esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Model Development
First, we used satellite data acquired on the same date as the field samples. Second, we expanded the available data by using satellite data acquired with a one- or two-day separation from the field data acquisition (e.g., a one-day  
Figure 2: Flowchart of the various steps in the development of a retrieval model. time window includes the data acquired on the same day as the field sample as well as those acquired one day before or after the field data were acquired). For the concurrent data, we had 874 field-satellite pairs, of which 87 had values above 8 \(\upmu\)g/L, with 27 pairs in the test dataset. For the one-day time window, the total number of data points was 1954, with 200 data points above 8 \(\upmu\)g/L, and a test dataset of 60 data points. We tested a range of indices for the model input, e.g., using two-band (red-edge and red bands), three-band (red and two red-edge bands), and the normalized difference chlorophyll index (NDCI) (Table 1). We also modified four indices initially derived for lower spatial resolution instruments, such as MODIS and MERIS, by selecting the closest bands in Sentinel-2 satellite (bottom four rows in Table 1). Table 1 shows that bands from blue (band 2), green (band 3), red (band 4), and red-edge (bands 5, 6 and 8A) wavelengths are particularly important within the indices used as model input. We assessed the accuracy of the optimal retrieval model using contingency tables for predictions based on both training and testing datasets. Additionally, as noted above, we also reported the confidence and prediction intervals [62] to validate the application of the best linear regression model using the randomly sampled testing dataset. To enhance the results and assess the impact of different cloud mitigation approaches, we reanalyzed select models utilizing the Cloud Score+ S2 Harmonized V1 dataset in GEE. This dataset includes two QA bands that score on a continuous scale between 0 and 1, where values closer to 1 are considered clearer observations [58]. We tested different Cloud Score+ thresholds using CSLAP 2019 data, selected the most effective threshold to apply to the Sentinel-2 L2A data based on the best model fit, and then evaluated the performance of the best estimation model using the CSLAP 2019 and 2020 testing data. We applied thresholds of 30%, 40%, 50%, and 60% to the Cloud Score+ cs band. The cs band indicates how closely a pixel matches what we would expect from an ideal clear reference [58].
## 3 Results
### Study Characteristics
Boosting algorithms, like AdaBoost and CatBoost algorithms, were used for diagnosing STI infection, predicting HIV status, clinic attendance, and interruptions in treatment. LightGBM also aided in predicting HIV infection and identifying high-risk individuals [38; 41; 45; 50; 51]. Moreover, random forest was applied for STI diagnosis, in particular HIV status prediction, and treatment outcomes,  
Figure 4: The distribution of studies according to Sub-Saharan African subregions and years of publication. such as virological failure and viral load suppression, while KNN assisted in HIV testing prediction and identifying high-risk individuals [37; 38; 46; 48; 50; 51]. Naive Bayes and SVM were used to predict STIs and HIV test results, aiding decision-making for targeted prevention and screening strategies, while SVM helped predict virological failure [37; 38; 42; 48; 51]. Specialized techniques, like the Geodetector--a statistical tool used for spatial analysis to detect and quantify the relationships between geographical factors and a dependent variable predicted HIV prevalence, computer vision analyzed HIV self-testing images, and elastic net predicted HIV status. DL algorithms are useful in classifying rapid HIV test images, and a conversational agent API supported HIV counseling and testing [39; 44; 49; 52; 56]. The performance of these models was evaluated based on predictive values, the detection of missed infections, false-positive rates, and sensitivity. Key outcomes included identifying top models, predicting high-risk individuals, and supporting targeted HIV prevention and policy development [39; 44; 46; 47; 52; 53]. The models also identified top-performing algorithms across different use cases, aiding in selecting optimal strategies for HIV/STI management. Classification of Artificial Intelligence Approaches Based on Implementation: Practical Use Versus Methodological  
All 20 reviewed studies were categorized as methodological, focusing on designing, developing, or testing AI methods in simulations or theoretical settings. None of the studies were conducted in real-world clinical settings or tested in practical implementations.
## 5 Case Studies of Dima Optimization
### Case Study of Multi-Objective Optimization  
[PERSON] et al. [33] considered a reference architecture of DIMA with 4 aircraft systems of air supply and conditioning domain to be optimized. Apart from allocation of peripherals, 14 DIMA devices, including CPMs and RDCs, are needed to install in 4 locations, that is, BAY, NOSE, MIDDLE, and TAIL. Due to cooling requirement, CPMs are hosted only in the AVIONICS BAY. RDCs can be installed in any of the 4 locations. There are 58 tasks mapped to 14 devices providing 10 resource types subject to 6 segregation constraints. The multi-objective optimization is carried out for software mapping on mass, SSC, IPC and OIC, and hardware mapping on mass and OIC.  
The authors proposed a manual reference mapping and three solutions on how devices are installed in the aircraft and tasks are allocated to the devices. The reference mapping does not belong to the Pareto front, but it is designed manually, where all devices are used and cable weight is as low as possible. Compared to the reference mapping, solution 1 is selected from hardware mapping, in which OIC is reduced by moving RDCs with critical tasks to avionics BAY, while cable weight increases slightly. Solution 2, using 9 devices, has the lowest average normalized objective in software mapping. It results in a slightly increasing cable weight, but reduced OIC by avoiding the hardly accessible TAIL installation. Solution 3 is similar to solution 2 except an additional CPM, which offers slightly lower OIC and IPC on the cost of higher mass and SSC. The optimizations were carried out on Windows 7, MATLAB Version 2011b, and CPLEX 12.4 equipped with an Intel Xeon E31270 at 3.4 GHz and 16 GB RAM. Based on the experiments reported, all 1455 solutions of Pareto front could be obtained after 279 min, while the solving time for a single BP run was just around 11.5 s. Consequently, the running time of the multi-objective optimization algorithm is orders of magnitude above that of single objective optimization. Moreover, it increases exponentially with the number of objectives. The running time could be reduced by approximate or heuristic solutions, which can calculate a Pareto front approximation in a single optimization run.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Urban Applications
#### 2.6.1 Smart Cities  
In large cities monitoring of various parameters is crucial for the optimal life of the citizens. WSNs have a huge amount of applications to provide real time data to the authorities for the optimal function of a city. Specifically, increased transportation of people creates problems and time wastage, when a large number of vehicles is heading towards common destinations. WSNs can be utilized for monitoring in order to reduce traffic, indicate car parking spots, etc. [46].  
In [47] a WSN-based intelligent car parking system is proposed. Specifically, within a car park area, each parking lot is equipped with one inexpensive sensor node, which detects the occupation or vacancy of this parking lot. A base station collects data relative to the status of all parking lots and sends periodic reports to a database. This database can be accessed by the upper layer management system in order to execute several tasks, such as vacant parking lots discovery, security supervision, automated toll, or/and statistic report creation.  
In [48], a system to measure and classify road traffic based on WSN is proposed. The nodes of this system have integrated magnetic sensors and are deployed along the road in order to provide real time traffic monitoring. This configuration has been proposed as an easy and cheap to implement alternative to inductive coils that are traditionally deployed in roads for basic traffic control tasks. The architecture adopted in the system proposed is graphically presented in Figure 17.  
In [49], a system for road traffic control near flooded tunnels is proposed. The specific system, consists of a WSN and a centralized control system. The analogue output signals of sensors, located in the interior of two underground tunnels, are converted to digital ones which are next transmitted through serial-to-Ethernet converters to an access point. Following a corresponding procedure the signals reach a programmable logic controller, which monitors the level of water in the interior of two  
Figure 16: The subcategories of the urban applications of WSNs and the types of the sensors used in them.  
underground tunnels and accordingly not only regulates the water drainage of flooded tunnels by using pumps, but also performs traffic control of vehicles approaching the entrance to the tunnels.  
A WSN based system for street lighting monitoring and control is presented in [50]. This system enables the remote control of street lighting lamps, by using Doppler sensors to allow for vehicle detection. The light intensity of the lamps is increased, to a preset level, in the presence of approaching vehicles and reduced in the absence of them.
## 4 Challenges and Considerations for Farmers
### Yield Production  
The feasibility of smart farming relies on internet connectivity and a panel to accommodate the sensor hub. Using these features, it has become easier for the farmers to monitor the process of cultivation using their smart devices. With the aid of sensors and interconnectivity, the smart farming initiative helps farmers to optimize the consumption of water, pesticide, and fertilizers to maintain the yield quality by maintaining several parameters such as soil condition, level of pesticide, temperature, and humidity. The system can be made more successful by combining the fertilization and pesticide spraying systems, all of which can be managed with an IoT application. Farmers may decide which variety of crop is compatible for cultivation with their respective farm. Accordingly, the farmers can opt for innovative technologies such as drone-based farming and various weather prediction tools, etc.  
Young aggrepreneurs involved in cultivation business can benefit from smart farming in terms of farm maintenance, irrigation, fertilization, and pesticide application. The sensors in the smart farming paradigm are tailored to report various readings, including measurements pertaining to the number of pesticides and fertilizers, irrigation pump status, humidity, and temperature. The readings also provide details pertaining to the growth timeline of the crop, as well as the crop type, i.e., when it was grown, the date it was cultivated, and how much was harvested. In agriculture, IoT is seen as a prominent and promising factor in the agricultural domain which can boost the yield in a quick manner. In addition, the smart farming paradigm can also serve as an avenue for the startup companies to invest in.
# DELER: Distributed Clustering with Local Direction Centrality and Density Measure
## 2 Preliminaries
### Fundamental Concepts  
As illustrated in Figure 4, the Direction Centrality Metric (_DCM_) measures the variance of angles between a given point and its \(k\)-nearest neighbors, referred to as the direction centrality of that point. The formal definition of _DCM_ is provided in Equation (1):  
**Definition 1**.: _Suppose the \(k\)-nearest neighbors of a data point \(p_{i}\) are denoted as \(p_{1},p_{2},\ldots,p_{k}\), arranged in a clockwise order. The DCM value, which quantifies the centrality of \(p_{i}\), is calculated using the following formula:_  
\[DCM=\frac{1}{k}\times\sum_{j=1}^{k}\left(\alpha_{j}-\frac{2\pi}{k}\right)^{2} \tag{1}\]  
_The angle \(\alpha_{j}\) is defined as the angle formed at the vertex of the data point \(p_{i}\)between the edge connecting \(p_{i}\) to the \(j\)-th neighbor \(p_{j}\) and the edge connecting \(p_{i}\) to the subsequent neighbor \(p_{j+1}\) in the clockwise direction._  
According to the _DCM_ metric, data points can be classified as either boundary points or interior points.  
**Definition 2** (Interior Points and Boundary Points).: _Given a DCM threshold \(T_{\text{DCM}}\) and any point \(p\) in the dataset \(P\), if the DDCM value of \(p\), denoted as \(DCM(p)\), satisfies \(DCM(p)\geq T_{\text{DCM}}\), then \(p\) is classified as a boundary point. Conversely, if \(DCM(p)<T_{\text{DCM}}\), \(p\) is classified as an interior point._Based on these definitions of interior and boundary points, a cluster can be intuitively understood as a group of interior points that are separated from other clusters by boundary points and other interior points.
## 3 Problem Solution
### Time-Oriented Nearest Neighbor Algorithm  
Several heuristic algorithms have been developed for obtaining solutions for the VRP. In the present paper, the Time-Oriented Nearest Neighbor (TONN) algorithm is selected for obtaining the initial solutions, due to its simplicity and speed. The algorithm works by building routes which start from the depot (Node 0) and, consequently, the \"closest\" to the last visited node unrouted customer is added. In the process of searching the \"closest\" customer, time window as well as capacity constraints must be respected; otherwise, no customer can be added. A new route is started if no customer is found, unless no more customers are left to add, meaning that all routes have been constructed and the process is completed. [PERSON] [18] proposed this method in an attempt to optimally solve the VRPTW.  
Figure 1: Pareto Optimal Front. (**a**) Initial; (**b**) Updated.  
The \"closeness\" of each customer is measured through metric \(c_{ij}\). This metric measures the direct distance between the two customers \(d_{ij}\), the time difference between the completion of service at \(i\) and the beginning of service at \(j\), \(T_{ij}\), and the urgency of delivery to customer \(j\), \(u_{ij}\).  
\[T_{ij}\ =\ \left(t_{j}\ +\ w_{j}^{k}\right)\ -\ \left(t_{i}\ +\ w_{i}^{k}\ +s_{i}\right)\]  
\[u_{ij}\ =\ \ l_{j}\ -\ \left(t_{i}\ +\ w_{i}^{k}\ +\ s_{i}\ +\ t_{ij}\right)\]  
\[c_{ij}\ =\ \ a_{1}\times d_{ij}\ +\ \ a_{2}\times T_{ij}\ +\ \ a_{3}\times u_{ij},\ a_{1}\ +\ a_{2}\ +\ a_{3}\ =\ 1\]
## 3 Experimental Implementation
### Video Classification Training Process  
The video input is fed into the convolutional neural network (CNN) to extract the features. A pre-trained CNN network, \"Googlenet\", is used to extract the feature vectors from the videos. The sequenced feature vectors from the video input are the output from the activation function on the last pooling layer of the convolution neural network \"Googlenet\". It consists of 148 layers. The layers consist of the convolution layers, max-pooling layers, and inception layers. The size of the convolution filter is kept 7-by-7, and 3-by-3, while max-pooling of 3-by-3 is used. The activation function used here is ReLU. After the convolution and max-pooling layers, the inception layers are used to stack all the processed information at the output. The add-in feature of the Googlenet is the inception layer, which deals with the convolution of multiscale input frames. The filters and the weight of the Googlenet layers are determined using the error backpropagation. The inception layers in the architecture are 1-by-1, 3-by-3, and 5-by-5 and are convoluted by the information from previous frames layers, respectively, by the application of max-pooling and the ReLU activation function. An average pooling of 7-by-7 is used instead of the max-pooling feature vectors at the end of the architecture.  
The minimum batch size for the training of sequences is kept at 16 for each iteration. The sequence length of each batch size has been measured and truncated to an optimum length, 400 in our case. The learning rate is the measure of the change in the weight while training the algorithm. The learning rate varies from 0 to 1, which is an important parameter to tune the algorithm in terms of efficiency and processing time. The processing time will increase by using a shorter learning rate. Additionally, a shorter learning rate will increase the number of epochs and iterations. The initial learning rate is kept constant at 0.0001 with the gradient threshold of 2. The forward and back pass in one iteration computes the output and loss function for that pass. The maximum number of epochs is 30, and the number of iterations per epoch is 15. After passing all data through the neural network, the input data is shuffled for the next epoch. The number of hidden units for the BiLSTM is kept at 2000 to remember the time steps. The dropout probability is set at 0.5, which will truncate the data to half. The learning rate of the algorithm has been selected based on the hit-and-trial methods. The results are shown in Table 2. The learning rate ranges between 0.1 and 0.00001 for the validation of optimum results. The performance of the algorithm is found to be optimal at a learning rate of 0.0001.
## 2 Ice Load Prediction Model
### Ice-Hull Contact Mechanism and Determination of Impact Load
#### 2.1.1 Ice Crushing  
Ice crushing is regarded to occur at the first stage during one impact event. Ship penetrates ice sheets with a constant speed. An ice sheet is unbreakable but localized fracture takes place with ice spalling and extruding. The crushing force, \(F_{C}\), indentation displacement, \(\zeta_{n}\), and contact area, \(A_{n}\), are therefore determined by the energy method.
###### Abstract  
The Underwater Acoustic Sensor Networks have gained significant attention because of their wide range of applications in submerged environments. However, ensuring reliable and energy-efficient communication in the submerged environment is challenging due to their distinctive characteristics such as limited energy resources, dynamic topology, extended propagation delays, and node mobility. Additionally, the void hole problem in submerged environments arises due to randomized node deployment. To curtail these issues, this paper introduces a novel way of strategically deploying the nodes based on the underwater depth parameters, which can reduce the likelihood of void hole occurrence. An optimal number of clusters based on the fixed transmission range of cluster heads is used to cater to extensive energy usage. In the proposed routing protocol, the path selection is based on the residual energy, link quality, and proximity to a higher number of nodes. Extensive simulations have been conducted by varying network parameters to analyze the network performance in terms of energy expenditure, packet delivery ratio, network throughput, number of dead nodes, and end-to-end delays. Also, the proposed work provides a performance comparison with some state-of-the-art protocols and exhibits promising results.  
Underwater Acoustic Sensor Network; cluster-based routing; energy conservation; void hole mitigation; strategically deployed nodes; reliable data transmission 2020 IEEEexample:BSTcontrol  
1
## 3 Results and Discussion
### Rapid Measurement System
#### 3.2.2 Filtering and Eccentric Correction  
The rapid measurement system needs to filter the measured results before subsequent program processing; the noise error generated by high-frequency reducer vibration needs to be optimized, as done in Reference [22].  
As shown in Figure 10a, all results are converted to X\({}_{0}\) relative offsets values for analysis, and the blue line indicates the X\({}_{0}\) relative offset data of the original measurement result. The orange line represents the results after they have been filtered using a Butterworth filter.  
There is considerable signal noise in the blue line, and the maximum upper limit error is 0.0887 mm, whereas the minimum lower limit error is \(-\)0.383 mm. The orange line indicates that, when the noise is eliminated, the maximum upper limit error is 0.043 mm, and the minimum lower limit error is \(-\)0.211 mm.  
Circular regression analysis is used to determine the center of the circle; the value obtained is then input into Equation (8). As shown in Figure 10b, the eccentric correction curves with and without the filter are similar; for the blue line, the maximum upper limit error is 0.037 mm, and the minimum lower limit error is \(-\)0.198 mm; and for the orange line, the maximum upper limit error is 0.036 mm, and the minimum lower limit error is \(-\)0.197 mm. This indicates that both curves are similar.  
Figure 10c shows the eccentric correction results. For the blue line, the ring gauge maximum upper limit value changes from 0.087 mm (without correction) to 0.069 mm (with correction), and the lower limit value changes from \(-\)0.383 mm (without correction) to \(-\)0.42 mm (with correction)--a total value of 0.488 mm.  
For the orange line, the ring gauge maximum upper limit value changes from 0.043 mm (without correction) to 0.026 mm (with correction), and the lower limit value changes from \(-\)0.211 mm (without correction) to \(-\)0.023 mm (with correction)--a total value of 0.049 mm. The error value is lower than the prototype error value of 0.06 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rotary Table** & **Total Data** & **Sampling** & **Sampling Interval Translates** \\
**Speed (rpm)** & **Number** & **Interval (degree)** & **to Arc Length (mm)** \\ \hline
0.25 & 48709 & 0.00739 & 0.012 \\
0.32 & 48700 & 0.00739 & 0.012 \\
2.5 & 31817 & 0.01131 & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Translation of sampling interval into arc length.
# Arc Modeling and Kurtosis Detection of Fault with Arc in Power Distribution Networks
## 1 Introduction
Then, a verification process using simulations is conducted to evaluate similarity between the mathematical arc model and arc phenomenon in experimental results. Furthermore, the fault detection method based on kurtosis detector is developed to detect the fault with arc. Additionally, power system simulations are performed to confirm the effectiveness of the proposed arc detection algorithm. The main contributions of this paper are as follows:  
* Fault with arc was experimentally implemented for arc waveform analysis. * New arc model was mathematically designed by solely using arc voltage. * Fault detector based on kurtosis detection algorithm was developed and detection performance was validated using simulation. This paper is organized as follows. Section 2 explains practical problems for open fault with arc and shows both the experimental results and waveform analysis to analyze the arc phenomenon. The design procedure of the proposed arc model is provided in Section 3. We develop a fault detection algorithm and conduct simulations to show the effectiveness of the fault detection performance in Sections 4 and 5.
## 5 Results
### Limitations of the Method  
As we can see, the proposed method works reasonably well for all the panel shapes we are likely to encounter and the indirect method is more likely to give better results in the cases where the direct method is not working well. In general, for more complex shapes, the further we get from the central assumption that near rectangular patches are being fitted to cover the surface, the more likely that this technique will not work well; however, this would be true of any other algorithm as well. The other major limitation of this technique is that it is not fully automated but requires user input. The user has to select the starting criteria properly, which can take some experience.