esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reference Model for Monitoring IoT WSN-Based Applications
## 4 Model Description
### Interchange Layer  
A monitoring platform is also a distributed system itself. All data managed by the information layer are spatially dispersed, and usually have been captured at different moments. They may be stored in non-volatile memory to be downloaded (or even physically transported) later. It is necessary to guarantee that the information communication between system components is correct. Issues related to data storage and communications (if exist) must be considered at this level. The Interchange layer must provide the information layer from those services required to fulfill its functions.  
This layer can offer the distributed data storage service to the upper layer, making use of one or more communication networks. Monitored data is stored in this layer in a structured way, being able to deliver the stored data when it is required by the Analyzer subsystem or by the Visualizer subsystem.  
Fortunately, communication and storage technologies have been developing its own standards in the last decades. The utilization of these standards, and future ones, relies in the definition of a sub-layer which can use the services offered by these implementations in an efficient way. In this sense, a Hardware Abstraction Sub-layer, which permits the adaptation to any of those existing standards to be applied, is required. Despite of not suggested, a WSN-MP designer may choose to implement its own non-standard network or storage technology, and integrate it with standard upper layer elementsif the appropriate abstraction sub-layer is defined. An Interchange Layer Entity must provide, at shown at Figure 4, two groups of services:  
* Network Management: this element has to deal with communication issues, such as device addressing, device discovering and routing between the different used networks.
* Storage Management: this element has to deal with distributed storage issues such as data coherence, data storing and data querying.  
Like any other element of this architecture, the definition of standard interfaces makes possible the migration from different implementations, as far as detailed services and functions are provided. In this way, many systems may be applicable, from powerful communication networks (wired or wireless networks, public or private) to the simplest, such as data storage in non-volatile memories carried manually to a data center.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate
#### 3.1.9 Surabaya  
Surabaya (7.2504\({}^{\circ}\) S, 112.7688\({}^{\circ}\) E), the second largest city in Indonesia after Jakarta, is the capital of East Java Province and has a population of 3.1 million, according to the 2020 census [16]. The city is known for its dynamic economy, encompassing a diverse range of industries, such as manufacturing, trade, and services. It is also recognized for its well  
Figure 10: LS in Greater Semarang: (**a**) LS rate in mm/year in Greater Semarang; (**b**) the coastline shift between Semarang and Demak from 1998 to 2024; and (**c**) LS time series retrieved from InSAR from 2017 to 2023 in Kendal (**c.1**), Semarang (**c.2**), and Demak (**c.3**); (**d**) Land use or situation of the region highlighted in the black box, (**a.1**) which correspond to industrial areas in the coastal area in Kendal Regency(**d.1**), (**a.2**) lowering of the housing floor level in the Northern Coast of Semarang (**d.2**), and (**a.3**) the agricultural area in Demak (**d.3**).  
planned urban infrastructure and ongoing efforts to promote environmental sustainability. Similar to most of the areas discussed in this study, Surabaya is also located on an alluvial plain area. Research on LS in Surabaya remains relatively limited, despite its accelerating rate due to urban development and environmental challenges [52; 53; 54; 55].  
The LS rate in Surabaya reaches up to 80 mm/year (Figure 10(b.1)), with the most affected area is marked by the black box (Figure 11b). Although LS in this city is considered relatively low compared to other cities, long-term impacts must be taken into account, particularly over densely populated urban areas. The LS is primarily observed in cluster housing areas, characterized by high-density development, extensive groundwater extraction, and insufficient drainage systems. The most affected areas are located within housing cluster zones, highlighted in Figure 11(b.2).
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 2 Wireless Sensor Node: An Overview
### Computation Sub-System  
The tasks of the microcontroller include the processing of the signals coming from the sensing sub-system and transceiver, and coordinating the activities of other sensor more components [14]. The major goal of the central processing unit (CPU) core is to ensure correct program execution. Thus, the CPU should have the ability to perform tasks such as accessing memories, performing computations, coordinating external devices and interrupt handling. Processors in sensor motes are capable of functioning in different operational modes such as active, idle and sleep modes as the least to mitigate unnecessary power consumption. The storage section of the mote normally comprises the flash memory and  
Figure 1: Basic architecture of a sensor node [13].  
random access memory (RAM). The flash memory stores the program code for the sensor mote and facilitates high speed sampling and flexible program updates while the RAM is utilized for the storage of sensed information and any data required for performing computations [15]. The electrically erasable programmable read-only memory (EEPROM) can be used for the storage of the information for identifying the registered sensor motes in the network [16]. Some of the motes have an embedded micro-secure digital (SD) card interface to provide the mote with a non-volatile memory for off-line data capturing. However, the process of writing to the SD card is power-demanding.
## 2 Related Work
### Channel Coding  
Some existing studies propose a variety of channel coding and forward error correction (FEC) methods to improve bit error rate (BER) and PER for wireless and optical communication. [PERSON] _et al_. [10] proposed the Hadamard codes as a way to improve the probability of bit error over block orthogonal code on a Rayleigh fading channel with a bandwidth constraint. The Hadamard code achieves better performance than the block orthogonal code does at the cost of an increase in decoding complexity. [PERSON] _et al_. [11] proposed two novel classes of optical orthogonal code (OOC) for synchronous and asynchronous incoherent optical CDMA (OCDMA). The proposed OOC scales well with the number of users. [PERSON] [12] proposed the error control coding using orthogonal codes. From bandwidth efficiency analysis, a specific length of orthogonal codes mentioned by the author provides an error correction scheme without bandwidth expansion by partitioning data into blocks at the expense of complexity. The same author [13] later proposed orthogonal on-off keying (OOOK) for free-space laser communication. A block of data is mapped into a block of bi-orthogonal code, which provides the error correction capability through a correlation process. However, these designs do not consider the sensitivity, power consumption and latency. [PERSON] _et al_. [14] explored increased interference rejection capability for channels with multiple access or multipath interference. From the results, the 64-ary orthogonal modulation shows better performance than binary phase-shift keying (BPSK) does for the turbo product code at a rate of 0.793 at PER comparison. However, the use of orthogonal modulation and turbo product code increases the implementation complexity. All of the above studies are for wireless communication in general, but are not specific to WuRs. The generality increases implementation complexity and may be impractical for WuRs.
## 3 Reliability Analysis Method Based on PSO-BPNN
### Failure Probability Calculation Process Based on the PSO-BPNN Method
These configurations are designed to balance training efficiency and effectiveness. They ensure that it converges to a satisfactory solution without incurring excessively long training times or encountering issues such as infinite loops. 5. Use the PSO algorithm to optimize neural network. The initial connection weights and thresholds of the BPNN are selected as the target parameters for optimization using the PSO algorithm. Through the iterative optimization process of the PSO algorithm, the optimal initial connection weights and thresholds are determined once the convergence condition (the maximum number of iterations, max\(gen=50\)) is satisfied. The parameters corresponding to the optimal particle are then assigned to the weight and threshold parameters within the network structure of the BPNN. Subsequently, the neural network is executed and trained to develop the optimal model. (6) Model validation. After completing the model training, the performance of the model is evaluated using the test set samples. If the goodness of fit satisfies the convergence condition (\(R^{2}\geq 99\%\)), the process is considered complete. Otherwise, the sample size \(K\) is increased, and steps 1-5 are repeated until the convergence condition is fulfilled. Finally, the network model that meets the specified requirements is obtained and output. (7) Calculate failure probability. The MCS method is employed for failure probability calculation. A total of \(N\) samples are generated for each of the random input variables \(\mathbf{X}=\{X_{1},X_{2},\ldots,X_{n}\}\). In this study, the LHS method is employed for generating random samples, as it improves sampling efficiency and ensures better coverage of the input variable distributions compared to simple random sampling. Once the samples are generated, the next step involves evaluating the limit state function for each sample. The optimal PSO-BPNN model obtained in step 5 is then used to evaluate the corresponding limit state function values \(\overline{g}(\mathbf{X})\). The trained PSO-BPNN serves as a surrogate model, significantly reducing computational costs compared to directly evaluating the finite element model. Subsequently, the failure probability estimate \(\hat{P}_{f}\) is determined using Equation (13). The algorithm flowchart is shown in Figure 2.  
This approach leverages the computational efficiency of the PSO-BPNN model and the robustness of the MCS method to provide accurate failure probability estimates. The integration of the LHS technique ensures that the stochastic space of the input variables is thoroughly explored, improving the reliability of the results. Figure 2: The flowchart of the proposed PSO-BPNN method.
## 3 Preliminaries, Models, and Definitions
### Related Models  
#### 3.2.1 Coverage Redundancy Ratio  
The coverage redundancy ratio (CRR) of the node (i.e., \(s_{i}\)) is defined as the ratio of the sensing area of the node with its neighbor node (i.e., the node within \(s_{i}\)'s maximum communication radius) to \(s_{i}\)'s sensing area [27]. CRR is formulated as follows [27]:  
\[\gamma(s_{i})=1-\prod_{j=1}^{n}\left(1-\frac{2\left[\frac{2}{3}\pi\left(R_{s} ^{3}-\frac{d^{3}(s_{i}s_{j})}{8}\right)-\frac{\pi d(s_{i}s_{j})}{2}\left(R_{s} ^{2}-\frac{d^{2}(s_{i}s_{j})}{4}\right)\right]}{\frac{4}{3}\pi R_{s}^{3}}\right) \tag{1}\]  
where \(n\) is the number of the neighbor nodes of node \(s_{i}\) and \(d(s_{i}s_{j})\) is the distance between node \(s_{i}\) and its neighbor node \(s_{j}\).
## 2 Related Studies and the Current Contribution  
### Related Studies  
In a notable work [13], deep reinforcement learning (DRL) is employed to dynamically configure RIS phase shifts, resulting in improved signal coverage, reduced interference, and enhanced spectral efficiency. Furthermore, another study [14] delves into the use of deep Q-networks (DQN) to optimize RIS-assisted massive multi-input-multi-output (MIMO) systems. The authors introduce an adaptive control mechanism that dynamically adjusts the RIS phase shifts and beamforming weights, thereby boosting the system capacity, coverage, and energy efficiency [11].  
Furthermore, in the optimization of RIS-assisted communication systems, the Twin Delayed Deep Deterministic Policy Gradient (TD3) [15] emerges as a particularly powerful and promising tool. This reinforcement learning technique, introduced as an extension of the Deep Deterministic Policy Gradient (DDPG) methodology, demonstrates significant potential in enhancing performance and adaptability in dynamic network environments.  
In a pioneering effort documented in [16], the application of Deep Deterministic Policy Gradient (DDPG) is showcased as an effective strategy to address the challenges posed by dynamic beamforming in RIS-assisted communication scenarios. The authors leverage DDPG to formulate an intelligent policy capable of making real-time adjustments to RIS phase shifts and beamforming vectors. This adaptive policy maximizes the signal quality while simultaneously minimizing interference, providing a crucial advantage in the ever-changing landscape of wireless communication.  
Expanding the spectrum of reinforcement learning techniques, Proximal Policy Optimization (PPO) takes center stage in [17], where it is harnessed to optimize resource allocation in RIS-assisted networks. By employing PPO, the authors craft an adaptive policy that dynamically allocates power, subcarriers, and RIS phase shifts. This dynamic allocation strategy aims to maximize network performance while adhering to user-specific quality of service (QoS) requirements, illustrating the versatility and effectiveness of reinforcement learning in addressing the complexities of RIS-assisted communication systems.  
Amidst various reinforcement learning techniques, TD3 emerges as a standout approach, offering distinctive advantages in optimizing RIS-assisted communication systems. Twin Delayed Deep Deterministic Policy Gradient not only inherits the strengths of DDPG but also introduces improvements that enhance its stability and sample efficiency. This makes TD3 well suited for the intricacies of dynamic network environments, paving the way for heightened performance and adaptability in RIS-assisted communication scenario.  
ios. As research in this field continues to evolve, the application of TD3 holds significant promise in pushing the boundaries of what is achievable in the optimization of intelligent communication systems.
## 4 Simulation Experiment and Analysis
### Performance Evaluation Index  
Since the measurement units of the three optimization objectives in this paper (maximum completion time, total labor cost, and total green index) are different, in order to evaluate the performance index of each algorithm, it is necessary to normalize the data of each target value. In this paper, three evaluation indexes SP, IGD, and \(\Omega\) are selected. Since the optimal Pareto frontier of the tested problem is unknown, the non-dominated solution set obtained from the operation results of the three algorithms is approximated as the optimal Pareto frontier.  
The SP index is used to measure the uniformity of the distribution of non-inferior solutions on the Pareto frontier. The calculation process is shown in Equation (14). Where \(d_{i}=min_{j}\Big{(}\Big{|}f_{1}^{i}(x)-f_{1}^{i}(x)\Big{|}+\Big{|}f_{2}^{i}( x)-f_{2}^{i}(x)\Big{|}+\Big{|}f_{3}^{i}(x)-f_{3}^{j}(x)\Big{|}\Big{)},i,j=1, \cdots,|PF|\), \(\overline{d}\) is the mean of all \(d_{i}\), and \(|PF|\) is the number of non-inferior solutions on the Pareto frontier. A smaller SP value means better uniformity of the distribution of non-inferior solutions. In particular, when the SP value is 0, the non-inferior solutions are equally uniformly distributed on the Pareto frontier.  
\[SP\triangleq\Bigg{[}\frac{1}{|PF|-1}\sum_{i=1}^{|PF|}\Big{(}\overline{d}-d_{i} \Big{)}^{2}\Bigg{]}^{1/2} \tag{14}\]  
IGD is a comprehensive index for simultaneously evaluating the diversity and frontier convergence of the obtained Pareto non-inferior solutions. The calculation is shown in Equation (15). \(|PF^{*}|\) is the number of non-inferior solutions on the optimal Pareto frontier, and \(dist(x,PF)\) is the Euclidean distance between a solution on the optimal Pareto frontier and the nearest solution on the obtained Pareto frontier. The smaller the IGD value, the better the performance of the algorithm.  
\[IGD=\frac{\sum_{x\in PF^{*}}dist(x,PF)}{|PF^{*}|} \tag{15}\]  
\(\Omega\) is the dominant index, which is the percentage of the number of non-inferior solution sets found by the algorithm falling into the optimal Pareto frontier. The calculation process is shown in Equation (16), where \(H_{k}\) represents the set of non-dominated solutions obtained by the \(k\)-th algorithm, and \(|P(S)|\) represents the number of non-dominated solutions in the set \(S\). The larger the value of \(\Omega\), the better the dominance of the Pareto frontier found by the algorithm.  
\[\Omega_{H_{k}}=\frac{\Bigg{|}P(\underset{i}{\cup}H_{i})\backslash P(\underset {i\
eq k}{\cup}H_{i})\Bigg{|}}{\Bigg{|}P(\underset{i}{\cup}H_{i})\Bigg{|}} \times 100\% \tag{16}\]
## 3 Detection of Damage Occurrence  
The first level of damage identification is to determine the damage occurrence in structures according to the global change of structural integrity. The most popular and effective method is the response-based method that directly or indirectly interprets the measured response data of the structure. Usually, the data available are the experimental data before and after damage occurred, and the occurrence of damage is determined through finding their difference. The vibration responses can be easily measured by a variety of sensors, for example, accelerometers, velocity transducer, displacement sensors, strain gauges, etc., and the measured time domain data can be converted into frequency or modal domain data by transforming techniques. Hence, the response-based damage detection methods can be grouped into three types in terms of the response data used: time domain, frequency domain, and modal domain.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 3 Materials and Methods
### Evaluation Metrics  
For any image with ground truth available, we introduced two reference-based image quality assessment (IQA) metrics, the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) [23], to evaluate image quality. They can measure the similarity and fidelity between input image and reference image. We also introduced another reference-based IQA metric LPIPS [24], which focuses on image perceptual quality. It is a learned evaluation metric based on a fine-tuned AlexNet, which has been shown to be more consistent with human perception. For images without ground truth, we conducted an image quality evaluation based on no-reference IQA metrics NIQE [25], BRISQUE [26], and PIQE [27]. All the metrics were calculated on RGB images.
## 5 Discussion  
This study aimed to develop an IoT-based system that evaluates the impact of various environmental factors in university libraries on user productivity, satisfaction, and overall work quality. The collected data were analyzed using machine learning models to determine patterns and correlations among six key environmental factors: sound, light, temperature, ventilation, crowding, and overall evaluation. Seven different machine learning models--KNNs, Random Forest, Decision Trees, SVM, Naive Bayes, Logistic Regression, and XGBoost--were trained and tested to assess their predictive accuracy and efficiency.  
The results indicate that KNNs demonstrated the highest predictive accuracy in sound (F1 = 96.14%) and temperature analysis (F1 = 98.13%), while Random Forest outperformed other models in light (F1 = 74.70%) and ventilation analysis (F1 = 90.14%). However, in crowding analysis, the best-performing model (Random Forest) only achieved an F1 score of 40.46%, suggesting that crowding prediction may require additional contextual features or alternative modeling techniques to improve accuracy.  
A key aspect of this study was the comparison between individual-sensor-based models and a combined model that integrated data from all sensors. The findings indicate that the combined model exhibited significantly superior performance across all major evaluation metrics, including the accuracy, precision, recall, and F1 score. This improvement is attributed to the inter-sensor relationship analysis, which allows the model to capture the holistic impact of environmental conditions rather than treating each variable in isolation. The combined model also showed better generalization capabilities, greater resilience to noise, and improved predictive accuracy compared to models trained on single-sensor data.  
In terms of computational efficiency, KNNs emerged as the most practical choice for real-time applications due to its high accuracy and efficient memory usage (12.332 MB), significantly lower than Random Forest (21.762 MB), despite achieving the same F1 score (0.9904). Moreover, KNNs required only 1369 s for training, while Random Forest took 7309.2 s, making KNNs the more cost-effective approach for practical implementations. Other models, such as SVM (F1 = 0.945) and Decision Trees (F1 = 0.941), demonstrated competitive performance but did not match the efficiency and reliability of KNNs and Random Forest. In contrast, XGBoost (F1 = 0.741), Naive Bayes (F1 = 0.429), and Logistic Regression (F1 = 0.493) significantly underperformed in comparison.  
Beyond machine learning performance, this study highlights the importance of a scalable and efficient computing architecture for smart campus applications. The proposed fog-cloud computing framework provides low-latency processing, bandwidth optimization, and enhanced security, ensuring seamless integration with large-scale IoT deployments. The ability to process data locally at fog nodes reduces the reliance on centralized cloud computing, addressing latency-sensitive applications such as real-time environmental adjustments.
### Market Bidding Process and Challenges for Demand-Response Aggregators  
Market bidding takes place on an online platform offered by the ISO, and the bidding strategy is often a complicated mathematical tool designed by the DR aggregator. The DR aggregator can participate in the following:  
* The emergency DR market.
* The economic DR market, which consists of the following:
* The capacity market (pre-capacity bidding).
* The energy market (DAM and spot market).
* The ancillary service market (day-ahead scheduling reserve, synchronous reserve, flexibility reserve, etc.) [137].  
Although DR aggregators can sign bilateral contracts with retailers, generators, and system operators, bidding in the energy market directly in order to maximize profit is the most common activity of DR aggregators [138; 139]. Settlements are generally determined differently by each market type and the flexibility that market type may offer. In PJM, the DR aggregator is compensated for the reduced demand in full emergency and full pre-emergency markets [69]. The same applies to the emergency energy market, which,however, is voluntarily joined by the prosumer. The DR aggregator is faced with a number of uncertainties when it comes to market bidding, such as market price uncertainty, prosumer demand uncertainty, prosumer flexibility, and competition with other market players; these are major open research topics that, if answered, will enable more efficient DR aggregator operations in future US electricity markets (Section 3.7).  
Section 3 will introduce the existing literature frameworks for DR aggregator operations and their interactions with the distributed load and marker, and this is accompanied by critical thinking on how DR aggregator operations will have to change in future US electricity markets in order to support the 2030 and 2050 goals, a topic that the existing literature falls short of covering.