esa-sceva/satcom-chunk-collection huggingface dataset:
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
## 5 Simulation and Experimental Results Analysis
### Simulation Results  
The simulation parameters of the DSDT system are listed in Table 2. To evaluate the estimation and compensation performance of the proposed algorithm for the amplitude-frequency-phase error, we first investigated the synchronization performance in the presence of amplitude error, frequency error, and phase error, respectively, and compared it with [11; 12].  
Figure 9 shows a comparison of [11] and the proposed algorithm with respect to the performance of amplitude compensation. It can be seen that the proposed algorithm exhibited a lower bit error rate after amplitude compensation. Meanwhile, the performance comparisons of the phase and frequency compensation with [12] are illustrated in Figures 10 and 11. As a result, the proposed algorithm demonstrates superior performance compared to the methods in [11; 12], and it is capable of simultaneously handling amplitude, phase, and frequency offset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Modulation & QPSK \\ Bandwidth/MHz & 2 \\ Sample rate/MHz & 20 \\ Number of sub-carriers & 4 \\ Sub-spectrum bandwidth/MHz & 0.5 \\ Sub-spectral roll-off factor & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sub-spectra synchronization experiment parameters.  
Figure 9: BER curves for synchronization methods in presence of \(A\in[0.8,12]\). (Black simulation curve is from [11]).  
Figure 12 shows a BER comparison of various error compensation methods in the presence of amplitude-frequency-phase errors. It can be seen that the proposed algorithm had a significantly lower BER compared to the traditional decision feedback equalization (DFE) algorithm and [12]. On the other hand, although the performance of the proposed algorithm was not as good as that of the pilot synchronization scheme, it could achieve better performance when used together with the pilot algorithm, as shown by purple line in Figure 12.  
Figure 11: BER curves for synchronization methods in presence of \(\Delta f\in[-15,15]\). (Black simulation curve is from [12]).  
Figure 12: BER curves for various synchronization methods in presence of \(A\), \(\Delta f\), and \(\Delta\phi\). (Black simulation curve is from [12]).  
Figure 10: BER curves for synchronization methods in presence of \(\Delta\phi\in[-0.05,0.05]\). (Black simulation curve is from [12]).  
Figure 13 shows a BER performance for various modulation types after synchronization. Compared to the ideal synchronization, the proposed algorithm could all effectively avoid performance loss for these modulation types. It also indicates that the proposed algorithm is not limited by the modulation type of the original signal.
## 2 Materials and Methods
### Data Input of AquaCrop Model and Analysis  
The first calibration experiment was conducted in both Group A, where the rice cultivar RD49 was mechanically transplanted on 9 July 2013 and harvested on 17 October 2013 and Group B, where the rice cultivar RD31 was manually transplanted on 12 August 2013 and harvested on 22 November 2013. Four types of data were required for the analysis: climate data, soil characteristics, crop characteristics and field and irrigation management. The training points used for calibration of the crop parameters totaled 35 pixels.
# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 5 Case Study for the Healthcare Sector
### Scenario Overview  
Patients Patient1, Patient2 with diabetes want to perform a regular check-up. Patient1 is visiting the Hospital for the first time and, hence, a registration process is initiated for collecting personal information. After the registration phase, a Doctor monitors their blood pressure and glucose considering their clinical history of diabetes. Two common processingactivities are identified in the aforementioned scenario (a) Patient Registration and (b) Patient Monitoring. In the former processing activity, we assume patient's registration to the hospital, where the hospital's secretary Secretary1 inserts into the HIS details such as their full name, their contact details etc. In the latter processing activity, we assume the monitoring and storing of the patient's blood pressure and glucose measurements to the hospital's HIS.  
Each processing activity includes a chain of assets considering the interdependencies among them. The hospital's CISO or DPO, periodically performs internal interviews, as a part of her role, in order to confirm the GDPR compliance levels of the organisation. In this sense, she is already aware of the processing activities that should be documented in order to keep track of how sensitive or personal data flow among the various entities and supporting assets. Overall, by utilising the interdependency graphs, a security analyst of the hospital, will be in position to identify potential privacy risks based on a cartography of assets, which encapsulate their vulnerabilities and the potential privacy threats posed against them.
# Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
## 3 Information Exchange between HEMSs
### k Odd, \(\ell\) Even
Once the \((\frac{|\ell-k|+1}{2})\)th HEMS knows the message from the \(\ell\)th HEMS, it computes the sum of signals of the kth and \(\ell\)th HEMSs and then forwards this message in the subsequent time slot to HEMS \(\frac{|\ell-k|+1}{2}-1\). Similarly, the \((\frac{|\ell-k|+1}{2}+1)\)th HEMS relays the superposition of the \(k\)th and \(\ell\)th HEMS' signals to HEMS \((\frac{|\ell-k|+1}{2})+2\). The \((\frac{|\ell-k|+1}{2}-1)\)th and \((\frac{|\ell-k|+1}{2}+2)\)th HEMSs decode the sum of the signals as \(\hat{V}_{k,\ell}\) and then forward these to the nearby HEMSs.The message exchange mechanism keeps repeating until the \(k\)th and the \(\ell\)th HEMS have received the messages from the \((k+1)\)th and the \((\ell-1)\)th HEMSs, respectively. Finally, the \(k\)th and the \(\ell\)th HEMSs compute \(\hat{V}_{k,\ell}\oplus u_{k}\) and \(\hat{V}_{k,\ell}\oplus u_{\ell}\), respectively to obtain the \(\ell\)th and \(k\)th HEMS' messages. The following example can illustrate the aforementioned process:  
_Example:_ Let us consider the example microgrid from Section 3.1. Now, HEMS A wants to exchange the information with HEMS F at a certain instant. At the first time slot, HEMSs A, C, E and G transmit and HEMSs B, D and F receive the sum of signals from HEMS A and C, HEMS C and E and HEMS E and G, respectively. During the second time slot, HEMSs B, D and F transmit their messages, whereas HEMSs C and E receive the sum of signals from HEMSs B and D and HEMSs D and F, respectively. On the other hand, HEMSs A and G receive from HEMS B and HEMS F, respectively. Then in time slot 3, HEMSs B, D and F decode the signals from HEMS A and C, HEMS C and E and HEMS E and G, before forwarding the sum to the corresponding HEMSs, respectively. Then, HEMS A and C, C and E and E and G extract each other's signals. In the fourth time slot, HEMSs C and E decode the signals from HEMS B and D and HEMS D and F, respectively, and forward the sum to the corresponding HEMSs. Then, HEMSs B and D and D and F extract each other's signals. Also, HEMSs A and G decode the signals from HEMS B and F. After this, HEMS C extracts the signal from HEMSs A and E and forwards the sum to HEMS D in time slot 5. On the other hand, HEMS D extracts the signals from HEMSs B and F and forwards the sum to HEMS C in time slot 6. HEMS D already knows the signal from HEMS E (from time slot 2). Thus, it cancels the known information and computes the signal from HEMS A. Similarly, HEMS C already knows the signal from HEMS B (from time slot 2) and cancels this to extract the signal from HEMS F. Then in the seventh time slot, HEMSs C and D compute the sum of the signals from HEMSs A and F and forward that to HEMSs B and E. During time slot 8, HEMSs B and E forward the signal to HEMSs A and F, respectively. After this, HEMSs A and F cancel their own signals and extract each other's messages.
## 4 AdaBoost Regressor-Based Seismic Vulnerability Analysis Method
### AdaBoost Regressor-Based Seismic Vulnerability Analytical Procedures
#### 4.4.3 Part C: Seismic Vulnerability Based on the Multi-Variable-Input AdaBoost Regressor  
Section 4.4.2 demonstrates the single-variable-input seismic vulnerability curve that predicts the failure probability based on the PGA. Other parameters are considered in the statistical calculation. Nevertheless, the input would be multi-variable once the failure probability is estimated not only by PGA but also by other parameters. Therefore, this part establishes the AdaBoost regressor based on multiple parameters as inputs and the responses as outputs. For the given PGA and material parameters, ground motions would change, which results in various responses, so there are also a mean \(\mu\) and a standard deviation \(\sigma\). AdaBoost regressors will be used to replace the mappings from the given PGA and material parameters to the mean \(\mu\) and standard deviation \(\sigma\), respectively. Then, the AdaBoost regressors can be utilized to predict more \(\mu\) and \(\sigma\) values when inputting more parameter combinations. The \(\mu\) and \(\sigma\) values, which represent a distribution, can output the failure probability by inputting one PGA. Then, the data points of failure probability can be obtained as PGA changes, so a seismic vulnerability curve also can be obtained by fitting using log-normal distribution as shown in Equation (3).  
If we would like to investigate the combined effects of PGA and other material parameters, the seismic vulnerability surface also can be acquired. Using the multi-variable-input AdaBoost regressor, the data points of failure probability versus PGA and other material parameters are plotted or clustered, then fitted by a multi-dimensional surface. Then, the features of the vulnerability surface can be accordingly synthetically analyzed.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Logistics Administrative Management
#### 4.3.2 Logistics Communication  
The subject of this case study is a medium-sized clothing factory producing 183 SKUs of uniquely designed local garments. The company distributes through 35 small shops in modern trade outlets across Thailand. The main challenge was the lack of real-time inventory visibility after product delivery to shops due to the absence of a Point-of-Sale (POS) system and reliance on offline, weekly inventory reporting.  
A simple, yet effective solution was implemented: an online spreadsheet-based inventory database (see Figure 6). The new procedure requires each shop to update stock levels daily, enabling more accurate production planning.  
Figure 5: Developed forecasting model using RapidMiner.  
This intervention reduced inventory holding costs at fabrication sites by 16.25% and significantly improved inventory visibility and production planning efficiency.
# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 2 Related Work  
### Target Detection Algorithms  
Target detection is also referred to as target classification detection. Given an image, algorithm is determined whether there is any target instance of multiple predefined categories in the image, and if so, the spatial locations and categories of each instance are returned. Since AlexNet [7] won the ILSVRC-2012 Challenge, the use of convolutional neural networks for target classification has become mainstream. Overall, today's target detectors can be divided into two main types: two-stage inspection frameworks and single-stage inspection frameworks.  
The two-stage detection algorithms include a pre-processing step and a target classification step. The purpose of the pre-processing step is to obtain potential locations that may contain targets. The target classification step is used to determine whether the target exists in the potential location. [PERSON] et al. proposed the Region CNN (R-CNN) algorithm [8]. The algorithm uses a selective search algorithm to create many proposed regions (ROIs) in the image, and converts these regions into fixed-size images which are sent to a pre-trained SVM model to achieve the target classification task. Subsequently, based on the spatial pyramid pooling network model [9], [PERSON] proposed Fast R-CNN [10] which obtains the features of each ROI on the deep feature map of the original image and sends it to the fully connected layer for classification and localization. This operation significantly reduces the time required for each image inspection. In 2015, [PERSON] proposed Faster-RCNN [1] which used the region proposal network (RPN) instead of the selective search algorithm and shared the convolution layer parameters of the RPN and R-CNN networks to simplify the operation and improve the inference speed.  
The single-stage detection (SSD) framework does not have a region proposal step. Instead, the target's bounding box and category are obtained directly in one step. SSD [11] extracts features through a deep convolutional network and directly uses the convolution kernel to achieve target detection. Since the convolutional layer reduces the resolution of the image, the SSD is operated on multiple scales of feature map to detect different scale targets. The YOLO algorithm [12] regards the target detection task as the regression problem of target region prediction and category prediction, and directly obtains the coordinates of the bounding box, the category information and the confidence of the objects using a single neural network.  
Although these algorithms can detect preset types of target from an image, they cannot distinguish between multiple targets of the same type.
# Numerical Study on the Route of Flame-Induced Thermoacoustic Instability in a Rijke Burner
###### Abstract  
The self-excited thermoacoustic instability in a two-dimensional Rijke-type burner with a center-stabilized premixed methane-air flame is numerically studied. The simulation considers the reacting flow, flame dynamics, and radiation model to investigate the important physical processes. A finite volume-based approach is used to simulate reacting flows under both laminar and turbulent flow conditions. Chemical reaction modeling is conducted via the finite-rate/eddy dissipation model with one-step reaction mechanisms, and the radiation heat flux and turbulent flow characteristics are determined by using the P-1 model and the standard k-\(\epsilon\) model, respectively. The steady-state reacting flow is first simulated for model verification. Then, the dynamic pressure, velocity, and reaction heat evolutions are determined to show the onset and growth rate of self-excited instability in the burner. Using the fast Fourier transform (FFT) method, the frequency of the limit cycle oscillation is obtained, which agrees well with the theoretical prediction. The dynamic pressure and velocity along the tube axis provide the acoustic oscillation mode and amplitude, also agreeing well with the prediction. Finally, the unsteady flow field at different times in a limit cycle shows that flame-induced vortices occur inside the combustor, and the temperature distribution indicates that the back-and-forth velocity changes in the tube vary the distance between the flame and honeycomb in turn, forming a forward feedback loop in the tube. The results reveal the route of flame-induced thermoacoustic instability in the Rijke-type burner and indicate periodical vortex formation and breakdown in the Rijke burner, which should be considered turbulent flow under thermoacoustic instability.  
thermoacoustic instability; combustion instability; Rijke burner; CFD; flame-induced turbulence +
Footnote †: journal: Applied Sciences
## 1 Introduction
To simplify the determination process of the \(T_{2}\) cut-off, previous studies have proposed several models to predict the \(T_{2}\) cut-offs of sandstones, shales, and coals. In these methods, fractal analysis was verified to be a useful tool for predicting the \(T_{2}\) cut-offs of sandstones, shales, and coals [32; 33; 34; 35; 36; 25; 37]. Fractal analysis has also become a commonly used method for evaluating the particle and pore distribution characteristics of soil [38; 39; 40; 41]. Nevertheless, almost no studies have established the relationship between the fractal dimension and \(T_{2}\) cut-off because of the complex structure and pore characteristics of clay-quartz mixtures. In this study, evaporation tests at 40 \({}^{\circ}\)C and NMR tests were performed on eight clay-quartz mixtures with different mineral compositions. The water in the mixtures was divided into two parts, according to the different decay rates of the \(T_{2}\) total signal amplitude during evaporation. A prediction model for the permeability coefficients of clay-quartz mixtures was proposed based on the TC model. We also established a method for determining the \(T_{2}\) cut-offs of mixtures by combining the NMR tests and fractal analysis. Based on the prediction model and novel method for determination of the \(T_{2}\) cut-off, a simplified method for determining the permeability coefficients of clay-quartz mixtures without centrifugal and evaporation tests was established.
## 4 Materials and Methods
### Obstacle's Danger and Risk  
The obstacle in our model is any person, animal, or object that would be considered as an obstruction in the pedestrian's thinking. Occasionally, the obstacle could be physical or abstract, such as a restricted area defined by traffic laws, for instance. The observed obstacle is defined by _spatial effect_, a term introduced by [PERSON] et al. [35]. An example of this is a group of other pedestrians walking together. Theoretically, these are considered multiple obstacles, but because planning a path through these obstacles is viewed as unnatural and even impolite, such practice is not encouraged. In our model, these obstacles would be considered as a single obstacle. In addition, because of the spatial effect, the obstacle may dynamically change its properties. An example of this is the crossroad. If the light is red, the entire crossroad would be treated as an obstacle, but if the light is green, it is no longer viewed as an obstacle from the pedestrian agent's perspective.  
For the agent's path-planning task, the most critical property of an obstacle would be its _risk_ perceived by the agent. The difference in the perceived risk of the obstacle could greatly change how the agent plan the path. For example, if the obstacle is a highly dangerous one (e.g., a deep hole on the street), the pedestrian would very likely stay further away from it, as represented in Figure 1a. On the other hand, if the obstacle is safer (e.g., a shallow water puddle), the pedestrian is less likely to avoid it too much. In certain situations, such as when the pedestrian is in a hurry, he may choose to walk over the water puddle obstacle, as presented in Figure 1b.  
The risk perceived from the obstacle could depend on many factors. As in the ISO/IEC Guide 51 in Safety Aspect [36], risk is defined as the \"combination of the probability of occurrence of harm and the severity of that harm\". For instance, the danger of a lion should be remarkably high, but if that lion is kept inside a cage, its risk should be close to 0 as the chance of the lion interacting with others is low. In pedestrian navigation, the  
Figure 1: The agent’s planned path with different obstacle’s danger: (**a**) Navigation around a deep hole; (**b**) navigation around a water puddle.  
danger from a human should be lower than a construction machine, for example. However, the risk coming from a pedestrian running at high speed, toward the pedestrian agent, should have a greater risk compared to the construction machine moving slowly on the side. Accordingly, we model our obstacle consisting of the following properties: _danger, size, direction, speed_, and _type_ of obstacle. Similarly to ISO/IEC Guide 51, the risk from the obstacle is formulated by the obstacle's harm and its probability of collision perceived by the agent, which is discussed in more detail in Section 7.  
All risk, danger level, and other obstacle's properties used in our study are perceived by only the pedestrian's cognitive system, which could be different from the actual information of the obstacle.
###### Abstract  
We propose a new type of refractive index sensing based on the transverse magnetic (TM) modes in the photonic crystal (PhC) nanobeam (NB) cavity with a horizontal air gap. The electric field of the resonant TM mode is strongly confined within the horizontal air gap present at the PhC NB cavity. In order to increase the quality (Q) factor and the sensitivity (\(S\)) of the refractive index change in the air simultaneously, the cavity structure is fully optimized. Because of the trade-off between the Q-factor and \(S\) of the TM mode in the PhC NB cavity with an air gap, there is an optimal thickness of the air gap in the dielectric slot. From the numerical simulation results, \(S\) can exceed 1000 nm/RIU with Q > 40,000. When the dielectric slot becomes thin, \(S\) could be higher than 1200 nm/RIU. For practical applications, we suggest an Si-based PhC NB cavity with a horizontal SiO\({}_{2}\) slot structure which can also provide high \(S\) with a high Q-factor after a very fine selective wet etching process. This new type of TM resonant mode in the PhC NB cavity can be an ideal platform for compact sensors in photonic integrated circuits for TM waveguide systems.  
Keywords:photonic crystals; refractive index sensing; TM mode +
Footnote †: journal: Applied Physics Letters
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 3 Routing on Wireless Sensor Networks
### Cluster-Head Choice Norms  
The following criteria are considered when picking a CH:  
While the choice of CH has a significant impact on the lifespan of WSNs, it is important to take the network's topology into account when making this decision.  
Residual energy (RE):_ Its power is crucial for the CH selection procedure. It appears that this power is all that is left in the sensor once it has completed its communication. This RE reduces the energy expenditure of the node while the CH and BS are exchanging information.