esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Optimal Orchestration Process
### Automation of Algorithm Selection
#### 3.5.2 Reinforcement Learning Algo Selection Using AutoRL  
AutoRL was chosen for its capacity to automate the selection and fine-tuning of reinforcement learning algorithms, enhancing online learning capabilities. Its design allows for adaptive learning in real time, making it particularly well suited for environments characterised by fluctuating demands and resource availability. Compared to traditional reinforcement learning approaches, AutoRL reduces the need for manual intervention and optimisation, thereby improving efficiency and responsiveness in resource orchestration.  
Figure 8 presents the AutoRL process flow, which demonstrates the initial environment state assessment, the algorithm selection among multiple options (DQN, PPO, and A3C), hyperparameter optimisation, and continuous monitoring and adaptation.
# A Simulation-Based Optimization Method for Warehouse Worker Assignment
## 4 Methodology  
[PERSON] et al. [6] considered the staff scheduling in the warehouse on both weekly and daily granularity levels. They built three mixed integer programming (MIP) models and solved the models to obtain the optimal staff assignment and shift pattern. Their objective was to minimize the cost, while assuming that all the tasks should be completed by hiring sufficient number of staff. However, in the daily operations of the warehouse, the number of available workers may not be enough to complete all the tasks. In this situation, the objective is to finish as many tasks, with an optimal worker assignment.  
The selected warehouse in this study serves as a distribution center with variations in product types and demands. There are different teams working on different activities. The warehouse operates as one single processing unit, which consists of sequence of activities. The major concern of the warehouse is to assign the optimal number of workers to each team. Moreover, under uncertainty in demand and processing time, this may require reallocation of worker(s) to other teams. This adds an additional layer of complexity to the problem and requires a methodology that can solve the problem. Given these requirements, following [PERSON] et al. [37], we devised a simulation-based optimization model that explores both simulation and optimization methods to improve the service level performance, by suggesting appropriate manpower assignment to different activities. First of all, the discrete-event simulation model was developed, considering the relationship between different activities. Then, the processing time of each activity was defined along with an initial assignment of manpower. Due to uncertainty in daily workload, the initial solution is not always optimal. Therefore, additional effort is required to optimally assign the manpower. As the assignment of workers to different activities is a complex task, a heuristic algorithm instead of exact algorithm was developed to find the optimal worker assignment. With each iteration, the new assignment of workers is input to the simulation model until the model output achieves the highest service level performance.
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 1 Introduction
Most existing studies have focused on offloading mobile users' workloads to edge data centers to enable energy savings for mobile devices [11; 12]. However, these approaches assume that the edge nodes have already been deployed. Other studies focus on the placement of EDCs in mobile edge environments, paying attention to access delays and workloads [13; 14; 15]. However, these proposals have overlooked scalability, which become increasingly important as networks grow more complex, due to the significant increase in the number of base stations required to provide coverage in 5G/6G environments [9]. With the advent of 5G and MIMO technologies [16], it is crucial that placement strategies are scalable, given the significant increase in the number of access devices and the corresponding deterioration in performance when addressing the EDC placement problem. Therefore, this paper proposes a decentralized cloud-based placement solution that achieves significant improvements in terms of scalability. The rest of the paper is structured as follows. Section 2 outlines this work's proposed methodology and techniques. Section 3 provides a detailed description of the experimental setup used for evaluation. In Section 4, we present the results of the comparative performance evaluation, and, finally, in Section 5, we conclude the paper.
## Materials and Methods
### Characterization Techniques
#### 2.6.3 NMR  
The proton nuclear magnetic resonance (\({}^{1}\)H-NMR) spectra for this study were recorded with a Bruker Avance II (Bruker, Rheinstetten, Germany). The system operated at 600 MHz, with 64 scans, 2.65 s acquisition time, and an 11 \(\upmu\)s pulse width. \({}^{13}\)C-NMR spectra were recorded with a Bruker Avance II operating at 150.9 MHz, with 20,480 scans, 0.9088 s acquisition times, and 9.40 s pulse width. The \({}^{1}\)H-NMR and \({}^{13}\)C-NMR spectra were run in CDCl\({}_{3}\) (room temperature), using tetramethylsilane (TMS) as an internal standard. Homonuclear correlation spectroscopy (COSY), Heteronuclear Multiple Bond Correlation (HMBC), and Heteronuclear Multiple Quantum Coherence (HMQC) were also performed on the same device.  
The NMR spectra for analysis of the carbon sources (PS0-4) were recorded on a 400 MHz JEOL NMR spectrometer JNM-ECZ400R/M1 (Akishima, Tokyo, Japan). Samples consisted of polystyrene (PS0-4; 10-20 mg) dissolved in CDCl\({}_{3}\). All spectra were internally referenced to the residual solvent [40]. \({}^{1}\)H-NMR spectra were acquired with a 45\({}^{\circ}\) pulse and an inter-pulse delay of 5 s across 16 transients, with an acquisition time of 2.18628 s and a pulse width of 6.48 \(\upmu\)s. Proton-decoupled \({}^{13}\)C-NMR spectra were recorded with a 30\({}^{\circ}\) pulse and inter pulse delay of 5 s across 4097 transients, with an acquisition time of 1.03809 s and a pulse width of 10.338 \(\upmu\)s. Phased Heteronuclear Single Quantum (HSQC) spectra were recorded using a matrix consisting of 256 \(\times\) 819 points across eight scans, with a relaxation delay of 3 s. Heteronuclear Multiple Bond Correlation (HMBC) spectra were recorded using a matrix consisting of 512 \(\times\) 1638 points across eight scans, with a relaxation delay of 3 s. Homonuclear Correlation Spectroscopy (COSY) spectra were recorded by using a matrix of 1024 \(\times\) 1024 points across 1 scan, with a relaxation delay of 3 s.
# Enhancing Cybersecurity Through Comprehensive Investigation of Data Flow-Based Attack Scenarios
## 2 Background
### Attack Surfaces and Attack Scenarios  
In cybersecurity, the attack surface commonly refers to the total number of points, interfaces, connections, and interactions with environments through which an attacker can potentially exploit vulnerabilities, launch malicious actions, or gain restricted data in the system. An attack scenario typically refers to paths by which cybercriminals can gain unauthorized access to a system or network.  
An attack scenario refers to a sequence of attack paths that an attacker might follow to exploit vulnerabilities and compromise a system. Attack scenarios are essential for cybersecurity professionals, as they are crucial for designing adequate protections and play a vital role in overall cybersecurity strategy. The landscape of attack scenarios in cybersecurity is constantly evolving, with new scenarios emerging as technology advances and cybercriminals become more sophisticated. Cybercriminals may use a single attack path or a combination of paths to compromise a system or network. The difficulty of identifying a specific attack scenario depends on various factors, including the complexity of the system or network, the level of security measures in place, and the attacker's skills and resources. In certain cases the path may be relatively easy to find, such as when a  
Figure 1: STRIDE acronym legend [13].  
system or network has known vulnerabilities that have not been sufficiently remediated. In other cases the path may be more challenging to identify, such as when an attacker uses advanced techniques or a combination of paths to avoid detection and bypass security measures. Finding attack scenarios is essential for cybersecurity professionals to develop appropriate security measures.  
A common example of an attack scenario in a trusted smart system is unauthorized access by an attacker who manipulates the functionality of critical components. For instance, in a smart home system an attacker could potentially spoof sensor data to deceive the central control unit about home conditions in real time. This could result in false commands being issued, such as unlocking doors or disabling security systems, potentially compromising the safety and security of the residents. Another example is a false data injection attack on a trusted smart health monitoring system, where an attacker manipulates vital sign readings to provide inaccurate health information, leading to incorrect medical decisions. Such attack scenarios targeting the integrity of both the digital and physical aspects of trusted smart systems can have serious consequences, highlighting the need for robust security measures tailored to networked systems.
## 4 Problem Complexity  
### NP-Hardness of the Problem  
**Theorem 1**.: _The problem of joint optimization of caching and recommendation expressed in Formulation (5) is NP-hard._Proof.: We prove Theorem 1 by showing how the joint optimization problem reduces to the weighted \(0/1\) knapsack problem through constructing a special case of the problem that relaxes the original problem to the weighted \(0/1\) knapsack problem.  
We construct a special case of the original problem by setting \(m=1\), \(d=0\), \(\gamma=-1\) and \(\alpha=0\). Under these settings, we have the following:  
* \(m=1\implies u=1\), i.e., only a single user;
* \(d=0\implies D_{u}=0\), the second constraint of Formula (5) is satisfied and hence can be removed (no distortion);
* \(\gamma=-1\implies R_{l}^{u}=0\), the third constraint of Formula (5) is satisfied and hence can be removed (no recommendation);
* \(\alpha=0\implies\hat{P}_{i}=P_{i}\), the fourth constraint of Formula (5) is satisfied and hence can be removed (no modification to request frequency).  
Under the above realization, the original problem is reduced to the following simplified problem:  
\[\begin{array}{rl}\max_{X}&H=\sum_{i=1,2,\ldots,n}x_{i}\hat{P}_{i}\\ s.t.&\sum x_{i}w_{i}\leq c\end{array} \tag{6}\]  
Clearly, the problem of Formula (6) is the classical weighted \(0/1\)-backpack problem with backpack capacity \(c\), item value \(P_{i}\), and weight \(w_{i}\), which is well-known to be NP-hard. The above reduction shows that the simplified joint optimization problem is NP-hard, and therefore so is the original problem of Formula (5).
## 2 System Architecture, Theoretical Methods, and Experiments
### Raw Data Collection  
The subject of research in this study is audio captured from the ground above field pipelines in various districts of the Taiwan Water Corporation. These data signals are mixed with various random environmental audio and underground leakage audio, so the events corresponding to each time point cannot be clearly marked. Therefore, it is necessary to introduce unsupervised learning algorithms (otherwise called swarming algorithms) to perform labeling. However, if the original signal is directly sent to the unsupervised learning algorithm for learning, the \"curse of dimensionality\" can arise [14], compromising the labeling. To address this issue, this study extracted representative features from the original data, and the algorithms then used these representative features to learn. In this study, the encoder in the autoencoder is used to complete feature extraction.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 3 Results
### Survey of DNB Imagery
#### 3.1.2 Observations for Twilight Scenes  
Evaluating nonuniformity for twilight is more difficult for two reasons: the twilight is observed for only a short period in the orbit; in the twilight region there are no radiometrically uniform scenes because of the steep change in radiance as day transitions to night (or vice versa). However, it is an important assessment to make, given that twilight observations dominate the imagery available to Alaska Region operational weather, aviation, and sea ice forecasters (heavy consumers of DNB imagery) during the winter season. In order to observe features in twilight, the Near Constant Contrast algorithm is used to produce imagery from DNB radiance. An NCC image is shown in Figure 2.  
The NCC twilight image in Figure 2 contains striping that is strongest on the right side near the end of scan. The transition separating the MGS and the LGS data is shown with a light blue line. The transition occurs where MGS is close to saturation, and this transition is not directly triggered by solar angles.  
In the LGS region the image appears grainier because LGS is at low end of its dynamic range and therefore has a lower signal-to-noise ratio (SNR). The striping in the MGS is similar to the striping in the LGS. This is not surprising, because even if the gain ratios were perfect, any gain errors in LGS are transferred to the MGS via the multiplication of the MGS radiance by the gain ratio. There is, however, a vertical zigzag artifact in the LGS that is not seen in the MGS. The likely cause of this artifact will be discussed later in this paper.  
Figure 3 shows most of the same DNB scans as Figure 2, but shifted to the left so that the center is near nadir. The ground track location is shifted deeper into the twilight than the end-of-scan. There is striping here across the entire swath. The very bright streak of light on the right side of the image is an example of the Aurora Australis. Other fainter streaks of the aurora appear across the entire image. Surface features of Antarctica include some mountains showing through the ice sheet. The transition separating the MGS and the LGS data is again shown with a light blue line, and the transition separating HGS and MGS is shown as a green line. It is remarkable that all three gain stages appear here in one image and the dynamic range of the radiance shown here is about three orders of magnitude. Twilight images such as this present the biggest challenge for destriping algorithms. Notice that the striping patterns in the LGS, MGS and HGS are similar. Again, this shows that much of this striping is transferred from a lower gain stage to the higher gain stage through the gain ratios.
## 2 Materials and Methods  
### Numerical Calculation Models  
#### 2.1.1 Governing Equations  
A numerical simulation was established based on the homogeneous multiphase flow model, which treats the gas-liquid mixture as a homogeneous medium. Both phases share the same pressure, velocity, and temperature. The continuity, momentum, enthalpy, and transport equations in the Cartesian coordinate system are as follows [30]:\[\frac{\partial\rho_{m}}{\partial t}+\frac{\partial\left(\rho_{m}U_{j}\right)}{ \partial x_{j}}=0 \tag{1}\]  
\[\frac{\partial\left(\rho_{m}U_{l}\right)}{\partial t}+\frac{\partial\left(\rho_ {m}U_{l}U_{j}\right)}{\partial x_{j}}=-\frac{\partial p}{\partial x_{i}}+\frac {\partial}{\partial x_{j}}\Bigg{[}(\mu_{m}+\mu_{tur})\Bigg{(}\frac{\partial U _{i}}{\partial x_{j}}+\frac{\partial U_{j}}{\partial x_{i}}-\frac{2}{3}\frac{ \partial u_{k}}{\partial x_{k}}\delta_{ij}\Bigg{)}\Bigg{]} \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho_{m}(h+f_{v}L_{ev}))+\frac{\partial}{\partial x _{j}}\big{(}\rho_{m}U_{j}(h+f_{v}L_{ev})\big{)}=\frac{\partial}{\partial x_{j }}\Bigg{[}\Bigg{(}\frac{\mu_{m}}{Pr_{lam}}+\frac{\mu_{tur}}{Pr_{tur}}\Bigg{)} \frac{\partial h_{m}}{\partial x_{j}}\Bigg{]} \tag{3}\]  
\[\frac{\partial\rho_{l}\alpha_{l}}{\partial t}+\frac{\partial\left(\rho_{l} \alpha_{l}U_{j}\right)}{\partial x_{j}}=m^{+}+m^{-} \tag{4}\]  
where the mixture density and the mixture laminar viscosity are shown as:  
\[\rho_{m}=\rho_{l}(1-\alpha_{v})+\rho_{v}\alpha_{v} \tag{5}\]  
\[\mu_{m}=\mu_{l}(1-\alpha_{v})+\mu_{v}\alpha_{v} \tag{6}\]  
In the above equations, \(\rho_{m}\) is the mixture density. \(\rho_{l}\) and \(\rho_{v}\) are the liquid and vapor density, respectively. \(\alpha_{l}\) and \(\alpha_{v}\) are the liquid fraction and the vapor fraction, respectively. \(U\) is the velocity; \(p\) is the pressure; \(\mu_{m}\) is the mixture laminar viscosity; \(l\) and \(v\) are the liquid and vapor dynamic viscosities, respectively; and \(\mu_{tur}\) is the turbulent viscosity. \(f\) is the mass volume fraction, \(L_{ev}\) is the latent heat, \(h\) is the enthalpy, and \(Pr_{tur}\) and \(Pr_{lum}\) are the turbulent and laminar Prandtl numbers, respectively. The subscripts \((i,j,k)\) denote the directions of the Cartesian coordinates, and the subscript m denotes the mixture phase. The source term \(m^{+}\) and the sink term \(m^{-}\) in Equation (4) represent the condensation and evaporation rates, respectively.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 3 Methods
### Classifiers  
In this study, two classifiers were used for classification: support vector machine (SVM) and random forest (RF).  
The SVM classifier is a machine learning method based on statistical learning theory [51], which has been widely used in remote sensing data classification and has achieved better classification accuracy [52, 53]. It searches the optimal hyperplane by mapping the input vector into the high-dimensional space to separate the training vectors of the two classes into two subspaces [33]. In this paper, the radial basis function (RBF) kernel was implemented, and the kernel parameters kernel width and penalty parameter were set to 0.125 and 100, respectively.  
The random forest (RF) classification algorithm belongs to an ensemble classifier class and is built on multiple decision trees, with each tree being fitted to a different bootstrapped training sample and a randomly-selected set of predictive variables [7, 9, 32, 54]. The final classification or prediction results are obtained by voting [7, 54]. A large number of studies have proved that the random forest algorithm has high prediction accuracy [55, 56], good tolerance for abnormal values and noise, and is not prone to over-fitting. In this study, 500 trees were generated using the square root of the total number of predictors at each node.
## 4 The Proposed Routing Protocol
### Routing Procedure in the Proposed Protocol
In the proposed protocol, we have used SFRRP that applies a hop-to-hop delivery instead of the broadcasting method to decrease the network traffic and consumption of energy. SFRRP is an inferential smart algorithm that chooses the selected node from among the list of neighbors to transmit the data packet to the base station via that node. In this protocol, there is no need to maintain discovered paths. This feature causes the sensor nodes to need short buffer sizes. Moreover, SFRRP employs fewer intermediate nodes to transmit the data packets to the base station. Furthermore, the network traffic decreases and the network lifetime increases. Less network traffic causes a decrease in the delay time of the transmitted data to the base station. The work flow of SFRRP is shown in Figure 9. Note that the reconfiguring operation, applying a signal carrier, the real-time state of the network, and determination of the active or passive modes are considered based on the method presented in [41]. Fuzzy decision making is only one of several useful decision making procedures which are based on mathematical proofs and presented in the literature [33-35]. However, the functional efficiency of the proposed protocol is also related to some parameters such as the number of nodes, initial energy of nodes, node buffer size and deploying method of the nodes in the whole network. Therefore, the efficiency of the proposed protocol is compared to another routing protocol with the same parameters in order to not exaggerate the functionality of the proposed approach.
# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
###### Abstract  
Ginseng is a perennial herbaceous plant that has been widely consumed for medicinal and dietary purposes since ancient times. Ginseng plants require shade and cool temperatures for better growth; climate warming and rising heat waves have a negative impact on the plants' productivity and yield quality. Since Republic of Korea's temperature is increasing beyond normal expectations and is seriously threatening ginseng plants, an early-stage non-destructive diagnosis of stressed ginseng plants is essential before symptomatic manifestation to produce high-quality ginseng roots. This study demonstrated the potential of fluorescence hyperspectral imaging to achieve the early high-throughput detection and prediction of chlorophyll composition in four varieties of heat-stressed ginseng plants: Chunpong, Jakyeong, Sunil, and Sunmyoung. Hyperspectral imaging data of 80 plants from these four varieties (temperature-sensitive and temperature-resistant) were acquired before and after exposing the plants to heat stress. Additionally, a SPAD-502 meter was used for the non-destructive measurement of the greenness level. In accordance, the mean spectral data of each leaf were extracted from the region of interest (ROI). Analysis of variance (ANOVA) was applied for the discrimination of heat-stressed plants, which was performed with 96% accuracy. Accordingly, the extracted spectral data were used to develop a partial least squares regression (PLSR) model combined with multiple preprocessing techniques for predicting greenness composition in ginseng plants that significantly correlates with chlorophyll concentration. The results obtained from PLSR analysis demonstrated higher determination coefficients of R\({}^{2}_{\text{val}}\) = 0.90, and a root mean square error (RMSE) of 3.59%. Furthermore, five proposed bands (683 nm, 688 nm, 703 nm, 731 nm, and 745 nm) by stepwise regression (SR) were developed into a PLSR model, and the model coefficients were used to create a greenness-level concentration in images that showed differences between the control and heat-stressed plants for all varieties.  
Keywords:plant phenotyping; ANOVA; band selection; chlorophyll; band ratio image; SPAD readings; PLSR; chemical image +
Footnote †: journal: Applied Sciences