esa-sceva/satcom-chunk-collection huggingface dataset:
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 4 Object Model Analysis
### Grasp Stability  
When pivoting, the object contacts two EEFs of the robot (see Figure 6). In addition, the object contacts the environment, such as the table. Considering these contacts, we can evaluate the grasp stability, which is the ability of an object to balance the external wrenches \(W_{e}\) by the force vector \(f\) applied at each contact point:  
\[Gf=-W_{e}, \tag{1}\]  
where \(G\) denotes the grasp map that maps the contact forces to the total object wrench. If an object contacts the environment with a vertex, we define \(f=[f_{0}^{T}\ f_{1}^{T}f_{2}^{T}]^{T}\), where \(f_{0}\), \(f_{1}\), and \(f_{2}\) are the force vectors applied by the environment and the two EEFs, respectively. On the other hand, if an object contacts the environment with two vertices during USP, one EEF will be sufficient to balance the external wrench. In this case, \(f_{0}\) and \(f_{1}\) are the force vectors applied by the environment, and \(f_{2}\) is the force vector exerted by the EEF. This property allows us to regrasp the object in USP. Contact force \(f_{i}=[f_{oi},f_{ti},f_{ni}]^{T}\), \((i=0,1,2)\) must lie within friction cone \(FC_{i}\):  
\[FC_{i}=\Big{\{}f_{i}:\sqrt{f_{oi}^{2}+f_{ii}^{2}}\leq\mu_{i}f_{ni}\Big{\}}, \tag{2}\]  
Figure 5: Selection of grasps with respect to the surface normals of the object and the rotational vector of the EEF (e1 and e2). The contact pairs on surfaces with normal vectors r1 and r2 are removed because collision occurs between the table and EEF if the EEF contacts the bottom of the piano. The contact pairs on surfaces with normal vectors r3 and r4 are removed because they are too far to be reached by the EEFs. The contact points on the surface with normal vectors g1 and g2 remain.  
where \(\mu_{i}\) is the friction coefficient at the \(i\)-th contact point. In this study, we approximate the friction cone using a six-sided polyhedral cone. By calculating the Minkowski sum [54] of the force at each contact point, we obtain the grasp wrench space (GWS, \(W_{s}\)) [55].  
\[W_{s}=\left\{w_{s}:w_{s}=\sum_{i=1}^{3}Gf_{i}+w_{g},\ f_{i}\in FC_{i}\right\}, \tag{3}\]  
where \(w_{g}\) indicates the wrench generated by the gravitational force. The stability \(s\) can be evaluated by calculating the minimum distance between the origin in the wrench space and the convex hull of the set \(W_{s}\). In this work, if the origin is inside of the GWS, the grasp is considered stable. In the physical world, the \(s\) indicates the ability to resist external wrenches \(W_{e}\) and it can be calculated by:  
\[s=\min_{d\in\text{convex}thull(W_{s})}||d||. \tag{4}\]  
Based on the grasp stability, we select the stable grasp configurations.
# A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles +
## 4 Simulation Results
### Comparison with Baselines
Specifically, the north and south approaches always have queues, while the west and east approaches have smoother traffic flow. As a comparison, the traffic flows simulated by other considered methods are plotted in Figure 14. It can be seen that their traffic flows are balanced, as evidenced by the similar trends and values of the average queue length and queue time for each lane. Figure 12: Performance estimation of the proposed method in each lane. To further validate the control policy acquired by our proposed method, we trained a DRL controller with symmetric signal phases and timing. Specifically, each signal cycle had equal lengths for the first and third phases, while the second and fourth phases were identical. The cycle length was selected from the set {10 s, 20 s, 30 s, 40 s, 50 s, 60s}, resulting in 27 total actions. The purpose of this experiment was to see the control policy learned by the DRL algorithm with symmetric SPaT. As shown in Figure 15, the results for the symmetric policy have more noticeable periodic oscillations, indicating periodic traffic congestion. However, despite not being as good as the original DRL-based controller with flexible SPaT, it still outperformed the fix-timing controller. There is no significant difference in traffic flow between each direction for the symmetric SPaT policy; hence a direction-wise plot was not included. This comparison confirms our speculation that the original DRL controller had learned a policy with asymmetric traffic flows to optimize the traffic performance of the entire intersection.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 1 Introduction
* We design a carrier feature block attention module and a symmetry-based defective carrier detection module to effectively reduce unrecognized results and false positives of carrier detection in wastewater microscopic images. Figure 1: Examples of normal detection results of existing detection methods (green bounding boxes) in wastewater images. There are unrecognized carriers with shallow edges (red bounding boxes in (a)) and false positives caused by defective carriers (blue bounding boxes in **b**)). * We propose a carrier dataset called Carrier600 with high-accuracy annotations for evaluation. The experimental results demonstrate the effectiveness of our proposed framework and its practical applicability in real industry scenarios. The rest of this paper is organized as follows: Section 2 provides background knowledge of carriers for wastewater treatment and related work of object detection in microscopic images. Section 3 presents the framework of CarrDet and details each component. In Section 4, we introduce the dataset, experimental settings, compared SOTA methods, and experimental results. Section 5 presents the ablation study and discusses the impact of carriers similarity threshold \(\theta\). We conducted CarrDet in industry practice and show the effectiveness in Section 6. Finally, we draw conclusions in Section 7.
## 5 Conclusions and Suggestions  
In this work, a comprehensive description of Malaysia's energy use was presented by mapping energy flow Sankey diagrams and analyzing ongoing trends of main factors influencing these energy flows. According to the results, currently Malaysia is a net energy exporter, and crude oil and natural gas are the main exports. Most of the natural gas was exported in LNG form. Coal and oil products are the main imports. The vast majority of the coal supply in Malaysia is imported. The lack of refineries also has required many petrol imports to satisfy domestic needs. Coal, natural gas, oil products and hydropower are the main fuels used for electricity generation. Electricity, oil products especially petrol and diesel, natural gas and coal were the main energy forms in the end-use sectors. The shares of the industrial, transportation and domestic sectors are almost the same, accounting for approximately one-third of the energy consumption in Malaysia.  
In the past, Malaysia has successfully optimized its energy structure to ensure energy security. In 1980, Malaysia's government launched the National Diversification Policy to reduce oil product consumption in electricity generation and replace it with natural gas, coal and hydropower. The share of oil products decreased from 88% to 10% by 2000, whereas the share of natural gas increased from 1% to 75%. After 2000, the share of coal-fired increased from 10% to 47% in 2011, and the share of natural gas and oil products decreased to 39% and 7%, respectively. In 2011, hydropower contributed to 7% of electricity generation. However, except for hydropower, there is no renewable energy form that is large enough to be illustrated in the energy Sankey diagram.  
For a sustainable development in the long term, the Malaysian government has launched a series of plans to encourage the utilization of renewable energy. It is expected that by 2050 renewable energy will contribute 56% of electricity generation, whereas 43% will be contributed by large hydropower. However, the development of renewable energy has been much slower than expected. In addition, Malaysians still lack of energy conservation awareness because of the low energy prices. To address the challenge of global climate change, more emphasis must be placed on the utilization of renewable energy, green technology and energy conservation in Malaysia, and the energy subsidy for fossil fuel must be gradually reduced.  
The lack of energy data and information in Malaysia were the main difficulties in completing this study. Although the energy balance was given, more detailed data are difficult to obtain because of the lack of consistent data standards in the various departments. More attention to data statistics and data publicity is required to encourage more studies on Malaysia's energy system in the future.
# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 3 Overview of the Application of Machine Learning in Construction Productivity Publications
### Audio-based
By analyzing this data, we may be able to extract a significant amount of information about the underlying operations [83]. Signal processing and ML or DL algorithm techniques are used to identify the distinct sound patterns that every activity or piece of equipment produces after the recordings are done via microphones. An audio-based approach depends on frequency magnitude features as inputs to ML algorithms. SVM has been popular in this area due to its efficiency in handling high dimensional data and the limited number of labeled data points. For instance, [PERSON] et al. [83] processed the audio signals from equipment by applying SVM to distinguish the activities of each equipment via their sound patterns. The value of detail specificity for  
Figure 12: Advancement of techniques for vision-based CEP monitoring. productivity measurement can be minimal, despite producing high accuracies. In addition, Bayesian models are supplemented with SVM outputs to produce cycle time estimates of an accuracy exceeding 85% [12]. Evaluation of 17 ML classifiers and three DL approaches were explored to determine the suitable analytics algorithm where Random Forest achieved a high 93.16% accuracy, while DL approaches achieved results between 90% and 94% [84]. Overall, the choice between ML and DL techniques depends on the specific application, available data, and computational resources. Notably, researchers exploring this area held to the perspective of wanting to identify the ML algorithm that could perform best. This explains the fact that numerous researchers adopted and compared multiple algorithms in their case studies continuously in search of improvement. To summarize, ML-based equipment activity recognition methods have advanced significantly in recent years. In addition, a growing trend in general research toward the adaptation of DL algorithms to increase the accuracy and generalizability of these approaches can be noted. Because construction equipment models vary widely, it would be necessary to gather a substantial amount of data from many versions pertaining to every piece of equipment to ensure the correctness of the activity recognition models.
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 4 3D Bioprinting Techniques
### Laser-Based Bioprinting
#### 4.1.5 Selective Laser Sintering (SLS)
\begin{table}
\begin{tabular}{c c c c} \hline
**3D Bioprinting Techniques** & **Advantage** & **Disadvantage** & **Ref.** \\ \hline Stereolithography (SLA) & High drug loading capability, good bioprinting & It is generally unsafe. Preference is given to liquid bio-products that can only be cured with light, and the material scale is low. Raw material toxicity, difficulty removing uncovered resins, and the ability to print only fatty layers & [104, 149, 150] \\ \hline Two Photon Polymerization (TPTP) & The production of complex structures; precise control of parameters such as the number of pulses, pulse duration or pulse energy allows increasing the resolution. Also, 30 structures with micro/sub-micron properties can be produced precisely. & TPP technology is not fast in reaching mass production. Volumetric loss occurs during polymerization. & [151, 152, 153, 154] \\ \hline Digital Light Processing (DLP) & High resolution; simple, short biosignature time; better cell viability; uncured photopolymer is suitable for reuse. & Complex processing steps, high-precision instruments, material toxicity, low biolink scale, possible UV damage to DNA, difficulty printing large structures and difficulty in controlling the exact structural shape. & [104, 155] \\ \hline Hybrid Laser Printing (HLP) & Micro-size printing, complex product production, wide range of materials, high printing flexibility, low cost, control system is simple and automatic. & As you move from the corners of the printing product to the middle, the mechanical strength decreases. & [124, 156] \\ \hline Selective Laser Sintering (SLS) & High resolution, does not need a support structure, high strength, manufacturing in a short time, complex prints can be easily manufactured. & Finishing is necessary due to the roughness after printing. There are difficulties in material exchange. & [104] \\ \hline \end{tabular}
\end{table} 
Table 2: Laser-based bioprinting advantages and disadvantages.
###### Abstract  
This paper presents a numerical study to investigate the blast performance of a plate-reinforced moment-resisting connection using a large concrete filled tubular (CFT) column with dimensions of \(1000\times 1000\) mm and a thickness of \(40\) mm. A steel H-section with dimensions of \(700\times 300\times 13\times 24\) mm is used for beam. The plate-reinforced large CFT connection is analyzed numerically using a finite element code to evaluate its blast resistance. The methodology of modeling the connection is validated based on a past experimental study and verified using an alternate finite element code. Five improvised explosive devices (IED) are considered as blast loadings. The blast resistance of the proposed connection against the IED attacks is investigated based on the design criteria specified in the U.S. government document, UFC 3-340-02, and in comparison to that of a widely used through-diaphrag moment connection. Local failure modes in the vicinity of the connections are also examined. Recommendation is provided for design practice.  
Keywords:plate-reinforced moment resisting connection; large CFT column; blast loading; numerical analysis +
Footnote †: journal: Applied Sciences
## 2 Typical Categories of Game Theory
### Cooperative Game Theory
#### 2.3.1.2 Coalition Formation  
[PERSON] [85] studied a dynamic social learning model by focusing on allocations and completely abstracts from coalition formation process. [PERSON] _et al_. [86] formed the WSN coalitions on the basis of Markov-process, and proposed the concept of absorption coefficient to measure the coalitional profiles and then use NE to determine the approximate data transfer strategies of the formed coalitions. Some nodes in a WSN form a coalition by transferring data coordinately instead of transferring independently in order to reduce energy consumption. [PERSON] _et al_. [87] focused on how to select a proper transmission scheme for improving the energy efficiency. They modeled the transmission scheme selection problem as a non-transferable coalition formation game with the characteristic function based on the network lifetime. They further proposed a simple algorithm based on a merge-and-split rule and the Pareto order to form coalition groups among individual sensor nodes.  
[PERSON] and [PERSON] [49; 50; 51] used cooperative game theory as a tool to devise a distributed dynamic coalition formation algorithm in which nodes autonomously decide which coalition to join while maximizing their feasible sleep times. The sleep time allocation problem is formulated as a non-convex cooperative game and the concept of the core is exploited to solve this problem. They solved two problems: (1) What are the optimal coalition structures for localizing multiple targets with a pre-specified accuracy? (2) How can nodes dynamically form optimal coalitions to ensure that the average sleep time allocated to the nodes is maximized? The two questions are solved nicely within the framework of coalition formation in a cooperative game.  
[PERSON] _et al_. [7] classified coalitional game theory into three categories: Class I--canonical coalitional games (as a coalition comprising all nodes, it is not optimal), Class II--coalitionalformation games (as a main branch of cooperative game theory, comprises several disjoint coalitions), and Class III--coalitional graph games (Figure 2). The classification is intended to provide an application-oriented approach to coalitional game theory.  
According to the coalitional game theory, \(n\) sensor nodes form the game by a triplet G = \(<\)_N_, \(v\), _S_\(>\) characteristic form, where \(G\) is a coalitional game, \(N\) = {_1_,..., _N_} is a fixed set of players called the grand coalition (the coalition comprising all nodes, it is not optimal), \(S\) = {_S_1,..., _S_} denotes a coalitional structure and a partition of \(N\), and \(v\) is a characteristic function of coalition value which quantifies the worth of a coalition in a game. The coalitional game \(G\) seeks to form cooperative coalitions to strengthen their positions in the game. Any coalition _S_(_i_ = 1,..., _l_) represents an agreement between the players in \(S\) to act as a single entity. Nodes in each coalition share measurements to localize a particular target.
# A Photonically-Excited Leaky-Wave Antenna Array at E-Band for 1-D Beam Steering
## 3 Antenna and Photodiode Integration  
In this section, we briefly describe the photodiode parameters and structure as well as its integration with the antenna. Additional details can be found in [3]. The available chip consists of two UTC-PD on InP. They are separated 500 \(\upmu\)m, and each photodiode is integrated with a 50-\(\Omega\) G-CPW (see Figure 6). The photodiode was reported in [25] and presents a 3 dB electrical bandwidth of 110 GHz. It is optically fed by a 4 \(\upmu\)m spot-size lensed fiber as shown in Figure 6a,c. The measured UTC-PD responsivity is 0.17 mA/mW at 0 V bias.  
The chip is mounted on an Aluminum Nitride (AlN) block for mechanical support, handling, and heat dissipation. During the integration process, the AlN block is positioned parallel to the PCB in such a way that the G-CPW of the chip is aligned and at the same height as the PCB (Figure 6b). Then, both are glued to a common housing, and the UTC-PD is finally bonded to the PCB using a silver-based conductive epoxy (Figure 6c). This approach results in a thick thread. Compared to wire bonding, it reduces the self-inductance of the wire, yielding a better electrical connection. This has been evaluated by bonding two sections of G-CPW on Duroid 5880 separated by 150 \(\upmu\)m. The insertion losses (IL) obtained for ball wire bonding at 67 GHz are 1.4 dB higher than the IL using conductive epoxy.  
Figure 5: (**a**) Simulated magnitude of the reflection coefficient and cross-talk for the complete array. (**b**) Simulated E-plane (black) and H-plane (grey) cuts of the radiation pattern for broadside emission at 81 GHz.  
Figure 6: Images of the integration process: (**a**) designed transition, (**b**) AlN block and PCB aligned and glued to the housing, and (**c**) detail of thread bonding and fiber used to excite the UTC-PD.
## 4 Conclusions  
This paper applies Genetic Programming to construct system-level and component-level features for system setting recognition and prognostics. First, the use of Genetic Programming in the PHM field is reviewed, and the essential elements of GP are described. Then, three GPs with distinct fitness functions are trained using vibration signals collected from an experimental platform built in the Department of Industrial Engineering laboratory of the University of Bologna.  
The first two GPs aim to construct a feature that reveals the system setting implemented during signal acquisition. The first GP adopts a supervised ML algorithm in the fitness function, while the second adopts an unsupervised learning approach. The two approaches can be applied depending on the available training data. The supervised approach may be adopted if the training data are provided with a label indicating the system setting. Otherwise, the unsupervised approach is necessary. This latter situation is widespread among automatic machinery producers, who do not know all machinery use conditions and collect the data from their clients, who, in turn, often provide unlabelled datasets. Two traditional ML models, i.e., the Decision Tree and the k-Nearest Neighbor, are chosen to evaluate the performance of the constructed features in terms of training accuracy, training time, and model accuracy when used to predict the setting of observations collected after the training, named testing accuracy. Finally, the GP features are also evaluated against the ability to reveal a setting not included during the feature construction process. The third GP aims to construct an optimal HI for the belt degradation process. The fitness function is computed as a weighted mean of monotonicity, trendability, and prognosability.  
Future development of this work will include a multi-objective GP for HI construction, which also considers HI evaluation criteria directly correlated with the effectiveness of the prognostic results. Moreover, an anomaly detection algorithm for incipient degradation detection will be applied to define the instant in which the algorithm should begin the RUL computation in a real-time application.  
Conceptualization, F.C. and R.P.; methodology, F.C. and F.G.G.; software, R.P.; validation, A.R. and M.B.; writing--original draft preparation, F.C.; writing--review and editing, M.B. and A.R.; supervision. F.G.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to agreements with the department.  
The authors declare no conflict of interest.
## 4 Experiments and Discussion
### Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Metric**} & \multicolumn{5}{c}{**Algorithm**} \\ \cline{2-6}  & **ML-KNN**[40] & **ML-ELM-RBF**[41] & **ML-KELM**[42] & **ML-CK-ELM**[43] & **ELM-RBF-SAGA** \\ \hline Average Precision & 0.6718 & 0.6983 & 0.7021 & 0.7112 & 0.7136 \\ Coverage & 3.9417 & 4.3896 & 3.9863 & 3.9578 & 3.9286 \\ Hamming Loss & 0.0362 & 0.0332 & 0.0339 & 0.0341 & 0.0336 \\ One-Error & 0.3960 & 0.3329 & 0.3526 & 0.3418 & 0.3345 \\ Ranking Loss & 0.0787 & 0.0913 & 0.0856 & 0.0793 & 0.0785 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Running time of different models in each dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Metric**} & \multicolumn{5}{c}{**Algorithm**} \\ \cline{2-6}  & **ML-KNN**[40] & **ML-ELM-RBF**[41] & **ML-KELM**[42] & **ML-CK-ELM**[43] & **ELM-RBF-SAGA** \\ \hline Training time in Art [40] & 1.2365 & 1.1735 & 1.1967 & 1.2132 & 1.2053 \\ Testing time in Art [40] & 1.1986 & 1.1552 & 1.1624 & 1.1862 & 1.1775 \\ Training time in Business [40] & 2.4573 & 1.8521 & 1.9173 & 2.0681 & 2.1367 \\ Testing time in Business [40] & 2.1061 & 1.7647 & 1.8265 & 1.8572 & 1.9356 \\ Training time in Computer [40] & 2.8732 & 2.1369 & 2.2358 & 2.3537 & 2.2132 \\ Testing time in Computer [40] & 2.6113 & 1.9127 & 2.0526 & 2.1769 & 2.0319 \\ Training time in Yeast [40] & 1.0523 & 0.7631 & 0.8256 & 0.9572 & 0.8543 \\ Testing time in Yeast [40] & 0.8958 & 0.7239 & 0.7653 & 0.8839 & 0.7668 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Running time of different models in each dataset.