esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 5 Conclusions  
This paper presents a method for identifying insulators under few-shot conditions, utilizing few-shot object detection via a feature reweighting approach. The proposed method addresses the challenges of low accuracy and high omission rates encountered when detecting insulator images with varying sizes, defects, and occlusions under few-shot conditions. In the feature extraction network part, the self-calibrated convolutional network is introduced and promoted the feature extraction ability for insulators of different sizes. The SKNet attention mechanism is embedded before the input of the reweighting module, which enhances the performance of the model by extracting the key detail meta-feature information of the support set. Finally, a multi-stage non-maximum suppression algorithm for missing values is introduced to set a penalty function for confidence. The results of multiple prediction boxes are retained to reduce the occurrence of false checks and cases. In the experiment, data enhancement and a warmup cosine annealing learning rate reduction strategy are adopted to further develop the detection ability of the model. The experimental results show that the average accuracy achieved by this method reaches up to 48.3%. This accuracy level is significantly higher, with an improvement of 8.6% compared to the original method. This substantial enhancement in accuracy greatly improves the detection and recognition ability of the network for normal and special insulators under few-shot conditions.  
Although the accuracy of this paper has been improved to some extent, it is still difficult to reach the same level as the model developed by using large-scale data training. In future work, we can try to use a deeper and more complicated network structure to further increase the accuracy of insulator identification under few-shot conditions.  
Figure 12: mAP comparison of different methods.  
**Author Contributions:** Conceptualization, J.W.; methodology, J.W. and Y.Z.; software, Y.Z.; validation, Y.Z.; formal analysis, J.W. and Y.Z.; investigation, J.W.; resources, J.W. and Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, J.W.; visualization, Y.Z.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financially supported by the Jilin Science and Technology Development Plan Project (Grant No. 20200403075 SF), Doctoral Research Start-up Fund of Northeast Electric Power University (Grant No. BSJXM-2018202).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors are grateful to the editor and reviewers for their suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Development of an Embedded Controller for a Hydraulic Walking Robot WLBOT
## 4 Embedded Controller Software Design
### Joint Control Algorithm  
#### 4.4.1 PID Control with Velocity Feedforward Compensation (VFC)  
In order to realize joint position control, we develop a PID (proportional-integral-derivative) compound controller with velocity feedforward compensation (VFC) as Figure 9 illustrates.  
The compound controller consists of a conventional PID controller and velocity feed-forward compensation (VFC). The control law is shown in Equation (9).  
\[\left\{\begin{array}{c}u_{p}=k_{p}e(t)+k_{i}\int e(t)dt+k_{d}\frac{de(t)}{dt} \\ u_{c}=k_{v}\theta_{d}\\ u=u_{p}+u_{c}\end{array}\right. \tag{9}\]  
where \(e(t)=\theta_{d}-\theta_{f}\) is the tracking error; \(\theta_{d}\) is the desired angle; \(\theta_{f}\) is the feedback angle; \(k_{p}\), \(k_{i}\), \(k_{d}\) are the proportional gain, integral gain, and derivative gain; \(\theta_{d}\) is the desired angular velocity; \(k_{v}\) is the velocity compensation gain.
## 4 Anti-Cancer Activities of Tanshinones
### Anti-Proliferation and Pro-Apoptosis
#### 4.1.4 Dihydrotanshinone I (DH-TI)  
Though the studies on DH-TI are not as many as other major tanshinones, DH-TI has been reported to exert the strongest potency and unique mechanisms on some tumor cells. [PERSON] _et al._[191] evaluated the potency of four major tanshinones (TI, TIIA, CT and DH-TI) on SPC-A-1 lung cancer cells, and found DH-TI was the most active, followed by TI, TIIA and CT. Similarly, DH-TI was the most potent out of the four tanshinones to inhibit HepG2 liver cancer cell growth. Interestingly, though all the four major tanshinones could induce ROS in HepG2 cells, DH-TI was the only one that triggered reactive oxygen species (ROS)-mediated apoptosis through activating p38 MAPK pathway, suggesting some unique signaling by DH-TI [192]. DH-TI also demonstrated stronger inhibitory effect on MCF-7 and MDA-MB-231 breast cancer cells than the same concentration of TI and CT. DH-TI at sub- and low uM levels (0.4-2 uM) significantly induced G\({}_{1}\) arrest and caspase-mediated apoptosis in these cells, in association with reduction of cyclin D\({}_{1}\), cyclin D\({}_{3}\), cyclin E, CDK4 and Bcl-xL expression, increase of P27 expression and cytochrome c release, inactivation of CDK2 and CDK4, and activation of caspase-9, caspase-7 and caspase-3 [193]. Moreover, _in vivo_ administration of DH-TI (10 mg/kg of DH-TI in DMSO, _i.p._ injection, three times per week for four weeks) retarded MDA-MB-231 xenograft tumors in nude mice [193]. DH-TI at sub- or low micromolar levels (0.4-5.4 \(\upmu\)M) could induce apoptosis in DU 145 prostate cancer cells via inducing classical endoplasmic reticulum (ER) stress, evidenced by up-regulation of ER stress markers such as GRP78 and GADD153, eIF2a, JNK and XBP1 mRNA splicers. DH-TI treatment caused significant accumulation of polyubiquitinated proteins and HIF-1a, indicating DH-TI as a potential proteasome inhibitor, which was known to induce ER stress or enhance ER stress-mediated apoptosis [194]. Moreover, co-treatment with an ER stress inhibitor (salubrinal) attenuated DH-TI-induced apoptosis, supporting a mediator role of ER stress in DH-TI-induced apoptosis [194].  
Taken together, these studies demonstrated the anti-proliferation and pro-apoptosis activities of tanshinones on various cancer cells, involving multiple targets. It is hard to judge how likely these targets and mechanisms are invoked _in vivo_ at the present because most of the reported studies did not have _in vivo_ data to corroborate the pharmacodynamic targets.
## Low Voltage Control Tool
### Control Actions Management Module  
The core of the LVC tool is the Control Actions Management Module (CAMM). The CAMM is responsible for analysing network data and for computation of set-points to apply to the network's controllable resources. Figure 3 shows a simplified flowchart of the CAMM.  
When a voltage violation is identified, the CAMM starts by evaluating the available LV controllable resources, with the objective of determining the best-suited resources to address the constraint.  
Figure 3: Flowchart of Control Actions Management Module (CAMM).  
The ranking of the resources is performed according to criteria established by the DSO, with the overall objective of minimizing the grid operation costs. The rank of each network resource is established according to the following criteria, by priority:  
* Resource type: the resource type is the highest priority criterion. In this work, priority was given to DSO-owned resources (OLTC transformers and energy storage devices) and then consumer-owned resources (flexibility from domestic clients via their HEMS, controllable microgenerators or loads).
* Electrical distance to the voltage violation node: the next criterion in the rank computation of the resource is the electrical distance to the node where the voltage violation occurred. Priority is given to resources located in the same phase and that are electrically closer to the voltage violation node.
* Available flexibility: for energy storage devices and HEMS. For storage devices, this flexibility is given by the State of Charge (SoC); for HEMS, this is the flexibility that is made available to participate in grid operation.
* Contract characteristics and curtailment time: this criteria is used a last resort. Here we can consider the nature of the contracts established with private consumers (for instance, curtailment under severe conditions), and curtailment time of DG.  
Once the best-suited resources are selected and ranked according to the aforementioned criteria, the first resource is selected and a set-point is computed, based on the electrical distance between the controllable resource's node and the node where the voltage violation occurs. The impact of the set-point on the network nodes' voltages is then evaluated recurring to an unbalanced three-phase power flow routine according to the formulation presented in Reference [33]. If the established set-point is unable to solve the constraint, the next resource in the controllable resources' list is selected and a new set-point is computed. The control action plan is closed when the computed set-points are able to solve the voltage violation or when the controllable resources list is exhausted.
## 2 Theoretical Foundation
### Markov Transition Field  
MTF is an image processing method based on the Markov transition probability matrix [21]. For a given time series \(X=\{x_{1},x_{2},\ldots,x_{i},\ldots,x_{N}\}\), the first step is to divide the time series \(X\) into \(Q\) discrete bins. Each time series value \(x_{i}\) is then quantized and mapped to its corresponding bin \(q_{i}\). The transition probabilities between these bins are calculated using a Markov chain along the time axis, resulting in a Markov state transition matrix \(W\) of size \(Q\times Q\). The equation for this is as follows:  
\[W=\left[\begin{array}{cccc}w_{11}&w_{12}&\cdots&w_{1Q}\\ w_{21}&w_{22}&\cdots&w_{2Q}\\ \vdots&\vdots&\ddots&\vdots\\ w_{Q1}&w_{Q2}&\cdots&w_{QQ}\end{array}\right] \tag{2}\]  
\[w_{ij}=p\big{\{}x_{t+1}\in q_{j}\mid x_{t}\in q_{i}\big{\}} \tag{3}\]  
In the matrix, \(w_{ij}\) represents the probability of transitioning from quantile \(q_{i}\) to quantile \(q_{j}\). Since the Markov transition matrix only reflects the overall state transition probabilities and ignores the dependency between time steps and the distribution of the time series \(X\), some temporal information is lost. To address this, the MTF is constructed by extending matrix \(W\) along the time sequence, resulting in matrix \(M\), which considers the relationship between position and temporal information. The matrix \(M\) is given by the following:  
\[M=\left[\begin{array}{cccc}m_{11}&m_{12}&\cdots&m_{1Q}\\ m_{21}&m_{22}&\cdots&m_{2Q}\\ \vdots&\vdots&\ddots&\vdots\\ m_{Q1}&m_{Q2}&\cdots&m_{QQ}\end{array}\right] \tag{4}\]  
\[m_{ij}=p\big{\{}w_{ij}\mid x_{i}\in q_{i},x_{j}\in q_{j}\big{\}} \tag{5}\]  
In the matrix, \(m_{ij}\) represents the probability of transitioning from quantile \(q_{i}\) to quantile \(q_{j}\) considering specific time steps.
# Investigation of Rock-Breaking Mechanisms Based on the Adaptive Matching Method for Drilling Loads
## 2 Methodology
### Modeling of the PDC Cutter Cutting Rock
Owing to the adaptive matching of the axial load, when the incremental displacement _disp_x_ fell below a predefined threshold, the axial load applied to the PDC cutter decreased proportionally. The reduction ratio in the adaptive method is defined as the load factor (donated as \(\alpha\)) in this study, expressed mathematically as \(F_{\text{WOB}}=\alpha\cdot F_{\text{WOB0}}\), where the \(F_{\text{WOB0}}\) indicates the initial applied axial force on the PDC cutter while simulating. Since the increment in _disp_x_ in this paper is small during each time step during simulation, there is a fluctuation in axial load within the range of 12-27.8 ms. It can also be seen that there is no displacement during that period, and the reaction force also oscillates synchronously. The work power and the mechanical specific energy (MSE) can be used to evaluate the rock breaking efficiency of the PDC cutter. In line with the monitored displacement and reaction force of the PDC cutter based on the above simulation model, the work power can be calculated according to the following formula:  
\[ \begin{array}{ll}W&=\int F_{x}\text{d}x+\int F_{y}\text{d}y+\int F_{z}\text {d}z\\ &=\sum\bigl{(}F_{xi}\Delta x_{i}+F_{yi}\Delta y_{i}+F_{zi}\Delta z_{i}\bigr{)} \end{array}  \tag{1}\]  
After simulation, the volume of rock broken can be counted in the software, and then the MSE can be obtained as follows:  
\[\text{MSE}=\frac{\int F_{x}\text{d}x+\int F_{y}\text{d}y+\int F_{z}\text{d}z }{V}=\frac{\sum\bigl{(}F_{xi}\Delta x_{i}+F_{yi}\Delta y_{i}+F_{zi}\Delta z_{ i}\bigr{)}}{V} \tag{2}\]  
where \(V\) indicates the volume of rock broken; \(F_{x}\), \(F_{y}\), and \(F_{x}\) are the detected reaction force, also named the cutting force; and dx, dy, dz, \(\Delta x\), \(\Delta y\) and \(\Delta z\) express the increment in the displacement in the \(x\), \(y\) and \(z\) directions, respectively.
## 3 Results and Discussion
### Penetration Index Results  
To determine the temperature sensitivity of bitumen, the penetration index (PI) was calculated using the penetration and softening point test results and the formulas below.  
\[\text{A}=\frac{\text{log}_{10}800-\text{log}_{10}\text{P}_{25}}{\text{T}_{\text{ SP}}-25} \tag{3}\]  
\[\text{PI}=\frac{20-500\text{A}}{1+50\text{A}} \tag{4}\]  
The P\({}_{25}\) value indicates bitumen penetration at 25 \({}^{\circ}\)C, and the T\({}_{\text{SP}}\) value represents the softening point. A PI value above 2 indicates the low-temperature sensitivity of bitumen, while that below \(-2\) indicates high-temperature sensitivity [61]. The results of the penetration index (PI) are given in Figure 8.  
As seen in Figure 8, PI values increased after aging at all levels of Cecabase RT BIO10 additive added to B 50/70 virgin bitumen. Before aging, the increase in PI value was higher in mixtures with a 0.30% additive rate, while it decreased relatively in mixtures with a 0.40% additive rate. At a 0.50% contribution rate, this decrease in PI value was greater. Cecabase RT BIO10 reduced bitumen's sensitivity to temperature, particularly after aging. Mixtures with higher PI values are more resistant to low-temperature cracking and permanent deformation [62].
## 3 Experimental Setup
### Discussion  
Figure 14 presents a comparison of training, validation, and test accuracies for all six encoding methods: 3 Phase-RGB, 3 Phase-DWT, 3 Phase-STFT, Id-Linear, Id-DWT, and Id-STFT.  
Experimental results indicate that 3 Phase-RGB and 3 Phase-DWT among the three-phase methods, and Id-STFT and Id-Linear among the single time-series methods, demonstrated an overall superior classification performance. Notably, Id-STFT maintained a consistently high accuracy across training and validation phases and matched or surpassed the generalization performance of multi-channel methods--despite using only a single time-series input.  
However, despite its high accuracy, 3 Phase-RGB exhibited persistent oscillations in validation accuracy during training. As noted in previous studies, the RGB encoding method is sensitive to input noise and simply assigning A, B, and C phases to the R, G, and B channels may inadequately capture meaningful variations in the time-frequency domain. These structural limitations are especially vulnerable to degradation when data augmentation techniques like SpecAugment are applied. Nevertheless, the 3 Phase-RGB method showed a gradual convergence in performance toward the latter stages of training, confirming that a certain level of classification accuracy can be achieved. Therefore, while this approach demonstrates the potential for practical use, it also suggests the need for structural improvements in terms of training stability.  
Among the three-phase methods, DWT demonstrated the most stable convergence and highest overall performance, with test accuracy closely matching validation accuracy--indicating strong generalization. In contrast, 3 Phase-STFT suffered a notable drop in test accuracy, suggesting that the features learned during training did not generalize well to actual fault data. This likely stems from suboptimal image generation due to non-ideal STFT parameters, such as window size and step settings.  
For the single time-series approaches, Id-STFT consistently delivered an excellent accuracy and stability across all stages, confirming its effectiveness. In contrast, Id-DWT exhibited unstable and unreliable results, likely due to the loss of critical fault features during the wavelet decomposition process.  
Interestingly, Id-Linear, despite its relatively simple encoding structure, achieved an above-average performance. Its computational efficiency and simplicity make it a promising candidate for lightweight model deployment and real-time fault diagnosis systems.  
Figure 14: Comparison of training, validation, and test accuracy of the six methods.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 4 Applications Related to the Water Cycle
### Public Health  
Among societal needs, public health is of paramount importance. The GPM Disease Initiative ([[https://pmm.nasa.gov/disease-initiative](https://pmm.nasa.gov/disease-initiative)]([https://pmm.nasa.gov/disease-initiative](https://pmm.nasa.gov/disease-initiative)), last accessed 11 September 2019) and its related vector-borne and water-related disease application campaign are an example of such new social projects. Vector-borne diseases (e.g., cholera, malaria, dengue fever, etc.), which are responsible for over 17% of all infectious diseases globally, strongly depend on the environmental conditions, water presence, and sanitation, all factors, which are often disrupted during extreme events (250,303). Moreover, the spread and incidence of this type of disease can be affected by climate patterns, as shown by Moore et al. (304) for cholera in Africa. More generally, public concerns about the health effects of climate change have the potential to accelerate research and political action in a much more effective way than simple attention to carbon dioxide emissions alone (305,306).
## 2 Methodological and Operational Framework for CDTs
### CDTs Positioning in the Supply Chain  
DTs have an important role in the manufacturing and supply chain context. In agile supply chain networks, connectivity among all factory assets and supply chain entities is of utmost importance. The RAMI industry 4.0 architecture [23] emphasizes how to connect different hierarchies of the factory from an individual asset up to connected processes, the enterprise level and the supply chain (connected enterprises). Such \"assets\" are orchestrated along the different product lifecycle phases (from development up to the product use and maintenance). Similarly, the Industrial Internet of Things Connectivity Framework is based on a hybrid interconnection in all product lifecycle phases and through on a vertical (field device up to Service system) and horizontal level (supply chain) [24]. The common denominator of both approaches is the need for modelling all production assets in a way that:  
* There is an up-to-date representation of the operational behaviour, which is fed by streamed data from sensing devices or other supporting measurement systems.
* All possible interactions with other production assets are known thus creating a virtual representation of the factory/supply chain as a dynamic system.
* Combining the behaviour and the networking of such asset we can monitor, simulate and optimize its performance.  
Already large enterprises have adopted the concept of DTs in different models and applications [20,25]. Furthermore, different DT scenarios are introduced in the literature [19,26,27].  
The importance of CDTs in the supply chain is that through their cognition capabilities we have a holistic view of the whole context, relating entities and processes at various levels of abstraction, and providing means to learn from them and their interactions. This is essential, since we go beyond the typical boundaries of a physical object and through this interaction concept, we are able to have a holistic approach of event detection, simulation, impact assessment and optimization (actuation) in complex supply chain networks.  
In the next subsections we will elaborate on the CDT characteristics and operation in supply chains. We will define the main CDT enablers (characteristics) and a reference operational model. At the end, we will provide some indicative application scenarios and will elaborate more on a circular supply chain case, where stakeholders are interacting in an attempt to optimize waste flows.
## 2 Literature Review
### Hierarchical Location-Allocation Problems  
Researchers have developed location-allocation models for hierarchical facilities. Hierarchical facilities are a type of facility that consist of multi-level facilities, facilities at each level that provide (totally or partially) different functions of service within different territories [36; 40]. A healthcare facility is a typical type of hierarchical facility [32].  
From a modelling perspective, hierarchical facilities can be classified according to their flow patterns, service varieties, spatial configurations, and optimization objectives [36; 37]. Flow pattern is about the organization and delivery of services among different levels of facilities. The single-flow pattern assumes demanders at each node are serviced in facilities at the lowest-level, then transferred to facilities at higher levels, if necessary. The multi-flow pattern indicates that demanders can be allocated to facilities at any level. Service varieties determine whether the functions at a lower level can also be supplied at higher levels. According to spatial configurations, the service scopes of facilities at a lower level should be in accord with those at higher levels. The optimization objectives of hierarchical location-allocation models are mainly built on the basis of the classic models described above, e.g., the hierarchical p-median model, hierarchical maximal covering model, and hierarchical location set covering model [35; 41; 42]. Therefore, these models focus on the efficiency of facility configurations and more or less overlook equity/equality issues. In addition, most hierarchical location-allocation models also fail to address the complex interactions between the demand and supply. In other words, the two drawbacks of the classic models previously pointed out by [PERSON] [4] persist in hierarchical models.