esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 4 Existing Representative Works
### Lscc  
LSCC [49] breaks up long feedback loops and achieves timely adjustments at the traffic source to improve inter-DC RDMA congestion control. As shown in Figure 5, LSCC employs segmented link control to avoid delayed congestion control. A segmented feedback loop is established between egress switches connected to the long-haul link, providing timely congestion feedback and reducing buffer pressure on switches. The entire inter-DC transmission is divided into three segments: sender to local egress switch, local egress switch to remote egress switch, and remote egress switch to receiver.  
By deploying the rate calculation and rate-limiting modules at the egress switches, LSCC establishes a feedback loop over the long-haul link. This loop transmits bottleneck information from the remote DC to the local egress switch, which then provides timely congestion signals to the traffic source. Additionally, the feedback loop offers reliable buffering for the remote DC, interrupting the propagation of PFC signals and ensuring optimal bandwidth utilization of the long-haul link.  
Specifically, the rate calculation module and rate-limiting module are deployed at the ports of the egress switches connected to the long-haul link, collaborating to work as the segmented feedback loop. The rate calculation module at the remote egress switch periodically assesses the congestion level within its local DC based on the received feedback signals and quantifies it into a virtual bottleneck rate, which is then fed back to the rate-limiting module located in the remote egress switch. Upon receiving the feedback rate from the remote egress switch, the local egress switch should immediately adjust its rate to match the feedback rate. The rate-limiting module sends direct feedback signals to sources within its local DC based on the received bottleneck rate, cooperating with the rate calculation module's schedule.  
Figure 4: Design diagram of Bifrost (adapted from [23], Figure 4).  
Figure 5: Design diagram of LSCC (adapted from [49], Figure 3).  
The authors conduct evaluations using DPDK implementation and large-scale simulations, and the results demonstrate that LSCC significantly reduces average FCT by 30-65% under realistic DC loads and 31-56% under inter-DC loads.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 2 Theory Related to the Reduction in Measurement Points
### Characteristic Analysis of Beam Deflection Difference Distribution Curve under Concentrated Force  
3.1 Analysis of Distribution Curves of Beam Deflection Difference before and after Single-Cable Damage  
It is assumed that the single cable M16 has different degrees of damage (10%, 20%, 30% and 40%, respectively) when the concentrated force acts on the anchorage point of cable M19. According to the method described in Section 2.1, the deflection data of some measuring points (M26, M24, M22, M20, M19, M18, M16, M14, M12, M10, M8, M6, M4, M2, M1) are obtained. The cubic spline interpolation function is used to encrypt the deflection  
Figure 5: Methods for reducing the number of measurement points: (a) Before the reduction; (b) After the reduction.  
data points and obtain the deflection values at the anchorage points of each cable and main beam. Figures 6 and 7 show the deflection and deflection difference distribution curves of measurement points before and after the reduction in the measurement points when single cable M16 has different degrees of damage.  
As can be seen from Figure 6, the deflection of the girder at the position where the concentrated force acts is the largest, and the deflection distribution curve of the girder obtained through the reduction in measuring points almost coincides with that before the reduction in the measurement points (the actual deflection distribution curve), with a maximum error of only 0.89%.  
As can be seen from Figure 7, the girder deflection difference distribution curve obtained through the reduction in the measurement points of measuring points almost coincides with that before the reduction in the measuring points (actual deflection difference distribution curve) with no difference, and the maximum is only 2.76% (located at the anchorage point of cable M15 and main beam). When only cable M16 is damaged, the deflection difference distribution curve of the side girder where the damaged cable is  
Figure 6: Comparison of deflection distribution curves before and after the reduction in the measurement points in the case of single-cable damage (M16 has different degrees of damage).  
Figure 7: Comparison of deflection difference distribution curves before and after the reduction in the measurement points in the case of single-cable damage (M16 has different degrees of damage).  
located is convex downward in the area near the anchorage point of the damaged cable, and the peak value (sharp point) appears at the anchorage point of the damaged cable, while the deflection difference corresponding to the anchorage point of each cable of the side girder where the non-damaged cable is located is very small (the calculation results are omitted). As the damage to the cable increases, the peak of the beam deflection difference distribution curve at the anchorage points of the damaged cable increases.
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.4 Impedance Measurement Method (IM)  
In this method, the islanding can be detected through the impedance change of inverter output that caused due to the loss of the main power source and leads to voltage change as a result of current perturbation. This change is monitored by calculating \(\mathrm{dv}/\mathrm{di}\) as impedance observed from the inverter to detect the islanding. This method has a small NDZ in the case of a single DG source, but the detection effectiveness can be declined in the case of multiple inverters. Moreover, the impedance threshold value cannot be set easily because the exact value of grid impedance has to be known [20].
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 5 Research Conclusions  
An experimental test system for analyzing dust-mist coupling behavior based on a PDPA tester is constructed, which can be used to study dust-mist coupling behavior under different spray pressures and different dust suppression solutions. The particle sizes and velocity distribution parameters of particles in the dust-mist flow field can be obtained in real time continuously.  
1. Experiments on the influence of dust suppressants on mist field parameters under different spray conditions were carried out, and it was found that the Sauter Mean Diameter (D32) of the mist size of the dust suppressants showed an increasing trend compared to water. This trend became less obvious with an increase in spray pressure. From the perspective of surface tension, a reduction in surface tension of the dust suppressants promotes an increase in the particle size distribution of water mist.  
2. According to the test results of the dust-mist coupling behavior experiment, compared with water, the coupling efficiency of Dodecyl Alcohol (DA), Alkylphenol Polyoxyethylene (OP-10), and Sodium Dodecyl Sulfate (SDS) increased by 27.0%, 20.3%, and 15.0%, respectively. This indicates a proportional relationship between the wetting performance of the dust suppressants and the dust-mist coupling rate, and an inverse relationship  
Figure 11: Influence of suppressants on dust–mist effect after coupling.  
between the surface tension of the dust suppressant solutions and dust removal efficiency. These research findings hold major possibilities for enhancing dust-removal efficiency.  
This study was limited to a single type of dust sample. In order to get the comprehensive rules of dust-mist interactions, more dust suppressants, such as polymer and bio-based materials, should be researched on coupling performance with various types of dust in the future.  
D.L.: writing--original draft, visualization, methodology, investigation, formal analysis, conceptualization. M.L.: supervision, visualization, investigation, suggestions, project administration. U.M.T.: writing--review, suggestions, editing, formal analysis, conceptualization. W.L.: visualization, formal analysis. H.L.: visualization, formal analysis, methodology. M.M.: investigation, visualization, formal analysis. All authors have read and agreed to the published version of the manuscript.  
We acknowledge funding from the National Key Research and Development Plan of China (No. 2022 YFC2903901) and the National Natural Science Foundation of China (No. 51674289).  
Not applicable.  
All correspondence and requests for materials should be addressed to [PERSON].  
The authors express their gratitude to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their assistance with the experiments and their valuable discussions.  
The authors declare no conflicts of interest.
## 6 Conclusions  
Herein, we proposed a low-power sewer methane monitoring system. The system can monitor the methane concentration in a sewer in real time, judge whether the concentration exceeds a limit, and give appropriate warnings to avoid explosion accidents caused by excessive methane concentrations. The system uses ultrasonic technology to detect the concentration of methane in a sewer, which has the advantage of improving the safety and service life of the monitoring system as well as reducing the system's power consumption. The power consumption of the wireless monitoring system was analyzed. Furthermore, the impacts on the measurement results of other gases in the sewer and multiple factors, such as the environment of a sewer, were considered, and the interference of other gases was eliminated. Compensation for the environment (i.e., temperature and humidity) was used, further improving the accuracy of the system. The experimental results showed that the methane detection accuracy of the monitoring system could not only meet commercial requirements, but could also reduce the impacts of carbon dioxide, temperature, and humidity on the measurement results. Expanding monitored areas by adding sensor nodes and gateways will better prevent sewer explosions and provide useful information for sewer security management.  
Conceptualization, X.D. ([PERSON]) and Y.S.; methodology, X.D. ([PERSON]); software, H.S.; validation, Y.S. and X.D. ([PERSON]); data curation, X.D. ([PERSON]); writing--original draft preparation, X.D. ([PERSON]); writing--review and editing, Y.S. and X.D. ([PERSON]); visualization, X.D. ([PERSON]); supervision, H.S.; project administration, Y.S. and X.D. ([PERSON]); funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China. Project Name: Collaborative Strategy for Enzyme Catalyzed Microbial Metabolism Gas Sensor and Efficient and Rapid Detection, grant number 62271176.  
Figure 8: Results of methane concentration measurements before and after humidity compensation: (**a**) comparison of methane concentration measurements before and after compensation; (**b**) comparison of methane concentration measurement deviations before and after compensation.
# Automatic Identification of Bridge Vortex-Induced Vibration Using Random Decrement Method
## 2 Presentation of Method
### Etablishment of Threshold-Discriminating VIV and Random Vibration  
As seen in Equations (7) and (11), there is a significant difference between the results of random vibration and VIV obtained with RDT. Specifically, the random vibration reduces to a kind of damped free vibration, while the VIV become a harmonic vibration. Figure 4 showed the obtained result of the acceleration data previously shown in Figure 2. It is verified that the result obtained from the random vibration is a damped free vibration, with the initial amplitude of \(5\times 10^{-3}\) m/s\({}^{2}\). As the length of the segment is limited, the number of the subsample that intercepted from the segment is limited, which makes the signal different from an ideal free vibration. By contrast, the amplitude of the VIV signal after the random decrement process changes little with time, remaining at about \(5\times 10^{-2}\) m/s\({}^{2}\).  
In order to distinguish the two kinds of responses given in Figure 4, the COV of peak values of the processed signals is used. First, the peak value of processed data is selected, and the COV of peak value is calculated for random vibration and for VIV. For random vibration signals, the COV of the random decrement signal will be significantly larger than that of VIV signal.  
The calculation formula for COV is:  
\[\textrm{COV}=\sigma/\mu \tag{12}\]  
In order to determine the characteristic value of COV for random vibration, a large number of COV of the random vibration signal should be used. The mean value \(u_{c}\) and the standard deviation \(\sigma_{c}\) are obtained from large data set of COV. In this paper, the Pauta criterion was considered when calculating the threshold for determining the VIV, the confidence interval was chosen as 99.99%:  
\[\delta_{c}=u_{c}-4\sigma_{c} \tag{13}\]  
Figure 4: Random decrement signal of random vibration and VIV: (**a**) Signal of random vibration; (**b**) signal of VIV.  
When the COV is greater than the above threshold \(d_{c}\), the signal is determined as random vibration, and when the COV is less than the threshold, it is determined as a VIV. The main steps of the proposed method are summarized as follows:  
1. Selecting the cutoff value \(A\) and the length of the output signal in the segment;
2. Performing the random decrement method to obtain the processed signal;
3. Extracting the peak value of the processed signal to obtain the COV of each signal segment;
4. Selecting a certain number of COV of the random vibration signal to obtain a threshold value;
5. Calculate the COV using steps (1)-(4) for a new data set. When the COV for the new data set is less than the threshold, it is identified as VIV.
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 4 Control Methods of Servomotors
### Fuzzy-Logic Control  
The use of fuzzy logic in servomotor control allows for optimizing and adapting the motor control system to changing input conditions. Fuzzy-logic control primarily provides flexibility and adaptability in conditions of fuzzy tasks and a lack of complete data. Accordingly, fuzzy logic finds its application in applications with variable loads, mechanisms operating in a changing environment, and robotic systems. Fuzzy logic-based control is built on the following stages [89; 90; 91]:  
1. Identification of input conditions: In the case of a servomotor, the input variables are typically the position or speed of the output shaft.
2. Definition of fuzzy rules: Creating fuzzy sets with different degrees of membership for each input variable and establishing rules that link the conditions into a unified system.
3. Fuzzy logical inference: Applying the defined rules to each input value.
4. Aggregation: Combining the applied rules to determine the control action.
5. Defuzzification: Converting the overall control action rule into a specific value that is then applied to the servomotor.  
Although fuzzy logic does not require an exact mathematical model, tuning this method is quite labor-intensive. The development of the method is also influenced by the difficulty of predicting its behavior under different conditions and achieving optimal performance without loss of computational efficiency [89; 92].  
Considering that for fuzzy logic there is no need to use special and accurate mathematical models, the structure of the algorithm can be illustrated as in Figure 4.  
Unlike binary logic, where variables are divided based on the values of true or false, fuzzy logic determines the degree of membership of a variable to 0 or 1. The use of this type of control allows for adapting the control system to operate in changing conditions, even under the influence of various stochastic disturbances such as changes in load, detection of mechanical errors, etc. Fuzzy logic control is effective in situations where predicting the behavior of the mathematical model of the system is challenging, and there is no possibility of its precise determination [89; 92].  
Such systems include digital twins, which, when working with real-world objects, have a significant degree of uncertainty. Therefore, the adaptability of a control system based on fuzzy logic is an ideal solution for managing a digital twin and can also serve as a tool for creating a multitasking control system.
# CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator
## 3 Background
### Carla Autonomous Driving Simulator  
In Carla, there are three important concepts: (i) _world_, (ii) _actors_, and (iii) _blueprints_. During the simulation, a Carla client first creates its world in the server with a pre-defined map. In such a simulated world, actors are spawned that can be any object playing a certain role in the simulation, usually moving around the town (e.g., vehicles, pedestrians, and sensors). Actors are spawned out of blueprints, which is a 3D model with animation effects. Carla provides a set of ready-to-use baseline blueprints. For example, there are blueprints for various vehicle models, pedestrians with varying appearances (e.g., male, female, and child), and various sensors (e.g., camera, lidar, and radar). After creating a world with necessary actors, the simulation loop begins, where the client can control the weather and lighting conditions and move the ego vehicle, surrounding vehicles, and pedestrians. Besides, at each simulation step, the client can extract various information from the server. In particular, CarFree collects the following data that are necessary for generating 2D bounding boxes:  
* **3D bounding boxes and classes:** Assuming \(n\) actors operating in the town, \(n\) corresponding 3D bounding boxes are extracted. A 3D bounding box is denoted by its eight corner points in the set of 3D bounding boxes \(\{(C_{1}^{1},C_{1}^{2},\cdots,C_{1}^{8}),\cdots,(C_{n}^{1},C_{n}^{2},\cdots,C_{ n}^{8})\}\), where each point \(C\) is represented by \((x,y,z)\) in the 3D coordinate. We can also find each actor's class, denoted by \(T_{i}\) (\(1\leq i\leq n\)), which is either _vehicle_ or _pedestrian_.
* **Camera images:** A forward-facing camera is installed on the ego vehicle, from which a 960 \(\times\) 540 resolution RGB image can be retrieved. Each image is given by a 960 \(\times\) 540 matrix \(I\), where each element \(I[x][y]\) represents the color code of the pixel at the location \((x,y)\) in the 2D image coordinate.
* **Semantic segmentation images:** For each RGB image, its corresponding semantic segmentation image is also extracted, given by a 960 \(\times\) 540 matrix \(S\), where each element \(S[x][y]\) represents the class the object the pixel belongs to by the pre-defined color codes.  
Note that we have three different coordinates: (i) the _world coordinate_ (3D) where its coordinate origin is the center of the town, (ii) the _vehicle coordinate_ (3D) where its coordinate origin is the center of the ego vehicle, and (iii) the _image coordinate_ (2D) where its coordinate origin is the left-top corner of an image.
# Kinematic and Workspace Analysis of RRU-3 RSS: A Novel 2T2R Parallel Manipulator
## 4 Workspace Analysis
### Obtaining the Useful WS  
In PMs with more than 3 DOF, only partial representations of the WS are possible [32]. As the RRU-3 RSS has 2T2R motion capability, two partial bidimensional representations of the WS are calculated in this work:  
* Translational WS: represents the set of all positions that can be reached by the reference point (\(\mathbf{P}\)) of the mobile platform when this is kept at a constant orientation (\(\varphi,\ \theta\)).
* Rotational WS: represents the set of all attainable orientations of the mobile platform for a fixed position (\(x,\ z\)) of its reference point.  
The useful WS (WS\({}_{\text{useful}}\)) is defined as the set of poses that the mechanism can reach in a controllable way. That is, a region of the WS free of DKP singularities, but also free of collisions and where all types of kinematic restrictions are fulfilled. A WS\({}_{\text{useful}}\) calculation is needed to see the real potential of the manipulator and to carry out efficient path planning.  
There are different methods to calculate the WS of PMs, such as analytical methods, discretization methods or geometric methods [33]. In this paper, we use the discretization method to obtain and analyze the WS\({}_{\text{useful}}\). This method is implemented in MATLAB and has two main steps, as shown in Figure 5. In the first step, discrete poses of the end-effector are generated by means of a mesh in output variables (\(x,z,\varphi\) and \(\theta\)). For each pose, the IKP is solved to verify whether the mechanism can be assembled in those poses or not. When the IKP has a solution, the discrete candidate pose belongs to the WS. In the second step, all poses belonging to the WS are subjected to a kinematic analysis, in which DKP singularities, physical limitation of the kinematic joints, existence of collisions and other types of constraints can be checked to analyze which poses belong to the WS\({}_{\text{useful}}\).  
Although the implementation of discrete methods is simple, the computational cost is high, and their accuracy depends on the step size of the mesh. For the present work, the step size used for translational WS is 0.01 m and 1\({}^{\circ}\) for rotational WS.  
Figure 4: Example of some working modes for the same pose of the RRU-3 RSS mobile platform.
## 6 Experiments Results and Discussion
### MLP Model Results  
For the MLP as the used architecture, the system reached 88.90% accuracy, which is a high rate based on one feature: pitch. Figure 11 presents the confusion matrix result for each speaker; for better understanding, the symbol S_10 M refers to speaker 10, male gender. As shown clearly in the confusion matrix, all the mistakes/errors of the system involve mostly the same gender of the speakers; in other words, a given male speaker is mostly confused (or confused by) another male speaker but not female. On the other hand, the female speaker is confused by only a female speaker. For example, speaker S_10M has four misprediction errors, one for speaker S_11M and three for speaker S_5M, all male.
## 2 System Model
### Communication Model  
mmWave signals are highly susceptible to blockages in a dense urban environment [35; 36; 37]. Therefore, for mmWave communications, we consider only LoS data transmission link for both the SBS and D2D modes [38]. Conversely, for \(\mu\)Wave communication, we leverage an omni-directional antenna for data transmission because blockages have negligible effect over \(\mu\)Wave frequency due to low penetration losses. For consistency in data transmission links (i.e., mmWave cellular, \(\mu\)Wave cellular, and mmWave D2D), we assume that the content-requesting user always prioritizes the lowest path loss link. This ensures the establishment of a connection with the content transmitter, whether it is an SBS or proximity UE within D2D range.