esa-sceva/satcom-chunk-collection huggingface dataset:
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## Guidelines and Recommendations
### Step 1: Define Acceptable Level of Accuracy and Choose Appropriate Metric  
As a starting point, researchers should determine the minimum level of accuracy required for their application, using the accuracy metric(s) most appropriate for answering their questions [201]. For example, if the goal of creating a categorical map is to obtain an unbiased area estimate for a particular land cover, it is essential to account for the map's commission and omission errors by adjusting the proportional area estimate of the cover type derived from the map by the proportion of that type estimated from the map reference sample [39; 40; 57]. For a continuous variable in which the absolute accuracy of the mapped variable is most important, then the mean absolute deviation from the y = x line is more informative than R\({}^{2}\)[93; 94].  
Error in the map reference data should also be factored into the selected accuracy metrics and resulting map-derived measures. Several published methods exist for categorical data (see Section 1.2.1). For continuous variables, the fit between the mapped and map reference variables can be assessed using Type 2 regression, which allows for error in the dependent (map reference) variable [202], unlike the more commonly used Type 1 regression. Determining map reference data error is critical to determining overall map accuracy. The error in these data effectively determines the upper limit of achievable map accuracy, as it is difficult (but not impossible; see [47]) to know whether a model's predictions are more accurate than its map reference data; thus if the map reference data are only 90% accurate, then the map can be at most 90% accurate. Acceptable accuracy should thus be determined relative to the accuracy of the map reference data, rather than the implicit assumption of 100%, which is widely used since map reference data are usually considered perfect [38; 39; 47; 57; 67].  
Although the above steps relate primarily to concerns about map accuracy assessment, they are essential to establishing best practices for map TD. This is firstly due to the fact that, without undertaking rigorous accuracy assessment as described above, it is not possible to assess fully how TD error impacts map accuracy. And secondly, the processes of map reference data and TD generation are often tightly intertwined and impacted by many of the same sources of error (see Sections 1.2.1 and 1.2.2). The procedures for minimizing and measuring errors in both datasets are thus often the same. Our subsequent recommendations, therefore, cover both training and map reference datasets, except where we indicate necessary distinctions.
# A Review of Medication Adherence Monitoring Technologies +
## 2 Background  
### Medication Adherence  
Medication adherence can be defined as the extent to which a person-taking medication adheres to a self-administered protocol [8; 20; 21]. In other words, medication adherence refers to the medication-intake behavior of the patient conforming to an agreed medication regimen specified by the healthcare provider with respect to timing, dosage, and frequency [22; 23]. From another point of view, non-adherence refers to the failure of taking medication as prescribed, including in-consistency, missing doses, and failing to re-fill the medication. Nonetheless, studies showed that failure to meet the medication-intake regime can result in emergence of drug resistance, accelerated progression of disease, many irrevocable health complications [23; 24], and increased mortalities [25].  
The benefits of adhering to medication regimens are many. However, for the patient, high adherence to prescribed medication leads to less health complications, more treatments' benefits, and potentially active drug effect in the case of completely treated infectious disease [22]. Another benefit is that medication adherence helps in minimizing drug wastage and reducing healthcare costs [26]. On the other side, poor medication adherence proven to come with degradation in the health of the patient that may potentially lead to substantial disability or death, especially for patients that are chronically ill [8; 27].
# ConDPC: Data Connectivity-Based Density Peak Clustering
## 4 Experiments and Results
### Experiments on Synthetic Datasets
#### 4.1.2 Results on Synthetic Datasets
Table 3 shows the clustering comparison results on the synthetic datasets. From Table 3, we can see that the traditional DPC algorithm achieves good clustering results on datasets Aggregation, Flame, and R15. On the Jain and Twocircle datasets with obvious density differences, DPC finds multiple cluster centers in the dense cluster, but fails to find cluster centers in the sparse cluster, so the clustering effect is poor. DPC can find the clustering center point on the semi-enclosed datasets Halfkernal and HalfCircle. However, when assigning neighbors to other points, the points will be assigned to the wrong neighbor because the relative location structure of the data set is not considered, which leads to the wrong cluster allocation. The proposed algorithm ConDPC has achieved good clustering results on the first three datasets. At the same time, on datasets with obvious density differences, ConDPC can find the corresponding center points in different clusters. On the datasets with enclosed/semi-enclosed distributions, ConDPC takes into account the data connectivity and preferentially  
Figure 10: Results on ThreeCircle. (**a**) ConDPC, (**b**) DPC, (**c**) K-means, (**d**) DBSCAN, (**e**) DPC-DBFN, (**f**) McDPC. searches for neighbors in the same connected group so that points can be correctly assigned to the corresponding clusters. The visualization results from Figures 3-10 and the index values in Table 3 show that the ConDPC algorithm has the highest accuracy among all comparison algorithms on the datasets.
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
###### Abstract  
The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training-testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively.  
machine learning; agricultural data analysis; decision making; Internet of Things; smart farming; zero hunger +
Footnote †: journal: Applied Sciences
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 4 QKD Applications  
While QKD systems are based on a few core principles, the large number of implementation variants drastically influences the complexity and cost of each solution. Since the application domains for QKD are numerous and heterogeneous, it is important to understand which set of protocols, systems, and devices are preferable for the different use cases, along with their different requirements. This mapping will help to clarify the correlation between technologies (protocols, systems, devices) and potential markets, and will be used in the following to elaborate considerations about the strategy the community should pursue to pierce the industrial barrier.  
Many discriminant factors may be considered for such a classification process, but we limit the discussion to three of them.  
A first classification can be made in terms of the medium into which the light travels, distinguishing between guided (fiber, waveguide) and unguided (in air) systems. This medium has a direct impact on both protocols and devices, due the large differences of the light propagation laws in different media. For example, the propagation in guided media is affected by random polarization rotation, which prevents easy implementation of encoding of polarization information. The limiting factor in air is instead the coupling of light with the detector due to the very low level of detected power and the high variability of weather conditions.  
A second classification parameter is the distance between the sender and receiver, leading to the coarse distinction between short-reach and long-reach communications. The distance limitation is defined here as the inherent limit of unamplified quantum transmission above which signal regeneration is required.  
The third factor we propose is the cost, which may range from extremely low (e.g., quantum encryption in sensors or smartphones) to extremely high for military and space applications. This also relates to the used technology--QKD systems requiring cryogenic cooling are very costly, while cheaper solutions with lower performance may be based on integrated devices used for classical optical communications.  
Table 2 maps technology features (devices, protocols, propagation medium, cost, distance) versus different application domains.
## 2 Materials and Methods
### CFD Calculations and Mesh Independency
For each porosity region, a local Cartesian coordinate system following the \(X\)-axis is established at the inertia resistance node, adjusting the main tensor to the local Cartesian coordinate system for the X and Y axes. At the inertia resistance node, the X component is defined with the inertia parameter value, while the Y and Z components are solidly defined as \(10^{3}\) kg/m\({}^{4}\). The porosity viscous resistance node is similarly defined using the appropriate viscous coefficient for other components with the same method. The Ergun equation is presented in Equation (1), and details are depicted in Appendix A.2. \[\frac{\Delta p}{L}=\frac{150\mu v(1-x)^{2}}{x^{3}D_{p}^{2}}+\frac{1.75p(1-x)v^ {2}}{x^{3}D_{p}} \tag{1}\]  
The graph shown in Figure 6 was derived using the speed-pressure loss and free-flow velocity values obtained from the fuel cell studied. These values were incorporated into the model after first calculating the geometric data based on the fuel cell's physical dimensions (\(238\times 103\times 103\) mm) and depicted in Appendix A.3. These data enabled the creation of detailed volume and area information, along with corresponding flow rate and pressure loss tables. These tables, which describe the relationship between flow rate and pressure loss in the system, were used to establish a more accurate representation of the fuel cell's flow characteristics. From these data, a second-degree polynomial equation was formulated to capture the key aspects of the fuel cell's behavior under different flow conditions. The equation incorporates two important coefficients: the coefficient of the second-degree term, which represents the inertia coefficient, and the coefficient of the first-degree term, which corresponds to the viscosity coefficient. The inclusion of inertia and viscosity resistance coefficients in the model is crucial because it allows the CFD software to account for how the porous structure of the fuel cell impacts the flow dynamics within the system [43; 44]. More precisely, the inertia resistance relates to the opposition to changes in the fluid's velocity as it moves through the porous medium, capturing the effects of inertia (or the fluid's resistance to acceleration or deceleration) in response to forces within the material. This term helps the simulation to reflect the transient behavior of the fluid in the system. Moreover, including these coefficients in the CFD setup allows for a more accurate representation of real-world behavior, where the interaction between fluid flow and the porous media significantly influences mass transfer, energy conversion efficiency, and the overall electrochemical performance of the fuel cell [45]. By simulating the effects of porosity and accurately capturing the resistance to flow, the CFD model can predict how different fuel cell designs will perform under various operating conditions. This makes  
Figure 5: PEMFC single-cell geometry. it possible to optimize the design of the fuel cell for enhanced efficiency, durability, and performance [46].
## System Specification
### SACOP Process  
In addition to the system composition, we will further explain the system process. The research object of the SACOP problem is the pallet-picking task in the shipyard AS/RS. It is easy to confuse that the concepts of \"pallet\" and \"order\" mentioned in this paper differ from the traditional OPS literature. Therefore, a brief explanation of these two concepts will be provided first. Palletized material management is an important means to support modern shipbuilding. The original meaning of a \"pallet\" is the container that carries sorted materials, and it is also used to refer to this batch of materials on the pallet in shipyard material management. This paper will continue to use this material management concept, using the \"pallet\" to refer to the set of sorted materials and using the \"order\" to refer to each specific material contained in the pallet.  
The main process of pallet picking is shown in Figure 2. Firstly, the pallet-picking tasks are assigned to each picking station, and the picking station will open several pallet-picking tasks based on its own capacity limit. After the tasks are opened, the upstream storage unit will \"spit out\" the containers to which the orders belong. The containers spit out are transported by AGVs to the picking stations to pick out. After all orders in the pallet are picked out, close the pallet-picking task, and open a new one until all pallet-picking tasks are completed.  
Figure 1: AS/RS-based picking system composition.  
The pallet-picking task consists of basic order-picking cycles. As shown in Figure 3, the operation cycle of a single order includes three stages, extracting the container, transporting the container, and returning the container, as well as two interactive nodes: handing over the container between the stacker and AGV, and AGV waiting at the picking station for the order to be picked out.  
Figure 3: Operation cycle of a single-order picking task.  
Figure 2: Main process of pallet picking in shipyard AS/RS.  
During the entire SACOP operation process, each device needs to interact with other devices for each operation, which will have a profound impact on itself and the entire system. Therefore, solving the SACOP optimization problem is extremely difficult. Due to the lack of a multi-devices collaborative scheduling solution, current shipyards adopt a picker-led mode for pallet picking in the shipyard AS/RS, which can be referred to as the traditional pallet-picking mode (TPPM), as shown in Figure 4.
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 6 Case Study
### The Dispatch under F-H Weather
Although G\({}_{1}\) and G\({}_{2}\) could at least run with minimal output at a much higher expense to satisfy the load requirement, they would be shut down if VPPs have larger capacities or there are other units with lower pollution and cost. The curves of variations of cost and emission under different AQI levels are shown in Figure 13 (the proportionality coefficient of the growth factor function of emission cost \(\alpha\) = 1.5). Figure 12: Comparison of daily dispatch results without and with AQI. Figure 13: Variations of cost and emission under different AQI levels. It can be seen in Figure 13 that the total cost increases with increasing AQI level (1-6) because the emission cost increases significantly according to Formulas (17) and (18). When the AQI level is smaller than four, G\({}_{1}\) and G\({}_{2}\) could still afford the rising emission cost. Therefore, the outputs of the units remain unchanged, and the total carbon emission and total pollution emission remain at the same level. However, when the AQI level increases from 4-6, the total carbon emission and pollution emission decrease significantly because G\({}_{1}\) and G\({}_{2}\) cannot endure a heavy emission punishment and must reduce their outputs to control emission, which allows the clean DERs inside VPPs to be fully used. The above situations are consistent with the emergency warning policies of the Chinese government. When the air condition is worse than moderate pollution (\(L_{\text{AQI}}>4\)), the Chinese government forcibly restricts the outputs of conventional thermal power units (or even shuts them down). Therefore, the method that modifies the emission cost by adding the AQI factor has a certain practical significance and research value. The curves of variations of cost and emission under different values of \(\alpha\) (the proportionality coefficient of the growth factor function of emission cost) are shown in Figure 14 (\(L_{\text{AQI}}=6\)). \(\alpha\) is an empirical value that could be drawn from the comprehensive analysis of policies, F-H condition and emission characteristics of the units. It can be seen in Figure 14 that the total cost increases with increasing \(\alpha\) according to Formulas (17) and (18). However, the total carbon emission and total pollution emission are not always decreasing. Therefore, an optimal value of \(\alpha\) must be selected to both control the total cost and reduce emissions of carbon and pollutants. The optimal value of \(\alpha\) is 1.5 (\(L_{\text{AQI}}=6\)) in Figure 14, which is adopted in this paper.
# Hybrid NOMA Protocol with Relay Adaptive AF/DF Collaboration and Its Modeling Analysis in NB-IoT
## 3 Performance Modeling Analysis of H-NOMA Protocol
### Relay-Based Adaptive Collaboration and H-NOMA Signal Transmission Process  
The signal transmission and retransmission processes are based on the positive/negative response (ACK/NACK) mechanism of ARQ, which uses the receiver (relay node or destination node) to broadcast a short-length error-free packet on a separate narrowband channel to notify the eNB and relay whether it is successfully received and then proceed to the next transmission. The total transmission process of a group of signals can be divided into two phases as follows.  
Figure 2: Five transmission cases.
# A Review of Convolutional Neural Network Applied to Fruit Image Processing
## 2 Preliminaries
Moreover, they highlight the fact that several classification approaches have been proposed for quality assessment and automatic harvesting, but these techniques are limited to a few classes and small datasets. Besides, their paper identifies three main groups of classification applications of fruits and vegetables: quality assessment, automatic harvesting, and supermarket inventory. Similarly, to the one mentioned above, it only identifies two articles where CNNs are applied. On the other hand, [PERSON] et al. [38] recently reviewed non-destructive optical techniques applied to berries quality control (e.g., strawberry and blueberry). They analyze different data acquisition techniques, such as computer vision system, Vis-NIR spectroscopy, laser-induced method, and thermal, multispectral and hyperspectral devices. Besides, the authors examine the latest analysis techniques like photoacoustics, odor images, X-rays, micro-destructive tests, terahertz spectroscopy, and intelligent analyzer based on mobile terminals. However, they do not perform an analysis of the algorithms or methods involved to process the obtained datasets. They present a table that summarizes more than 45 papers (from 2011-2019) and only two articles are based on CNN. Notwithstanding all the considerable attention that CNNs have gained, fruits studies applying CNNs had not extended until the end of 2018 ([29; 33; 34; 35; 37; 38]), although there is a great diversity of fruit study areas. For the presented review, the searching strategy was designed as follows. First, we limited the years range of the search from 2015 to the present, due to the novelty of the use of CNNs in the studied area. Secondly, the main search keywords were \"Fruits\" and \"Convolutional Neural Network\", as well as the possible combinations of them with other auxiliary keywords such as \"deep learning\", \"classification\", \"quality\", and \"detection\". The search was performed on the well-known scientific databases Web of Science and SCOPUS, as well as through direct searches on the very important publishing companies, such as MDPI, IEEE Xplore Digital Library, and arXiv. Finally, the results were separated into the three studied groups of applications that are classification, quality control, and detection for automatic harvesting. These three groups cover almost the entire fruit treatment chain from harvest to final consumer. We observed that by the end of 2019 this type of study dedicated to fruits follows the general trend of CNNs application. Figure 1 shows the relationship of the articles (by groups and total) that use a CNN for fruit image processing.