \(t=0\); \(\mathbf{X}_{t}=\{X_{0}\}\) for\(t=1\); \(t=t+1\); do
3 for\(X_{i}^{t}\in\mathbf{X}_{t}\)do
4 Generate next time states from \(X_{i}^{t}\), denoted by \(\mathbf{s}_{\mathbf{i},\mathbf{i}+\mathbf{1}}=\{X_{i+1}^{1},X_{i+1}^{2},\cdots, X_{t+1}^{n_{i}}\}\) ;
5 Delete the states beyond \(T\). The remaining set is denoted by \(\mathbf{s}_{\mathbf{i},\mathbf{i}+\mathbf{1}}^{\prime}\) ;
6 Calculate VOs of \(X_{t+1}^{j}\in\mathbf{s}_{\mathbf{i},\mathbf{i}+\mathbf{1}}^{\prime}\) based on predicted locations of other ships ;
7 Add edges, and calculate the edge cost from \(X_{i}^{t}\) to \(X_{j}^{t+1}\in\mathbf{s}_{\mathbf{i},\mathbf{i}+\mathbf{1}}^{\prime}\), denoted by \(\mathcal{A}_{i,j}^{t+1}\);
8 Update vertexes at time \(t+1\), i.e., \(\mathbf{X}_{t+1}=\mathbf{X}_{t+1}\cup\mathbf{s}_{\mathbf{i},\mathbf{i}+\mathbf{ 1}}^{\prime}\) ;
9 if\((\mathbf{X}_{t+1}==\emptyset)\)then
10 break;
11 Step 2: Find the optimal motion plan for\(\tau=t-1\); \(\tau--\); do
12 for\(X_{i}^{\tau}\in\mathbf{X}_{\tau}\)do
13\(\mathcal{V}(X_{i}^{\tau})=\min\limits_{X_{i}^{\tau}+V_{\tau+1}^{i}\Delta_{t} =X_{j}^{\tau+1}\&\&\&X_{j}^{\tau+1}\in\mathbf{X}_{\tau+1}}\left\{\eta\mathcal{ V}(X_{j}^{\tau+1})+\mathcal{A}(X_{i}^{\tau},X_{j}^{\tau+1})\right\}\)
14 Start from \(X_{0}\) to generate the motion path \(X_{1},X_{2},\cdots\) with the minimum cost to reach \(T\) ; return\(\{X_{0},X_{1},\cdots,T\}\)
```  
**Algorithm 3**Multiple interval velocity optimization.  
The first step to generate the cost graph starts from \(X_{0}\) and then expands the next states at interval \(t+1\) until beyond the destination. The edges and one-step costs on the edges are generated by (17). The second step evaluates the minimum cost of each node in the graph. The optimal motion path is the path that derives the minimum cost for \(X_{0}\).  
Figure 6: An illustration of the multiple interval dynamic programming approach for motion planning.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Regulatory Challenges in AI Healthcare: Adapting to AI's Evolving Nature
#### 5.2.1 Navigating Regulatory Challenges in the Dynamic AI Healthcare Landscape  
Addressing concrete examples of regulatory challenges further elucidates this complex landscape. Traditional regulatory standards for assessing the safety and efficacy of medical software may be ill-suited to the dynamic nature of AI. AI algorithms, especially those based on machine learning, can undergo changes and improvements over time, potentially altering their performance and safety profile post-approval. Additionally, concerns regarding liability and accountability arise when AI systems malfunction or cause harm in a healthcare setting. Current legal frameworks may not be equipped to address these new challenges, such as determining liability when an AI system's decision results in a medical error. Moreover, the heavy reliance of AI systems on large datasets, often containing sensitive patient information, poses significant challenges to data privacy and security, especially as AI systems require continuous access to new data to learn and improve [2].
## 4 Machine Learning Algorithms
### Support Vector Classifier (SVC)  
In the SVC realm, support vectors refer to points closest to the boundary of the hyperplane, while the margin is the distance between the hyperplane and these support vectors [50; 51; 52]. This structure is fundamental to the operation of the SVC, as illustrated in Figure 2. The equation defining the hyperplane is given in (18).  
\[d(x)=w^{T}\cdot x+b \tag{18}\]  
where \(x\) signifies the input vector, the weight vector perpendicular to the hyperplane is represented by \(W\), and \(b\) represents the \(y\)-axis intercept. Additionally, the width of the decision boundary margin can be determined as in (19).  
\[\gamma=\frac{2}{\|w\|} \tag{19}\]  
Here, \(\|w\|\) represents the Euclidean norm of the weight vector.  
To address noise, skewness, and irrelevance information associated with data, the concept of a soft-margin SVM was introduced in [53]. Consequently, the margin can be reformulated to optimize this trade-off as follows:  
\[\gamma=\frac{2}{\|w\|^{2}}+C{\sum}_{i=1}^{n}\zeta_{i} \tag{20}\]  
where the slack variable (\(\zeta_{i}\)) is introduced to accommodate potential, and the \(C\) parameter is the slack penalty. Consecutively, the convex optimization problem for maximizing the margin in the SVC domain can be expressed with the constraint as \(y_{i}\big{(}w^{T}\cdot x_{i}\big{)}+b\ \geq 1-\zeta_{i}\), where \(\zeta_{i}\ \geq\ 0\). However, due to the inherent nonlinearity, the original dataset (\(x_{i}\)) must be projected into a higher-dimensional space (\(\psi(x_{i})\)), where a linear boundary can effectively separate the dataset \(x_{i}\). Given the computationally intensity of this process, the deployment of the kernel function becomes crucial, offering an efficient solution to the inner product calculation by using the kernel trick \(k(x,\ x_{i})=\phi(x)\). \(\phi(x_{i})\). The most well-known kernel in SVC is the Gaussian kernel, also known as the radial basis function (RBF) kernel, which defines \(k(x,\ x_{i})\) as follows [51; 52; 54]:  
\[k(x,x_{i})=\ e^{-\phi\|x-x_{i}\|^{2}},\text{where }\delta=\frac{1}{2\xi^{2}} \tag{21}\]where \(\delta\), the user-defined parameter, regulates the flexibility of the decision boundary, and the RBF kernel width is defined using \(\varepsilon\). Lastly, the standard form of the SVC can be formulated as follows [45; 55; 56; 57; 58]:  
\[f(x)=\sum_{i=1}^{n}\left(\alpha_{i}\,y_{i}\,k(x,\,x_{i})\right)+b \tag{22}\]  
where \(\alpha_{i}\) is known as the Lagrange multiplier.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## Materials and Methods
### MODIS GPP Products  
The MODIS primary production products (MOD17A2) were designed to provide an accurate regular measure of the growth of the terrestrial vegetation with a spatial resolution of 1 km and temporal resolution of 8 days [50]. MOD17 GPP outputs, which were generated using a radiation use efficiency model, were useful for global carbon cycle analysis, ecosystem status assessment, and environmental change monitoring. Modification of parameters in the Biome property look-up table (BPLUT) had been made to agree with GPP derived from measurements at eddy flux towers and estimated GPP [51]. The BPLUT contains parameters for temperature and vapor pressure difference (VPD) limits, light use efficiency, specific leaf area and respiration coefficients for representative vegetation in each biome type [51]. In this study, MODIS GPP products from May to September in 2014 were used as the reference data to cross validate the up-scaling results.
# Tampered and Computer-Generated Face Images Identification Based on Deep Learning
## 3 Methodology
### TFID Extensions for Imbalanced Dataset Problem (IDP)  
As explained in the introduction section, the number of tampered images is insignificant compared to the massive number of real images in existing tampered face datasets. As a result, previous tampered face images and GAN-generated images identification frameworks have suffered significantly from the IDP [1, 13]. This section demonstrates three well-known techniques to solve the IDP for TFID model, including ensemble-based technique, cost-sensitive learning technique, and data-based technique, as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**TFID Framework**} \\ \hline
**Layer Name** & **Configuration** & **Output (Rows, Cols, Channels)** \\ \hline Input & & \(256\times 256\) \\ Convolution\_1 & \(11\times 11\) & (246, 246, 32) \\ Maxpool\_1 & \(2\times 2\) & (123, 123, 32) \\ Dropout\_1 & Probability: 0.2 & \\ Convolution\_2 & \(7\times 7\) & (117, 117, 64) \\ Maxpool\_2 & \(2\times 2\) & (58, 58, 64) \\ Dropout\_2 & Probability: 0.2 & \\ Convolution\_3 & \(5\times 5\) & (54, 54, 128) \\ Maxpool\_3 & \(2\times 2\) & (27, 27, 128) \\ Dropout\_3 & Probability: 0.2 & \\ Convolution\_4 & \(3\times 3\) & (25, 25, 256) \\ Maxpool\_4 & \(2\times 2\) & (12, 12, 256) \\ Dropout\_4 & Probability: 0.2 & \\ Convolution\_5 & \(3\times 3\) & (10, 10, 512) \\ Maxpool\_5 & \(2\times 2\) & (5, 5, 512) \\ Dropout\_5 & Probability: 0.2 & \\ Convolution\_6 & \(3\times 3\) & (3, 3, 1024) \\ BatchNorm & & (3, 3, 1024) \\ Dropout\_4 & Probability: 0.5 & \\ Flatten & Length: 16 & (9216) \\ Dense & Length: 2 & (2) \\ \hline \hline \end{tabular}
\end{table}
Table 1: A detailed configuration and output of each layer in the proposed TFID framework.  
Figure 5: Implementation of three different approaches to deal with the imbalanced data problem (IDP) for the proposed TFID model.  
In the first approach, the softmax layer that determines the multi-class probabilities for each test sample is replaced with an XGBoost boosting function. For the cost-sensitive learning approach, a class weight is assigned for each class. Finally, an over-sampling approach is applied to the minority class (tampered images). In contrast, an under-sampling method is conducted on the majority class (real images) to obtain a new balanced dataset. A detailed explanation for each approach is described as follows.
## 2 Literature Review
### Dynamic-Pricing Problem  
[PERSON] is the originator of revenue management theory research. In 1972, he introduced the application of revenue management theory that was based on the example data in the aviation field for the first time [11]. As the relevant theories and methods of revenue management continue to mature, scholars in some developed countries such as the UK, the US, and Japan have started to introduce the dynamic-pricing method into the railway field [6; 12; 13]. However, in developing countries such as China, where a fixed ticket price policy had been used for a long time, although floating pricing reform has been started in recent years, it is still in the experimental stage, so it is necessary to pay attention to the research results concerning dynamic pricing. [PERSON] and [PERSON]'s examination of the interacting relationships between the presence of HSR transportation and other important dimensions of city development in determining land prices could help policy-makers devise ways to curb the escalation of property prices while enjoying the benefits of a HSR [14]. In recent years, due to the increasingly fierce competition with air transport, scholars have studied the dynamic pricing model of HSRs while considering the competitive situation [15; 16; 17]. Some scholars studied different types of passenger groups and decided to set more attractive prices to expand railway passenger flow, and finally increased the profit of enterprises [18; 19]. To optimize HSR ticket prices based on passenger choice behavior, it is necessary to analyze the arrival patterns of the passengers and the change patterns of passenger demand in the pre-sale period, and to reasonably quantify the influencing factors [20; 21; 22; 23]. [PERSON] et al. [24] proposed a data-driven ticket dynamic pricing methodology for a railway service provider. [PERSON] et al. [25] considered that passenger demand is sensitive to the generalized travel cost and that the train's stopping plan can affect the travel time and passenger distribution. Then, a mixed-integer non-linear optimization model was proposed for the joint problem of ticket pricing and trains' stop planning to maximize the HSR's transport revenue and minimize passengers' travel time.