esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
In this study, the proposed UniU-Net model demonstrated exceptional performance in segmenting area palms from remote sensing images, particularly in challenging environments, where traditional methods often struggle. By incorporating an auxiliary encoder and the UAFM, UniU-Net significantly improves the ability to capture detailed morphological features of area palms and excels in preserving fine structural details along the boundaries of individual trees. This enhancement in performance is evident in the model's superiority over existing models, such as U-Net and ResUnet, as measured by both the F1 and the IoU metrics. The combination of the auxiliary encoder, UAFM, and spatial attention mechanism enhances the model's feature extraction capabilities and improves its ability to capture spatial relationships between individual trees.  
Despite these successes, several limitations must be addressed. First, the relatively small dataset used in this study limits the model's generalization ability. To improve the robustness, future work could expand the study area to include more diverse area palm distributions and morphological characteristics. Additionally, the use of long temporal sequences to observe pathological changes, such as yellowing disease, would increase the model's practical applicability for disease detection. Second, while UniU-Net performed well on the area palm dataset, its adaptability to other types of remote sensing data needs further evaluation. Optical satellite images (e.g., Landsat, Sentinel-2) provide richer spectral information that could enhance tree extraction by offering additional spectral features, while SAR data can penetrate cloud cover and vegetation canopies to capture structural information, such as tree heights. The integration of both optical and SAR data could offer more comprehensive support for tree extraction and monitoring applications.  
Moreover, recent advancements in network architectures, such as Mamba, have shown strong compatibility with long temporal and multi-modal data, which could be beneficial for future studies. To further extend the application of UniU-Net, the combination of transfer learning techniques and multi-modal data fusion could enhance the model's performance with fewer computational resources. In subsequent research, it will be valuable to explore additional attention mechanisms, as well as multi-modal data fusion techniques, to improve the accuracy and robustness of area palm extraction models, ultimately advancing their practical use in remote sensing applications.
## 6 Conclusions and Future Directions  
Existing spectral dimensionality reduction methods do not work well when the underlying data exhibits a temporal correlation, as is the case with dynamic process data. We propose using the notion of _neighborhood entropy_ to quantitatively determine the amount of information exchanged among data instances, independent of the temporal component. On the basis of this measure, we propose the Entropy-Isomap algorithm, which uses the entropy of the neighborhood to adaptively increase the neighborhood size, and thus facilitate cross-talk across different process trajectories.  
We show how the proposed methodology can be used to better understand the morphology evolution of two immiscible materials. In future, the proposed approach can be leveraged for the smart and autonomous exploration of the manufacturing design spaces in organic electronics. The notion of mixing between individual trajectories can be used to detect the underexplored parts in the design, thus helping to build more reliable manifolds and increase confidence in the understanding of the dynamic process. The reduced order representation learnt by the proposed method shows a clear ordering of the trajectories according to the process variables. At the same time, the learnt representation reveals sparsely populated regions in the manifold, motivating the need for more samples in those regions. Thus, this insight can provide guidance in terms of designing the next round  
Figure 9: The manifold of the late stage with the first 80 points per trajectory. The same manifold is color coded according to increasing \(\phi\) (**top**) and \(\chi\) (**bottom**). (Please view in color).  
of simulations that can generate data corresponding to the undersampled process configurations. More importantly, the exposed undersampled regions of the design space can be used for active learning of the dynamic processes, potentially reducing the number of required numerical experiments to discover a stable manifold. Another promising direction is the use if a Bayesian surrogate, e.g., a Gaussian process-based model [44], as well as the use of predictive variance to execute the smart exploration of the manufacturing process.  
Conceptualization, V.C., N.N., O.W. and J.Z.; methodology, V.C., N.N., O.W. and J.Z.; software, F.S.; validation, F.S. and O.W.; data curation, F.S. and O.W.; writing--original draft preparation, F.S., V.C., N.N., O.W. and J.Z.; writing--review and editing, V.C., O.W. and J.Z.; funding acquisition, V.C., N.N., O.W. and J.Z. All authors have read and agreed to the published version of the manuscript. This work has been supported by the National Science Foundation under grant OAC-1910539. The authors would like to acknowledge support provided by the Center for Computational Research at the University at Buffalo. The authors declare no conflict of interest.
## 2 Industry 4.0 Background
### Advanced Analytics  
As introduced in the previous subsections, ubiquitous Internet access, IoT, M2M communication, and cloud computing are among the enabling technologies of Industry 4.0. They are used to collect and handle huge amounts of data (big data) that can be used to achieve in-depth understanding, gain insights and perform discoveries for accurate decision-making that will guide industrial processes [7]. Wide ranges of data can be integrated for this purpose, both structured and unstructured, from historical to real-time, coming from the most diverse sources, e.g., sensors, machines, products, workers, and customers. In particular, people will be among the most valuable data sources in Industry 4.0, for example: feedback from customers will be used to improve mass customization [22] and data gathered from worker devices will be used for network collaboration [7]. In this context, crowdsensing and crowdsourcing, often referred to as mobile crowdsensing and mobile crowdsourcing, provide a new way to collect data collaboratively, by exploiting the pervasive presence of Internet-connected geolocated smart user devices, such as smartphones, smartwatches and tablets [12]. In crowdsensing [23], these devices serve as moving sensors to collect data from different places. In crowdsourcing [24], the objective is for users to complete a task, usually consisting of providing feedback. In the following, when no further differentiation is needed, MCS will be used to refer to both mobile crowdsensing and mobile crowdsourcing. Although these two novel paradigms are very powerful, as they enable the collection of massive amounts of useful local data and information in a cost-effective and scalable way, they have not been widely used in industries yet, due to the strict requirements that characterize this environment [12].
# Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution
###### Abstract  
In modern training, entertainment and education applications, behavior trees (BTs) have already become a fantastic alternative to finite state machines (FSMs) in modeling and controlling autonomous agents. However, it is expensive and inefficient to create BTs for various task scenarios manually. Thus, the genetic programming (GP) approach has been devised to evolve BTs automatically but only received limited success. The standard GP approaches to evolve BTs fail to scale up and to provide good solutions, while GP approaches with domain-specific constraints can accelerate learning but need significant knowledge engineering effort. In this paper, we propose a modified approach, named evolving BTs with hybrid constraints (EBT-HC), to improve the evolution of BTs for autonomous agents. We first propose a novel idea of dynamic constraint based on frequent sub-trees mining, which can accelerate evolution by protecting preponderant behavior sub-trees from undesired crossover. Then we introduce the existing'static' structural constraint into our dynamic constraint to form the evolving BTs with hybrid constraints. The static structure can constrain expected BT form to reduce the size of the search space, thus the hybrid constraints would lead more efficient learning and find better solutions without the loss of the domain-independence. Preliminary experiments, carried out on the Pac-Man game environment, show that the hybrid EBT-HC outperforms other approaches in facilitating the BT design by achieving better behavior performance within fewer generations. Moreover, the generated behavior models by EBT-HC are human readable and easy to be fine-tuned by domain experts.  
Behavior Trees (BTs); Genetic Programming (GP); autonomous agents; behavior modeling; tree mining +
Footnote †: journal: Applied Sciences
## 2 Model of Beam-Column Subassemblage
### Constitutive Model of Concrete and Tendons  
The PC members considered in this study are made of high-strength concrete. When the concrete strain is less than the ultimate limit \(\varepsilon_{cu}\), the stress-strain relationship is approximately linear, as shown in Figure 4[28; 60]. Therefore, the linear constitutive model shown in Figure 5a is employed for the high-strength concrete, and the elastic stiffness \(E_{c}\) is defined as the ratio of compressive strength \(\sigma_{B}\) to \(\varepsilon_{cu}\). Meanwhile, the trilinear constitutive model in Figure 5b is employed for the tendons, where the elastic limit point \((\epsilon_{te},\epsilon_{te})\) separates the elastic stage and the stiffness decreasing stage, while the yielding point \((\epsilon_{ty},\sigma_{ty})\) separates the stiffness decreasing stage and the postyielding stage where the stiffness is very small. The two points can be determined from the tensile test of tendons, where the elastic limit point and the yielding point are obtained by 0.01% and 0.2% offset methods, respectively [61]. This trilinear model can reflect the gradual degradation of tendon stiffness from the elastic stage to the postyielding stage. Based on the trilinear constitutive model, the equations for tendon tensile force \(T_{t}\) can be obtained as follows.  
\[T_{t}=\sigma_{t}A_{t}=\left\{\begin{aligned} & E_{t1}\varepsilon_{t}A_{t}& \varepsilon_{t}<\varepsilon_{te}\\ & E_{t2}(\varepsilon_{t}-\varepsilon_{te})A_{t}+\sigma_{te}A_{t}& \varepsilon_{te}\leq\varepsilon_{t}<\varepsilon_{ty}\\ &\sigma_{ty}A_{t}&\varepsilon_{t}\geq\varepsilon_{ty} \end{aligned}\right. \tag{1}\]  
Figure 4: Compressive stress–strain relations of concrete by material tests: (a) PCX01; (b) PCJ06.  
Figure 5: Constitutive model: (a) high-strength concrete; (b) tendons.
# Exploring a Multimodal Mixture-Of-YOLOs Framework for Advanced Real-Time Object Detection
## 3 Multimodal Mixture-Of-YOLOs Framework for Object Detection
### Object Detection through Multimodal Mixture-Of-YOLO  
After preprocessing, each YOLO model marked with Y-CM, Y-DM, and Y-RM is trained and optimized based on the color map, depth map, and reflectance map, respectively, so object detection proceeds independently. Each trained YOLO object detection model outputs the information of the bounding box, \(b.b_{k}=(x_{k},\ y_{k},\ w_{k},\ h_{k})\), indicating the position and size of the object included in the data, and \(CS_{k}\), where \(k=c,\ d,\ r\), indicates the detection result as a probability. \(CS_{k}\) reflects the probability that an object is classified, and the bounding box, \(b.b_{k}\), from the object detection result with high \(CS_{k}\) has a high IOU because the area overlapping the bounding box of the ground truth is widened. In general, when there is more than one object, many object detection algorithms apply a non-maximum suppression technique that excludes all bounding boxes other than that with the highest confidence score. Rather, a weighted mean is employed to extract a more accurate bounding box, which results in better convergence of detection results. The element-wise mean of geometric information of the bounding box is obtained by weighting \(CS_{k}\) of the detected objects from the three models as described in Equation (5)  
\[b.b_{f}=\left(\frac{\sum_{k}x_{k}CS_{k}}{\sum_{k}CS_{k}},\ \frac{\sum_{k}y_{k}CS_{k}}{ \sum_{k}CS_{k}},\ \frac{\sum_{k}w_{k}CS_{k}}{\sum_{k}CS_{k}},\ \frac{\sum_{k}h_{k}CS_{k}}{\sum_{k}CS_{k}}\right) \tag{5}\]  
When simply averaging the three bounding boxes without applying weights, they are fused based on the geometric information of each regardless of the ground truth. However, when the average of three bounding boxes is calculated using \(CS_{k}\) as a weight, the bounding box with a high IOU can be obtained because the IOU considers the ground truth. In addition, even if at least one of the three models of Y-CM, Y-DM, and Y-RM fails to detect an object, detection performance can be compensated by a weighted mean based on detection results from the other two models. In this way, the proposed object detection system composed of three YOLO models (Y-CM, Y-DM, Y-RM) and a weighted mean scheme is called a mixture-of-YOLOs with weighted mean defined by MYs-WM.  
Figure 7: Scaled high-resolution PCD (**a**) depth map; (**b**) reflectance map.
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
## 3 Background
### New Rule for Broadcast Data Traffic  
After thoroughly analyzing how Orchestra is implemented in Contiki-NG, we concluded that there is no rule that can accommodate only LL broadcast/multicast traffic. Since we intended to investigate whether LL broadcast/multicast was worth considering, we decided to implement a dedicated rule for this kind of traffic. It was observed that in the case of TSCH with default Orchestra settings, data packets sent via LL broadcast frequently collided with control packets used for RPL [18].  
The default operation of Orchestra was extended with a rule that accommodates only the data broadcast traffic. This new rule schedules a slotframe for LL multicast transmission containing sender-based shared slots. In other words, one broadcast-related transmitting slot and as many broadcast-related listening slots as the number of children are allocated for every node. This rule takes the particularities of the BMRF protocol into consideration. BMRF drops all broadcast messages not originating from its RPL parent to avoid receivingand forwarding the same messages several times, upstream BMRF traffic being always unicast. Therefore, we made the following alterations:  
* Receive slots are not scheduled for broadcast traffic originating from children.
* With the help of an attribute, downward broadcast is differentiated, and the desired rule is triggered.  
After running simulations with BMRF in the broadcast mode with the Orchestra default broadcast rule and the above-mentioned rule, we observed that there was an improvement of the packet delivery ratio and delay for the newly implemented rule.
# ECC: Passenger Counting in the Elevator Using Commodity WiFi
## 3 Materials and Methods
### Noise Reduction  
CSI subcarriers obtained from the wireless network cards are accompanied by additive white Gaussian noise and encounter various interference during propagation [33]. Time domain methods may cause vital high-frequency information to be lost for unstable signals. Frequency domain methods have a long transition zone, which is easy to cause distortion and loses vital signal components. Hence, the wavelet coefficient correlation de-noising algorithm (WCCDA) based on discrete wavelet transform (DWT) is adopted; it is a time-frequency analysis method in all bands. In WCCDA, by transforming the signal into the wavelet domain, the signal is divided into multiple frequency levels, called wavelet levels. The high-frequency wavelet coefficients of the signals between the adjacent levels have a strong correlation, but the noise has no such correlation [34]. Based on this, the noise mixed in the CSI can be effectively eliminated. The wavelet correlation coefficient and normalization coefficient can be calculated as:  
\[CW_{j,k}=W_{j,k}W_{j+1,k} \tag{10}\]  
\[\widetilde{W}_{j,k}=CW_{j,k}\sqrt{\frac{PW_{j}}{PCW_{j}}} \tag{11}\]  
where \(W_{j,k}\) is the high-frequency wavelet coefficient, \(CW_{j,k}\) is the correlation coefficient at point \(k\) of level \(j\), \(\widetilde{W}_{j,k}\) is the normalized correlation coefficient, \(PW_{j}=\sum\limits_{k}W_{j,k}^{2}\) represents the energy of high-frequency wavelet coefficients, and \(PCW_{j}=\sum\limits_{k}CW_{j,k}^{2}\) represents the correlation coefficient's energy.  
Then, compare \(\widetilde{W}_{j,k}\) and \(W_{j,k}\) on each level: If \(\widetilde{W}_{j,k}\)\(\geq\)\(W_{j,k}\), it is the true signal, take \(\widetilde{W}_{j,k}=W_{j,k}\), and set \(W_{j,k}=0\). If \(\widetilde{W}_{j,k}<W_{j,k}\), it is controlled by noise, leave \(W_{j,k}\), and set \(\widetilde{W}_{j,k}=0\). Finally, the signal points are retained in \(\widetilde{W}_{j,k}\) and the noise points are retained in \(W_{j,k}\). Figure 5 denoises the first three PCs in Figure 4a. WCCDA can effectively eliminate the noise in each principal component and reflect the variation of the signal. The results also showed that the CSI amplitude attenuation gradually strengthens, as the highest amplitude is present in the first PC, the next highest amplitude is present in the second PC, and so on.  
Figure 4: Sub-carrier decorrelation and PC selection.
## 3 Results and Discussion
### Unrestrained Concentric Dent-Crack Defects
#### 3.1.1 Effect of Crack Depth on Burst Pressure  
Unrestrained dent-cracks having 0.2 t deep cracks of any length inside dents less than 6% OD deep had no tangible effect on the burst pressure if the defects were formed at \(<\)0.5 \(P_{\mathrm{y}}\) denting pressure. A denting pressure of 0.5 \(P_{\mathrm{y}}\) for dents of depths \(<\) 6% OD and crack of lengths \(\leq\) 200 mm, was not consequential to operations of pipeline if crack depths were \(\leq\)0.2 t. The pipes sustained pressures higher than 10.13 MPa -the MAOP of the pipe- without bursting. Most specimens with 0.2 t deep cracks inside dents sustained very high internal pressure and plasticized significantly without crack propagation. On the other hand, all specimens with 0.5 t deep crack in the dent failed by fracture and their burst pressures decreased with crack length, for all dent depths and denting pressures. Specimens with shallow cracks tend to fail by plastic collapse, while deeply cracked specimens tend to fail by fracture [19; 24].
## 4 Different Scenario and Observation: A Real Time Case Study  
A case study was carried out relating to the barking of Rajapalayam and Kombai dog types. These dogs are used both as pet animals and as hunters. Kombai dogs have high hunting skills compared to Rajapalayam dogs, and both were found in the Theni District, Tamil Nadu. The sounds of these two dog species were considered for the case study to understand the pattern classification and to predict barking patterns based on context. The dogs were trained by their owners to exactly make a sound during different scenarios. Recorded barking sounds under a variety of scenarios were analyzed to determine the prediction level.
# Efficient High-Dimensional Kernel k-Means++ with Random Projection
## 2 Related Work
In [23] by [PERSON], [PERSON], and [PERSON], a simple two-dimensional dataset is presented, on which the seeding algorithm achieves an \(O(log\ k)\) approximation ratio with probability exponentially small in \(k\). In [24], the presented seeding algorithm works well even when more than one center is chosen in a single iteration with k-means++ parallelized, which drastically reduces the number of passes needed to obtain a good initialization for k-means. In [25], it has been shown that by mapping data points into randomized lower-dimensional subspace, the point-wise distances are preserved as well as conventional dimensionality reduction methods, such as principal component analysis (PCA). Unlike the approaches in [18; 20], our goal is to improve the computational efficiency for the kernel k-means and its initialization with k-means++ using random projection for dimensionality reduction such that all distances are computed in a much lower-dimensional space. We propose an efficient clustering method by (1) making k-means++ more efficient with random projection and (2) subsequently clustering data using kernel k-means in the projected lower-dimensional space. The intuition of the proposed method is to reduce the computational complexity by performing dimensionality reduction prior to both centroid initialization and kernel k-means. One of the advantages of the proposed method is that it is simple to code and analyze. The induced error is theoretically bound by the Johnson Lindenstrauss lemma (the JL lemma) (i.e., \(\epsilon=\sqrt{\frac{20\ log\ n}{d}}\)). Compared to the traditional dimensionality reduction approaches, such as feature selection, the features extraction we proposed would consider all the available features. This reduces the risk that relevant features might be mistakenly overlooked. Furthermore, in the efficient initialization we proposed, the user can strike a balance between accuracy and required learning time by the selection between kmFRP/kmIRP/kmBRP.