esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Modification
### Ghost and CSPCM  
Ghost Module: Inspired by GhostNet, some structural improvements to the Backbone part of the YOLOv5s model were made by replacing the Conv module in the original YOLOv5s model with a Ghost module and naming it GhostCov, introducing the Ghost module as the Bottleneck in the original C3 module, and naming the improved C3 module C3 Ghost. The module structure is shown in Figure 5a.  
C3 Ghost is built on the concept of GhostBottleneck, which improves the model's feature extraction capability based on the original C3 module. By stacking the GhostCov modules numerous times, it is possible to capture more detailed information in the image and improve the accuracy of crack identification. This is especially important when dealing with issues like poor crack continuity, low contrast, and multiple directions and multiple morphologies.  
In terms of computational resources, the addition of the Ghost module provides some structural benefits. The Ghost map is generated using a simple linear operation rather than the original partial convolution operation, and the intrinsic map with less convolution operations superimposed through the residual-like structure with the Ghost map is utilized as the final output. This architecture can make better use of the model's limited computational resources, increasing its computational speed and efficiency. This is especially essential in UAV application scenarios since UAV road detection situations typically have limited processing resources and high-performance requirements.  
CSPCM Module: Inspired by C3 Ghost's achievement in lowering the number of parameters while retaining detection accuracy, this lightweight module design incorporates the concept of residual linkage. Meanwhile, as a major component, the Connix module is introduced, and the CSPCM module is proposed. This module successfully decreases the number of parameters while maintaining detection accuracy by combining the characteristics of the Convix module and the benefits of the residual connection.  
As shown in Figure 5b, the CSPCM module realizes feature cascading and information transmission by cascading the branch and backbone networks in a cascade operation. The Convix module is specifically used in the CSPCM module, which uses grouped convolution, residual connection, and 1 \(\times\) 1 convolution operations for nonlinear enhancement and feature channel tuning. This design extracts a rich feature while reducing feature dimensionality, reducing the number of parameters while preserving detection accuracy.  
The CSPCM module successfully combines the idea of C3 Ghost with the benefits of the Convix module to achieve the goal of simultaneously lowering the number of parameters while maintaining detection accuracy in the target detection task. The benefit of this design is that it conserves the model's storage and computational resources while retaining sufficient expressive capability, resulting in greater efficiency and performance for practical applications.
# Effect of Impeller Trimming on the Energy Efficiency of the Counter-Rotating Pumping Stage
## 2 Materials and Methods  
### Fundamental Criteria  
Trimming the outer diameter of the impeller increases the gap between the impeller and the spiral guide vane. As a result, equalization of the flow behind the centrifugal wheel decreases the force interaction of the flow with the working parts of the impeller. This effect is widely used to reduce pressure pulsations, vortices, and noise of the pumping unit [19].  
When reducing the output diameter \(D_{2}\) of an impeller, peripheral speed decreases. Therefore, the pressure head of the centrifugal stage is decreased. Experiments show that moderate impeller trimming slightly changes the pump's energy efficiency.  
When impeller trimming for centrifugal pumps, the impeller correction and the change in pump characteristics can be roughly determined by the following similarity equations [20]:  
\[Q^{\prime}=Q\frac{D_{2}^{\prime}}{D_{2}};\;H^{\prime}=H\left(\frac{D_{2}^{ \prime}}{D_{2}}\right)^{2};\;N^{\prime}=N\left(\frac{D_{2}^{\prime}}{D_{2}} \right)^{3}, \tag{1}\]  
where \(Q\), \(H\), \(N\), \(D_{2}\) represents the nominal flow rate [m\({}^{3}\)/h], pressure head [m], power consumption [kW], and external diameter of the impeller [m] before trimming; \(Q^{\prime}\), \(H^{\prime}\), \(N^{\prime}\), and \(D_{2}^{\prime}\) represents the nominal flow rate [m\({}^{3}\)/h], pressure head [m], power consumption [kW], and external diameter of the impeller [m] after trimming.  
These dependencies allow the building of the characteristics of a pump with a turned wheel. Remarkably, the impeller turning along the outer diameter changes not only the pump's characteristics but also its speed ratio:  
\[n_{s}=3.65n\frac{\sqrt{Q}}{\sqrt[4]{H^{3}}}, \tag{2}\]  
where \(n\)--operating speed, rpm.  
Simultaneously, the pressure head decreased more significantly than that according to the standard calculation technique. The energy efficiency of the pump decreased too. Consequently, the rational impeller trimming should be consistent with the speed ratio.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 6 Discussion
### Research Questions
#### 6.1.4 RQ4--Limitations  
There is a huge variety of states, actions and rewards. Since they are often very problem-dependent, there is a lack of consensus on what the best approaches are for each agent-environment interaction.  
Exploration is a key part of RL approaches. They require extensive interactions with the environment, which can be computationally expensive. As a result, RL training is likely slower than other end-to-end deep learning approaches, such as supervised learning, that train on large, curated datasets. However, dataset generation can be cumbersome and expensive. Thus, the development of more data-based approaches can also be considered slow. Since RL approaches can instead leverage existing mathematical formulations, they effectively skip this step. Moreover, various methods such as curriculum learning and offline RL help reduce inefficiencies by improving both training speed and sample efficiency.  
While RL training can be slow, a deployed agent is fast when compared to traditional exact or search-based methods used for combinatorial problems. For example, linear programming models and metaheuristic methods can take significantly longer to reach good solutions. Also, the iterative nature of RL easily allows for hybrid approaches.  
There are multiple examples where very simple representations and simple approaches are enough to solve small instances of complex problems. However, these smaller state and action spaces are often not enough to represent and interact with the real problem. Industrial problems can be represented in many ways, as seen in Table 2. However, a very complex and thorough state might still not solve the problem. Adding unnecessary or redundant information increases the complexity of the approaches, often increasing the number of parameters greatly, without being matched by the same increase in performance. A careful balance is needed between state and action space length versus their inherent complexity increase.  
When more complex approaches are needed, such as GNNs or RNNs, these require some expertise to train efficiently. They are often accompanied by comparatively longer training computational times, which can prevent these models from converging and seeing competitive results. Furthermore, while these bring some advantages compared to traditional models, such as variable-sized inputs or outputs, they do not solve the scalability issues of these representations. Graphs' edge numbers grow exponentially with number of nodes. Recurrent approaches are forced to use extra mechanisms, such as attention, to not lose important information.
## 3 Remote Terminal Unit (RTU)
### Hardware Description of the RTU  
#### 3.1.1 Acquisition and Processing Module (APM)  
This module includes six analog channels (with possible expansion up to 16), which are used to receive signals from the climatologic sensors such as: AT, RH, SR, BP, WD and WS. It also monitors the state of the rechargeable battery in order to prevent a power failure. The RTU also processes two digital channels, which consist of signals delivered by the WS and rain sensor. The analog channels are designed to receive input voltages from 0 to 5 V (solar radiation sensor), 0 to 2.5 V (Pressure Sensor and voltage of the battery) and from 0 to 1 V (Temperature, Pressure, and Relative Humidity sensors). These voltages are converted into a 12-bits digital value by the internal ADC of the microcontroller and saved in the EEPROM data memory. Digital channels are related to pulse counting or frequency measuring. The rain sensor catches rainfall in a 200 mm collection funnel. When 25.4 mm of rainfall are collected, the tipping bucket assembly tips and activates a reed switch. As a consequence, the switch closure generates a pulse, which is recorded by the MCU and added in a 32-bits counter. Finally, when the bucket tips, the water drains out the screened base of the gage. On the other hand, wind speed is measured with a three-cup anemometer. Rotation of the cup wheel opens and closes a reed switch at a rate proportional to wind speed. The maximum frequency that the MCU can measure is 74.76 Hz or, in terms of speed, the range varies from 0 to 49 ms\({}^{-1}\). In Figure 2 a block diagram of the main components of the APM are depicted as well as the source power and communication modules, which will be explained in the next section.  
Figure 2: Block diagram of the main modules of the Remote Terminal Unit.
## Materials and Methods
### Estimation of Forest Degradation Indicators
#### 2.4.2 Net Primary Productivity (NPP)  
NPP was calculated according to the theory of Light Use Efficiency (LUE) as follows [39; 40]:  
\[\text{NPP}=\varepsilon\cdot\text{fPAR}\cdot\text{PAR} \tag{1}\]  
where  
PAR is photosynthetically active radiation (MJ/(m\({}^{2}\) month)),  
fPAR is the fraction of PAR absorbed by vegetation canopy,  
\(\varepsilon\) is the light use efficiency coefficient (g of C/MJ) and includes the plant respiration costs [41].  
Light Use Efficiency Coefficient (\(\varepsilon\))  
The Light Use Efficiency coefficient \(\varepsilon\) was derived following the MODIS-GPP approach [42] where \(\varepsilon\) is calculated from two factors: the biome-specific maximum conversion efficiency \(\varepsilon_{\text{max}}\) and the effect of temperature f (T) and water on plant photosynthesis f (W) [39].  
Estimation of fPAR and PAR  
To estimate fPAR, an empirical relationship between fPAR as measured in the field, and the Normalized Difference Vegetation Index (NDVI), was used. Pearson correlation coefficients (r) were used to assess the strength of the relationship. NDVI was selected to correlate fPAR based on previous reports of successful correlation [43; 44; 45; 40]. The measurements of fPAR (62) were taken randomly within the study area using a ceptometer model Accurap LP-80 TM during the beginning of the rainy season of 2013. Due to the lack of available imagery that matches the period of the measurements, an image from 2012 within the rainy season (December) was used to derive the correlation coefficients. A linear model was fitted to derive values of fPAR from other satellite imagery.  
Data acquired from meteorological stations as radiation (W/m\({}^{2}\)) were converted to PAR daily solar units (MJ). Then, solar radiation was converted to PAR using the coefficient 0.47 [46; 40]. Later, daily PAR was converted to monthly values and spatially interpolated across the extent of the study area.  
The Calculated Length Growing Period (LGP) [47] was used to separate and classify Landsat imagery to be used for monthly NPP estimates. LGP was used to avoid the mismatch of Landsat imagery within the wrong phenological state of the forest.
## 4 Discussion
Current results of numerous studies, such as near-infrared spectroscopy (NIRS) or time-resolved spectroscopy (TRS) for deep muscle sampling, enable extremely precise measurements [74]. It has been shown that a slight increase in tissue temperature (1-2 \({}^{\circ}\)C) does not cause excessive heat stress, which is considered an inducer of oxidative stress in skeletal muscles. A slight increase in temperature triggers adaptive mechanisms that protect against protein degradation through increased proteolysis and autophagy. As a consequence, there may be a better metabolism and optocoupling of proteins in skeletal muscles. Additionally, it has been described that a slight increase in temperature may affect muscle plasticity. Contrast therapy slightly affected tissue temperature, which may suggest a positive, stimulating effect on skeletal muscles [75]. In our studies, the effect of GR-HCT improved the maximum isometric force score. Stimulation time was not statistically significant for 5 and 10 min. It indicates that 5 min is the optimal time for improving isometric forearm strength. Numerous studies focus on the improvement of muscle strength, power, and hypertrophy after the application of a thermal stimulus on the activity of heat shock proteins (HPSPs) [76; 77]. HSPs are considered molecular chaperones that play a universal role in maintaining cellular homeostasis. It has been confirmed that HSPs are expressed in skeletal muscles, the induction of which varies depending on the histological and even functional characteristics of the muscles [76]. Heat increases the expression of genes involved in the growth and differentiation of muscle cells [78].
# DTN Trustworthiness for Permafrost Telemetry IoT Network
###### Abstract
and given the strong performance that some modern transport protocols showed in our tests, we believed that it was crucial to assess how the use of modern transport protocols could improve or affect the performance and trustworthiness of the service, especially in this congestion situation provoked by the DTN technique. Having collected the initial results and analyzed the system's trustworthiness in previous work with the standard transport protocols of the devices' operative systems, this paper studies the trustworthiness and compares the usage of different transport protocols by modeling the scenario in the Riverbed Modeler. The paper contributions are as follows:  
1. The definition and concretion of the remote sensor network architecture that will be deployed in Antarctica, detailing the type of nodes, protocol stack, and communication techniques that will be used. 2. The modeling of the Antarctic scenario in the simulator. To perform the simulation tests, we modeled the communication media (LoRa and NVIS), the telemetry application, the faulty behavior of Byzantine nodes, the social trust management and the consensus algorithms, the DTN technique, and the tested transport protocols. 3. The assessment and analysis of the results using our proposed trustworthiness model. From this analysis, we conclude which transport protocol best suits our use case and propose a modification of the scenario to be deployed in Antarctica. The rest of this paper is structured as follows. Section 2 describes the related work in DTNs, transport protocols, and a system's trustworthiness. Section 3 defines our use case's network architecture. Section 4 reminds our proposed model to measure and evaluate a system's trustworthiness. Section 5 describes the simulation tests. Sections 6 and 7 present and discuss the obtained results, respectively. Finally, Section 8 concludes the paper.
## 4 Analysis of Inner Motion Loops
### Incremental Cartesian Position Inner Loop
#### 4.3.3 Implications  
As in the case of absolute position loop, in the Jacobian inverse method, the stiffness matrix must be determined through the transformation of the proportional gain from the space of the articulations into the cartesian space of the end effector. However, it should be noted that, in an incremental position loop, the force will not be affected by environmental conditions.  
Table 3 shows the steady-state force obtained by replacing \(\Delta x_{d}\) by the algorithms exposed in Table 1.  
Here, \(S_{f}\) is the selection matrix of the force direction and \(K_{pf}\) is the active compliance matrix of the force control.  
The force control with incremental position loop depends only on the active stiffness of the position control, \(K_{px}\) or \(\overline{K}_{px}\), and the active stiffness of the force control, \(K_{pf}\). Besides, the feedforward action is easier to implement because it only depends on the gains of the position controls. For the case of Jacobian transpose, to obtain zero error, the feedforward should be  
\[f_{\infty}=f_{d}\ \Leftrightarrow F\!F=K_{px}^{-1}f_{d}, \tag{53}\]  
and for the case of the Jacobian inverse,  
\[f_{\infty}=f_{d}\ \Leftrightarrow F\!F=\overline{K}_{px}^{-1}f_{d}, \tag{54}\]  
In summary, the incremental position control has an important advantage over the absolute position control; it does not depend on characteristics of the environment.
## 2 Materials and Methods
### Feature Extraction
#### 2.2.2 Local Ternary Patterns  
[PERSON] and [PERSON] [51] presented a new texture operator which is more robust to noise than LBP in uniform regions. It consists of an LBP extended into 3-valued codes (\(0,1,\,-1\)). Figure 3 shows a practical example of how Local Ternary Patterns (LTP) work: first, threshold \(t\) is established. Then, if any neighbor pixel has a value below the value of the central pixel minus the threshold, it is assigned \(-1\) and, if the value is over the value of the central pixel plus the threshold, it is assigned \(1\). Otherwise, it is assigned \(0\). After the thresholding step, the upper pattern and lower pattern are constructed as follows: for the upper pattern, all \(1\)'s are assigned \(1\), and the rest of the values (\(0\)s and \(-1\)'s) are assigned \(0\); for the lower pattern, all \(-1\)'s are assigned \(1\), and the rest of the values (\(0\)s and \(1\)'s) are assigned \(0\). Finally, both patterns are encoded in two different binary codes, so this descriptor provides two binary codes for one pixel instead of one as LBP does, that is, more information about the texture of the image. All of this process is shown in Figure 3.  
The LTP operator has been applied successfully to similar applications as LBP, including medical images, human action classification and facial expression recognition, among others.
## 2 Literature Review
### Addressing Unique Challenges in Ocean Cities  
While we have highlighted the numerous advantages of urban color design in ocean cities, it is equally vital to acknowledge and address the specific challenges inherent to these coastal environments. Ocean cities present a distinctive set of hurdles that necessitate thoughtful consideration when formulating color design strategies. Here, we delve into some of these unique challenges to offer a more comprehensive perspective:  
Dynamic Coastal Environments: Ocean cities are intrinsically linked to dynamic coastal ecosystems characterized by fluctuating tides, changing weather patterns, and seasonal variations. These dynamic shifts pose a continuous challenge to urban color design. The color palette and materials chosen must not only withstand the rigors of coastal climates but also adapt harmoniously to the ever-changing natural surroundings. The ability to maintain aesthetic coherence amidst such dynamism is a paramount concern.  
Harmonizing with Natural Surroundings: Achieving a seamless blend between urban structures and the natural beauty of coastal landscapes is another challenge unique to ocean cities. In these environments, vibrant blues of the ocean, soft sandy shores, and lush greenery often set the backdrop for urban developments. Urban color choices must respect and complement these natural colors, ensuring that the cityscape harmoniously integrates with, rather than clashes against, its breathtaking surroundings.  
Durability in Saltwater Exposure: Ocean cities face a constant battle against the corrosive effects of saltwater exposure. The harsh saline environment can accelerate wear and tear on materials, potentially compromising the longevity and vibrancy of urban color schemes. Designers must carefully select materials and color finishes that are not only aesthetically pleasing but also engineered to withstand the corrosive impact of saltwater, ensuring the durability and integrity of the chosen color palette.  
Balancing Environmental Conservation: Conservation and preservation of the ocean ecosystem are paramount in ocean cities. The choice of colors must align with principles of environmental sustainability, avoiding any adverse impact on marine life or water quality. This necessitates the use of eco-friendly and non-toxic materials in urban color design, minimizing the ecological footprint of color choices and materials.  
By recognizing and addressing these unique challenges specific to ocean cities, our approach to urban color design becomes more nuanced and adaptive. The careful consideration of these hurdles ensures that our color design strategies not only enhance aesthetics but also coexist harmoniously with the dynamic coastal environments, contributing to the overall sustainability and livability of ocean cities.
## 3 Data Introduction
### Issues and Solutions  
The 1:50,000 scale data in this study area was collected by two different companies for sampling, analysis, and other tasks. As a result, systematic errors inevitably arise, which are directly reflected in the \"shift\" effect at the boundaries of the map sheets in the geochemical maps. This means that there may be discontinuities or abrupt changes in geochemical values between different map sheets. The ideal solution to this problem would be to conduct repeated sampling at the boundaries of the map sheets to calibrate the data of each map sheet. However, this is difficult to implement in field exploration, so alternative methods, such as the classical CR method, must be considered for calibration.  
In addition, we can assume that the boundaries of the northern and southern map sheets have similar geological backgrounds, meaning the geochemical data characteristics should be similar. In this case, the BL method can be used for data leveling.  
Furthermore, the six 1:50,000 scale map sheets in the study area are located within the same 1:200,000 scale map sheet. Since the geochemical mapping at the 1:200,000 scale  
Figure 4: Box plot of the raw data (1:50,000 scale geochemical data).  
was carried out earliest, and the work within the same map sheet was conducted by the same unit, the consistency between the data is relatively high. Based on this, we can use the 1:200,000 scale geochemical data to calibrate the 1:50,000 scale data, thus compensating for the systematic errors in the 1:50,000 scale data.  
To compare the results of data calibration and verify its reliability, we included the CR method, BL method, and MSC method for comparison.