esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 6 Results
### Example of Classification of One Application with the Proposed Taxonomy
It is composed of an effective algorithm that benefits from sensor data and provides a wide scope for discovering patterns and trends. Real-time data from sensors provides the relevant information to actuators (staff of the store) to immediately solve problems like products being out of stock on the shelves. Other sensors like video cameras process video streams locally to provide the analysis of product availability on the shelves. The information is confirmed by other sensors (light sensors, infra-red, and RFID sensors), and the data and metadata are sent to the cloud to be processed. Other relevant information, such as weather data, local events and commemorative dates, and promotion details, are analyzed and combined with the current on-shelf availability of products to provide demand forecasting and model buyer behavior [114]. In the cloud servers, real-time data are processed and combined with models from learning systems, data obtained from enterprise Point of Sale systems, and inventory systems to recommend action plans to maintain the on-shelf availability of products. The store staff is informed, and action is taken to restock products and rectify business processes for quality improvement. While shopping, customers can take their pets to the pet store for bathing and grooming, and they can use their mobile devices to monitor their pets via a sensor on the animal's collar. The classification of the on-shelf availability application using the proposed IoE taxonomy highlighted the interdependencies between knowledge characteristics, sensors, and observations, considering the capabilities of available resources and expected outcomes for a new buying experience, such as insights for customers and business process improvement for suppliers. With these elements, we defined a common vocabulary that can uncover existing and forthcoming application characteristics. The taxonomy has the quality attribute of being concise and having a limited number of dimensions, restricted to what is relevant and understandable. Moreover, it is explanatory enough to provide useful explanations and valuable descriptions of the nature of the exemplified application selected.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Cell Line** & **CSEO** & **NAEO** & **Vinblastine** & **Doxorubicin** & **Combrestatin A4** & **MMAE (*)** \\ \hline A2780 & 0.14 \(\pm\) 0.03 & 0.62 \(\pm\) 0.09 & - & - & - & 0.45 \(\pm\) 0.01 \\ \hline A549 & 0.49 \(\pm\) 0.23 & 0.07 \(\pm\) 0.01 & - & 56.60 \(\pm\) 0.84 & 20.00 \(\pm\) 0.10 & 0.46 \(\pm\) 0.05 \\ \hline HCT116 & 0.65 \(\pm\) 0.03 & 0.11 \(\pm\) 0.01 & 35.00 \(\pm\) 0.84 & - & 2.00 \(\pm\) 0.10 & 2.07 \(\pm\) 0.02 \\ \hline HEK93 & 0.19 \(\pm\) 0.05 & 0.83 \(\pm\) 0.11 & - & - & - & - \\ \hline JIMT-T1 & 1.50 \(\pm\) 0.30 & 2.07 \(\pm\) 0.20 & - & - & - & - \\ \hline K562 & 0.99 \(\pm\) 0.01 & 1.00 \(\pm\) 0.01 & 20.00 \(\pm\) 0.12 & - & 5.00 \(\pm\) 0.30 & 3.12 \(\pm\) 0.20 \\ \hline MIA-Paca2 & 0.55 \(\pm\) 0.02 & 0.86 \(\pm\) 0.01 & - & - & - & 4.36 \(\pm\) 0.20 \\ \hline MRC-5 & 0.83 \(\pm\) 0.09 & 0.09 \(\pm\) 0.01 & - & 39.88 \(\pm\) 1.22 & - & - \\ \hline NCI-N87 & 3.26 \(\pm\) 1.52 & 0.90 \(\pm\) 0.27 & - & - & - & 1.65 \(\pm\) 0.07 \\ \hline PC3 & 0.29 \(\pm\) 0.01 & 0.80 \(\pm\) 0.07 & - & 2.09 \(\pm\) 0.03 & & 0.36 \(\pm\) 0.03 \\ \hline RT4 & 4.75 \(\pm\) 1.24 & 0.53 \(\pm\) 0.01 & - & 36.29 \(\pm\) 1.20 & - & 0.50 \(\pm\) 0.01 \\ \hline U2 OS & 0.24 \(\pm\) 0.02 & 0.67\(\pm\) 0.05 & - & - & - & - \\ \hline U87-MG & 0.59 \(\pm\) 0.09 & 1.13 \(\pm\) 0.22 & 2.00 \(\pm\) 0.04 & 99.61 \(\pm\) 2.34 & 9.00 \(\pm\) 0.50 & 0.21 \(\pm\) 0.03 \\ \hline \end{tabular} (*) MMAE: Monomethyl Aurisattin E.  
\end{table} 
Table 5: IC\({}_{50}\) (\(\mu\)g/mL) values for the evaluation of CSEO and NAEO in the cytotoxicity test against the cancer lines.
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 2 Related Work
The context-conditioned motion models implicitly incorporates complex inter-object correlations while remaining tractable using cost flow networks. [PERSON] et al. [33] also took image-appearances into account based on KSP tracking. These improved methods can preserve identities better than previous approaches over long sequences, but they perform badly when most of the appearance cues or motion cues cannot be available. This is very common in team sports because of the serious frequent occlusions. In order to accurately track multiple people with sporadic appearance features, [PERSON] et al. (2018) firstly proposed a graph-based framework to connect the detections across time based on their position and partial appearance estimates. It proceeds with hypothesis testing in an iterative framework which considers the input data at different time scales. Its main disadvantage is that it is greedy, and consequently there is no guarantee of a global optimal solution. Afterwards, they adopted graph-based label propagation framework ([PERSON] et al., 2018), in which a number of distinct graphs are constructed in order to capture the spatio-temporal exclusivity constraint, and the appearance information. The effectiveness of the proposed approach has been demonstrated with several challenging datasets, but one limitation is the scalability of the method with respect to the number of nodes. Other relative tracking approaches towards multiple players in team sports, as reported by a recent review ([PERSON] et al., 2018), include using bipartite matching ([PERSON] et al., 2018) or the attribute matching algorithm ([PERSON] et al., 2018) to associate detections in consecutive frames, extracting dense clusters ([PERSON] et al., 2018) of tracked players hypotheses to track, and combining multiple local trackers to a global tracker ([PERSON] et al., 2018). Recently, MOT problems addressed by correlation filters ([PERSON] et al., 2018) and deep network flow ([PERSON] et al., 2018) were also reported. For example, [PERSON] et al. (2018) have exploited the Markov decision process to integrate the discriminative correlation filters-based (DCFB) tracking method into the MOT framework. Although the proposed method outperforms the state-of-the-art algorithms in many surveillance scenarios, it is yet to be explored in our team sports field. In this paper we focus on how to further improve KSP tracking by taking appearance features into consideration. Moreover, we introduce a similarity metric between two players to measure their matched probability. The similarity can be computed based on appearance features extracted from multiple views. However, as the occlusions are very serious in basketball videos, some identity features can not be recognized in many frames. In this regard, we carried out extensive experiments to quantify the requirements of appearance features in our KSP-based tracking.
## 2 Materials and Methods
### Models Training and Test  
Due to the high number of correlated variables in the training dataset, the potential challenges of the curse of dimensionality and multicollinearity arise, impacting the interpretability and stability of traditional models. The selection of the RF algorithm [76] is well founded, as it is widely supported by a substantial body of literature. Numerous studies have demonstrated its efficacy in satellite data analysis and crop classification tasks [77; 78; 79] especially with a fused dataset [80]. Random Forest is particularly well suited for this study due to its robustness in handling high-dimensional datasets and addressing multicollinearity challenges. The ensemble nature of RF, which combines multiple decision trees through bagging, allows it to effectively manage a large number of variables. By employing different subsets of training data and features in each tree, RF mitigates the risk of overfitting, ensuring both high accuracy and good model generalization [81]. Moreover, RF excels at handling large volumes of data with linear and nonlinear structures as frequently encountered in satellite data [48], providing interpretable models and the explainability of variables through specific metrics [82; 83]. The RF algorithm's capability to handle high-dimensional datasets and correlated features makes it an optimal choice for our analysis, striking a balance between accuracy and computational efficiency. The methodology employed (Figure 6) involves using multiple RF classifiers, each trained at a specific hierarchical layer, to achieve accurate separation of hazelnut areas. For training the classifiers, optimal hyperparameters were searched using grid search and 10-fold cross-validation to maximize model performance at each step. At each step of the classification process, accuracy metrics such as overall accuracy, precision, recall, and F1-score were assessed to evaluate the model's performance. These metrics were calculated both in cross-validation and on the separate 20% test dataset, to provide a comprehensive evaluation of the models' performance using different sets of unseen data.  
The trained models were applied to classify the entire reference dataset hierarchically, isolating a layer with only HZ instances. The resulting HZ classification layer underwent sieving (maximum 10 pixels, 4-connectivity) to reduce salt-and-pepper effects. Once the final raster classification was generated, the shape of the classified HZ area was vectorized and analyzed.
## 4 Discussion  
In this paper, in order to solve the problem of feature drift when AUV drifts in different ocean layers after the AUV crash, a new method of AUV drift track prediction based on a modified neural network is proposed.  
* The modified neural network establishes two different correction modules of Proactive Modulation and Response Modulation by imitating two strategies of people to regulate emotional fluctuations. The prediction errors caused by feature drift when the AUV drifts to the deep ocean layer and thermocline are corrected respectively.
* The modified neural network realizes the continuity of prediction results and the ability of sustainable learning of the model to a certain extent through different activation functions, selection weights, and different modified strategies.
* The modified neural network reduces computation in the time dimension, and the model structure is simpler for more accurate and faster training and prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Type** & **Relative Error (\%)** & **MSE (10\({}^{-3}\))** & **RMSE (10\({}^{-3}\))** & **MAE (10\({}^{-3}\))** \\ \hline
\begin{tabular}{c} ECRNet \\ (Our Method) \\ \end{tabular} & 0.0667 & 3.319 & 1.822 & 1.723 \\ \hline RNN & 0.328 & 27.762 & 5.269 & 5.126 \\ \hline LSTM & 0.236 & 16.991 & 4.128 & 4.096 \\ \hline Transformer & 0.125 & 12.341 & 3.513 & 3.422 \\ \hline LightGBM & 0.269 & 19.148 & 4.625 & 4.103 \\ \hline LSTNet & 0.253 & 17.118 & 4.125 & 4.093 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction performance of different models in the thermocline.  
Figure 14: Comparison diagram of Depth between the predicted results of each model in the thermocline and the real track sequence.
###### Abstract  
This study systematically compares the metrological characteristics of single-exposure, double-exposure, and continuous-exposure holographic interferometry for micro-deformation detection. Results demonstrate that the double-exposure method achieves optimal balance across critical performance metrics through its ideal cosine fringe field modulation. This approach (1) eliminates object wave amplitude interference via dual-exposure superposition, establishing submicron linear mapping between fringe displacement and deformation amplitude; (2) introduces a fringe gradient-based direction detection algorithm resolving deformation vector ambiguity; and (3) implements an error-compensated fusion framework integrating theoretical modeling, MATLAB 2015b simulations, and experimental validation. Experiments on drilled glass samples confirm their superior performance in terms of near-ideal fringe contrast (1.0) and noise suppression (0.06). The technique significantly improves real-time capability and anti-interference robustness in micro-deformation monitoring, providing a validated solution for MEMS and material mechanics characterization.  
Keywords:holographic interferometry; double-exposure method; deformation calculation; MATLAB simulation +
Footnote †: journal: Applied sciences
## 2 Radio Communication Technologies
### The Main Characteristics of LPWAN Technologies
#### 2.2.2 Deployment Bandwidth, Scalability, and Coverage
However, NB-IoT is ahead of both Sigfox and LoRaWAN, allowing over 100 k devices per base station in comparison to 50 k for Sigfox and LoRaWAN. NB-IoT also offers the maximum payload length and up to 1600 bytes of data transmission. This number for LoRaWAN is 243 bytes at maximum, while Sigfox is the lowest at 12 bytes, constraining its usage in various IoT applications where large data transmission is needed [10].
## 3.Arf
### Routing Recovery
Other child nodes of nodes 1 and 2 (nodes 4, 6, 7) in the routing tree also become orphan nodes, and thus routing control messages with orphan flags from these child nodes are transmitted quickly. The child nodes of orphan nodes (nodes 10-15) also become an orphan node and thus transmit a routing control message with orphan flag. If a routing control message with an orphan flag is received by a sensor node with a proper parent node at the boundary of the area affected by the failure, the sensor node shortens its transmission interval (Figure 4C). As routing control messages with proper routing tree information are propagated through the failure affected area, orphan nodes are able to recover the routing tree (Figure 4D). Figure 3: Routing Recovery in ARF. As shown in Figure 4, even if only a few nodes (just 2 nodes in Figure 4) fail, numerous child nodes are affected. Sensor nodes at a higher level of the routing tree affect a larger area when they fail (as shown in Figure 5A). In this case, even if routing control messages are transferred quickly from the area with the destroyed routing tree during routing recovery, the routing recovery speed would not be very great. Moreover, the increased number of routing control messages might slow down the recovery speed due to routing control message collisions. To reduce the overhead, ARF restricts the transmission of routing control messages with orphan flags by using short intervals with a predefined count. Therefore, the transmission interval in an area with a destroyed routing tree is set to a short value only while routing control messages with orphan flags are propagated (as shown in Figure 5B). If routing control messages arrive at the boundary area, sensor nodes with proper parents begin to transmit routing control messages with short intervals, and do so until they stop receiving routing packets with orphan flags. Given routing control messages with proper parent nodes, orphan nodes near the boundary could recover from the destroyed routing tree. The boundary moves further toward the center of the affected area, and therefore, the routing tree is reconstructed from the boundary area to the center of the failure affected area (Figure 5C). Figure 4: Example of routing recovery. The running time of our ARF algorithm is influenced by finding Parent node and implementation of the routing tree. The initialization of fields takes O (V) time where V is the number of average number of neighbor sensor nodes of a sensor node. The operation of computing the link quality takes O (lgV) time. The weight assignment operation takes O (1) time and in fully connected network a node has at most \(\left|\mathrm{V}-\ \ 1\right|\) edges. The finding parent candidate Set operation in get Parent procedure takes O (lgV) time. As a node can have maximum V - 1 edges, the loop in get parent runs V - 1 times in a worst case. So the worst case running time of get Parent procedure is O (V lgV). The loop in Routing Tree Reconstruction is executed \(\left|\mathrm{V}\ \right|\) times and these make a total of O (V 2 lgV) running our ARF algorithm.
## 2 Materials and Methods
### Determination of Volatile Flavor Compound  
The volatile flavor compounds were quantitatively analyzed using headspace solid-phase microextraction, coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS) (5977B GC/MSD, Agilent Technologies Co., Santa Clara, CA, USA). Specifically, a 75 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA) was utilized for solid-phase microextraction to capture the volatile compounds present in the headspace. Following a 40 min equilibrium period at 60 \({}^{\circ}\)C in a water bath, the extracted volatile compounds were then analyzed by the GC-MS system, which was equipped with a GCMS-QP2010 gas chromatograph mass spectrometer (Shimadzu Corp., Kyoto, Japan). The system utilized a DB-Wax column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m). The initial temperature was kept at 40 \({}^{\circ}\)C for 4 min, then increased to 245 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\), and kept at that temperature for 5 min. A preliminary identification of the compounds was accomplished through a comparative analysis of their fragmentation signatures, using electron ionization mass spectrometry, against those recorded in the National Institute of Standards and Technology database. Further confirmation of these identifications was achieved by matching their linear retention indices. Finally, the relative concentrations of the detected volatile compounds were determined using the relative peak area method.
## 1 Introduction
A wider open area in a straight-slotted plate developed a smaller velocity head. The distributor 45\({}^{\circ}\) slotted plate exhibited a higher pressure drop than the 90\({}^{\circ}\) slotted plate due to the longer path length of the slots [29]. In a fluidized bed gasifier, to check the hydrodynamics of gas-solid mixtures and particles--gas and particle-particle contact mechanisms, four different perforated plate distributors with rectangular and triangular arrangements with different holes and pitches were studied. As a result of the bubble formation, pressure drop and superficial velocity, the type D distributor, i.e., triangular, was the best distributor among the other types [30]. All the discussion above is related to geometric as well as operational parameters and there is a need to introduce a novel distributor plate that not only enhances the lower portion mixing phenomena by swirling pattern but also optimizes the pressure drop and minimum fluidization velocity as well for a range of operational parameters to improve gasifier operational efficiency. It is frequently perceived in fluidized bed literature that the distributor pressure drop may be relatively high for uniform fluidization due to numerous smaller holes. However, such a configuration might cause lower portion agglomeration and poor lateral dispersion of solid particles. Therefore, there is a need to design an optimum configuration of the distributor plate, which provides the axial flow of solids for uniform fluidization and assists in lateral particle movement resulting in swirling flow. [PERSON] et al. performed CFD simulations to predict the reaction characteristics, heat and mass transfer within a spouted fluidized bed gasifier. They revealed the findings that with increasing inlet cross-sectional area to reactor area (Am/A), gas species (CO, H\({}_{2}\), and CH\({}_{4}\)) and the lower heating value (LHV) is gradually rising [31]. In this paper, geometric and operational parameters have been studied experimentally and computationally to depict the hydrodynamics of bubbling fluidized bed gasifiers. Firstly, different distributor plates have been tested experimentally with the variations of operational parameters to observe the output fluidization parameters, i.e., bed pressure drop, bed height rise ratio, minimum fluidization velocity (U\({}_{\mathrm{mf}}\)) and bubble rise velocity ratio for each distributor plate. Afterward, CFD analysis was also carried out to explore the inside flow dynamics of gas-solid flow patterns using different distributor plates. It was found through a comparative study that among all three types of distributor plates, the novel hybrid distributor plate is found to be the most optimum distributor plate employed for a vast range of operational parameters due to its lowest pressure drop, intermediate minimum fluidization velocity and combination of air bubble flow pattern such as axial as well as radial within shallow and deep beds for enhance mixing of particles.
## 3 Experimental Results
### Experiment of the Dynamic Stack Workpiece Feeding System  
Figure 17 shows schematic diagram of dynamic workpiece feeding. When the signal starts to be transmitted and the pose estimation program is activated and completes the positioning of the object, the robot and the conveyor will move simultaneously so that while the conveyor is still moving, the robot will successfully pick up the object. The linear feed rate of the robot arm was set at 0.03 m/s. The distance between gripping position and scanning position was set at 0.5 m, and between gripping position and robot home position was set to 0.3 m. The dynamic task was accomplished by applying Equation (27) related to the velocity of the conveyor and the feed rate of the robot arm. Therefore, the velocity of the conveyor will be calculated as in Equation (29):  
\[v_{conveyor}=\frac{FL\times\big{|}P_{scan}-P_{gripping}\big{|}}{\big{|}P_{robot}-P_ {gripping}\big{|}}=\frac{0.3\times 0.5}{0.3}=0.5\;\text{m/s} \tag{29}\]  
After the camera finishes forming the point cloud, the program will predict the 6-DoF pose of the object based on that point cloud. If there is an object, the program will predict the location of the object, but if the object does not appear, the program will stop here. The position of the object will then be added with the conveyor's travel distance, then transmitted over the Ethernet connection, and the robot arm will begin to pick up the object to the stamping machine. The rest of the task will be the stamping machine's responsibility, and the pick-placing task is completed. The loop will continue if there are still objects to stamp.  
There were two experiments--the first with a single object and the second with a stack of two objects. Figures 18 and 19 show cases where the robot successfully picked up the object to stamping die with single and piled objects, respectively. Figures 18a and 19a show the workpieces that were placed in random positions, Figures 18b and 19b show the  
Figure 16: Position differences of the robot before and after compensation using the non-linear correction algorithm for robot pick-place.  
Figure 19: Dynamic piled workpieces on the conveyor (**a**) in a random position, (**b**) automatically identifying the RGB point cloud scene of a top object, (**c**) estimating the object 6-DoF poses of a top object only, and (**d**) robot grasping the top object in the dynamic experiment.  
Figure 18: Dynamic single object on the conveyor (**a**) in a random position, (**b**) automatically identifying the RGB point cloud scene of the object, (**c**) estimating the object 6-DoF poses, and (**d**) the robot grasping the object in the dynamic experiment.  
The results' data are presented in Table 1. Compared with static feeding (90% and 95%), dynamic feeding will have a significantly lower success rate--down to 65% in the case of 2 objects and 70% in the case of 1 object. The sudden drop in success rate is due to hardware limitations. The conveyor travel speed is not constant due to resistance and friction caused by the spindle shaft and the belt. The program's object position estimation time will not change in this experiment.