esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Performance Analysis of Interference in DSS System
## 4 DSS Interference and Solutions
### DSS Interference Principle  
DSS is deployed with 4G and 5G spectrum sharing, both LTE and NR are OFDM symbols with LTE subcarrier spacing of 15 kHz and NR low-frequency subcarrier spacing of 30 kHz. The frame length and time slot ratios are kept consistent for both networks, and both time and frequency domains are aligned [41]. However, since 4G is a broadband system, the channel configuration is relatively broad, with full-band mapping of control channels, pilot frequency, etc. 5G is a broadband system with the same types of physical channels. Therefore, the reference signal such as SSB or the demodulation reference signal (DMRS) of 5G NR will conflict with the reference signal (CRS) of LTE in terms of time and frequency resource allocation. It is easy to cause intra-frequency interference between LTE and NR. Due to the flexibility of the NR physical layer, 5G interference is less than 4G, and 4G interference is more than 5G [42].  
In addition, there is serious interference in the DSS networking, which has a great impact on the wireless network environment and user experience. LTE is based on physical cell identifier (PCI) Mode 3, which means that there is a mode 3 interference of LTE [43]. The 3 GPP protocol stipulates that there are 4 cell common reference signals (CRS) in each RB [44]. In this case, it is specified that there is one CRS in every six subcarriers in the frequency domain and the CRS is located in the first and fifth symbols in the time domain. Since the LTE system uses a dual antenna transceiver, there are three cases of positions of CRS within RB, which are shown in Figure 5A-C.  
As shown in Figure 5, if the CRS is in the same position within the RB, this is a mode 3 conflict, also called mode 3 interference. Since there are only three possible positions of CRS within RB, a mode 3 conflict is bound to occur when signals from four or more cells appear at the same position [45].  
Figure 6 shows a schematic diagram of DSS interference.  
Figure 4: Downlink channel map.  
Figure 5: Positions of CRS within RB.  
Figure 6: Schematic diagram of DSS interference.  
Since LTE CRS always exists and there is no CRS interference cancellation mechanism for NR terminals, LTE based on PCI Mode 3 planning will destroy the demodulation of NR time-frequency resources in the shaded part of the figure, thus bringing interference. This paper mainly analyzes the more serious DSS neighbor interference in the existing network.
# Union Models for Model Families: Efficient Reasoning over Space and Time
## 6 Analysis and Reasoning with Model Families
### Analysis and Experiments
#### 6.2.1 Methodology
The same can be said for the number of individual models; a family is considered small when it contains a handful of models, and it is considered very large when it contains hundreds of models or more. To evaluate the property checking task (i.e., RT1), each annotated individual model \(m\) in a model family \(MF\) was encoded as a propositional logic formula, namely, \(\Phi m=\bigwedge\textit{ElementToPropositionWithAmotation}(e_{i}),e_{i}\in m\), where \(e_{i}\) are elements of the model \(m\). A union model \(M_{U}\) of that \(MF\) was encoded as \(\Phi M_{U}=\bigwedge\textit{ElementToPropositionWithAnnotation}(e_{i}),e_{i}\in M_{U}\). Furthermore, the property to be checked was encoded into a propositional formula \(\Phi p\). Afterwards, an SAT solver was used to check whether or not the encodings of each of the individual model and their union model satisfied the property. In particular, a formula \(\Phi m\wedge\Phi p\) was constructed for each individual model. The property was said to hold in any model if and only if this formula was satisfiable. Similarly, a formula \(\Phi M_{U}\wedge\Phi p\) was constructed and checked against whether the property was satisfiable. In both experiments, using the same computer settings, the time it took to check a property on individual models (\(T_{ind}\)) was recorded and compared to the time needed to carry out the check on union models (\(T_{MU}\)).
## 4 Description of the Developed Method
### Data Acquisition and DFT  
The data acquisition follows the IEC 61000-4-7 standard. The sampling period is 20,480 Hz and the samples are stored in a buffer of 4096 data points, corresponding to an acquisition window of 200 ms (T1), to achieve a DFT base frequency of 5 Hz when the fundamental harmonic is 50 Hz. As shown in Figure 4, data acquisition occurs at the start of the algorithm, and whenever the system fails to meet the error condition, the signal obtained with the LKF does not correspond to the input signal.  
The acquired signals are stored in an input data buffer. A Hanning weighting window [23] is applied to the buffer values, and the result is used to compute the DFT. The DFT provides a combination of spectral components consisting of real values, at frequencies close to the spectral components actually forming the signal to be measured, and a set of component values that do not actually exist, originating from leakage and picket-fence effects.  
Figure 4: Flow diagram of the evolution of Kalman filter calculations.
# An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure
###### Abstract  
Structural Health Monitoring (SHM) is a very important research domain whereby civil infrastructure is monitored. Using wireless technologies can boost SHM by providing the level of autonomous operation that is essential for these tasks. Wireless routing, with its subset, geographic routing, is an important procedure that needs to be optimised, in order to lead packets to the basestation. Occasionally, routing is susceptible to interference and collisions due to a large number of connected devices. This fact led to cooperative transmission; cooperative networks are the ones that utilise relays to accomplish the transmission of packets; thus, resulting in link quality as well as throughput increase. In this paper, we investigate the Collection Tree Protocol (CTP) to show that it can be cooperative when used in an SHM for civil infrastructure monitoring applications giving a geographical essence to the routing protocol. We do that by exploiting the fact that the CTP's mechanism uses its tree formation for a node to transmit to the best link quality parents. An example of a cooperative model to show that it may be applied to the protocol is given. Further, Indriya testbed results of direct and cooperative transmissions are given to strengthen the case of this work, with which a scenario where the CTP exhibits better link quality when using a relay is given. A practical addition is suggested, whereby an extra field in the packet struct is proposed, which will provide the CTP with further strength to changing conditions and direct communication loss.  
collection tree protocol; expected transmission count; cooperative communication; parent; relay; structural health monitoring; infrastructure +
Footnote †: journal:  
Article An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure  
1 Hellenic Institute of Transport, Centre for Research and Technology Hellas, 60361 Thessaloniki, Greece 1  
2 Department of Informatics and Telecommunication, University of Ioannina, Kostakioi, 47150 Arta, Greece 2
## 4 Overview of the Proposed Error Recovery Mechanism
### The Proposed Error Recovery Mechanism
#### 4.3.1 Adaptive Service Differentiation  
To address adaptiveness to users' or applications' requirements, an adaptive service differentiation mechanism has been included in the proposed approach. In this approach, two priority levels are defined: critical and non-critical (for simpli, we have considered two levels, but there can be more). Each sensor in a health monitoring system is labeled by one of these two priority levels. However, this priority assignment is not static, and users can reassign a priority level to sensors at any time depending on their needs. This functionality allows the users to identify the critical sensors for the system any time and get their responses according to their or the applications' demand. Thus, this QoS mechanism will be able to respond to users' or applications' queries in an adaptive fashion. The PS or MS executes the adaptive service differentiation algorithm and sends the priority information to the relay nodes, even to the sensors (not considered in this work) if necessary. In an application, the role of the relays is predefined, but can be changed in the next cycle if necessary. The change of role for the relays may require additional control overhead. As the relay nodes are comparatively powerful than sensor nodes, they can afford this overhead. Moreover, the role of relays in many healthcare applications may change very infrequently, which will minimize the overhead, and the impact on the overall performance will be little. The MS or PS assigns priority for every sensor depending on the application or the disease types and their senors' relative importance. The priority settings for each application can be preset (as shown in Table 2) or given by the doctors/nurses online. Here, we only consider critical and non-critical settings. Within each of this setting category (hierarchical), further leveling is possible.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 3 SBAUPT-Based Pedestrian Dead Reckoning (S-PDR)
### Step Detection
#### 3.1.2 Temporal Filtering  
Although filtering the signal removes most of the unwanted signal fluctuation interfering with the step detection, there is leftover motion noise that can affect the detection accuracy. To enhance the peak/valley extraction for step detection, temporal filtering is used to avoid unwanted peak/valley detections. The temporal filtering is carried out by introducing candidate and rejection regions. The candidate regions represent the regions where a peak/valley exists and only one peak/valley is accepted. The rejection region is the region where no peak/valley should exist and if any is detected, it is discarded.
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial** & **Reference** & **Author** & **Algorithm Name** & **Advantages** & **Disadvantages** \\ \hline
10 & [13] & [PERSON]. &
\begin{tabular}{c} Proposed a task load balancing scheduling \\ algorithm based on an colony optimization \\ (WLB-ACO). \\ \end{tabular} & The algorithm task completion & The task scheduling quality is \\ \hline
11 & [14] & [PERSON]. &
\begin{tabular}{c} Presented a particle swarm optimization with the \\ random forest classifier algorithm. \\ \end{tabular} & In order to balance the utilization & This method relies too much \\ \hline
12 & [15] & [PERSON]. &
\begin{tabular}{c} Presented a resource scheduling method based on \\ a parallel genetic algorithm. \\ \end{tabular} & It can reduce the overall execution & It easily falls into local \\ \hline
13 & [16] & [PERSON]. &
\begin{tabular}{c} Introduced the concept of virtual machine relative \\ fitness. \\ \end{tabular} & It can obtain greater variation. & It speeds up the convergence \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._On the basis of the existing research results, this paper analyzes and refers to a large number of network task scheduling algorithms in distributed and heterogeneous environments. Combined with the characteristics of SWIM, the adopted ant colony algorithm is improved. We propose a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB). The simulation experiment results show that the performance of the ACTS-LB algorithm is better than that of the traditional min-min algorithm, ACO algorithm and PSO algorithm. The specific contributions of this study are as follows:  
(1) The ACTS-LB algorithm reduces the task scheduling completion time and improves SWIM resource utilization;  
(2) It can ensure that SWIM has better load balancing performance;  
(3) It is of great significance to promote the SWIM application for civil aviation industry development.
## 3 Results
### Microstructure and Properties  
#### 3.3.1 Macrostructure  
The macrostructure of the WAAM alloy is shown in Figure 8, which can be divided into three areas: penetration zone (PZ) affected by arc, heat affected zone (HAZ), and as cast zone (ACZ) as show in Figure 8A-C, respectively. Moreover, the macrostructure model of the WAAM alloy was established, as shown in Figure 9, where \(t1\) is the width of the PZ; \(t2\) is the width of deposit spreading zone; \(t\) is the effective thickness of the WAAM alloy (=\(t1+2t2\)); \(s\) is the depth of the single layer in the PZ; \(h1\) is the height of the HAZ; \(h2\) is the height of the ACZ; \(h\) is the height of the single layer (=\(h1+h2\)). According to the research results of Gu [25], \(h\) and \(t\) increase with WFS and decrease with increasing WS. The window of WFS studied in this paper was smaller (5.0-6.0 m/min); thus, the main factor influencing \(h\) and \(t\) was WS. With the decrease in WS, \(h\) and \(t\) gradually increased, as shown in Figure 7. \(h\) consists of two parts: height of the HAZ (\(h1\)) and height of the ACZ (\(h2\)). \(h1\) is affected by heat input (i.e., with the increase in heat input, \(h1\) will gradually increase). Moreover, \(t\) is composed of two parts: width of the PZ (\(t1\)) and width of deposit spreading zone (\(t2\)). \(t1\) is affected by the WFS (the CMT power automatically equates to the current and voltage according to the WFS, while the width of the PZ is mainly affected by the voltage). In addition, \(t2\) plays an important role in that \(t\) is affected by heat input (i.e., with the increase in heat input, \(t2\) will increase gradually). The depth of the PZ (\(s\)) is mainly affected by the WFS (current). With the increase in WFS, \(s\) will increase gradually.  
Figure 8: Macro metallographic pictures of WAAM alloy (A is penetration zone (PZ); B is heat affected zone (HAZ); C is as cast zone (ACZ)).  
Figure 7: Thickness and layer height of WAAM alloy under different heat inputs.
## 5 Flight Verification and Data Analysis
### Analysis of Heat Transfer Characteristics On-Orbit
#### 5.5.2 Analysis of Heat Transfer Characteristics inside Payload Rack  
Payload rack 1 is a gas-liquid mixed heat exchange rack. During the period of on-orbit operation, the fan is opened for air-cooling heat exchange; the typical heat exchange parameters and hydraulic parameters in the working process of the rack are as shown in Figure 23. In Figure 23a, the working pressure difference in the payload rack 1 is 102.1 kPa. During operation on-orbit, the flow rate values of the three branches of the rack are 58.2 L/h, 30.6 L/h and 30.2 L/h. The flow rate of the fan is 77.5 m\({}^{3}\)/h. The transient temperature responses are given in Figure 23b, the temperature of the working medium provided by the thermal control bus is 22.4 \({}^{\circ}\)C.  
The results of the analysis are shown in Figure 24; the active heat exchange capacity of the whole rack reaches 272.7 W, and the maximum air-cooled heat transfer capacity of the secondary thermal control system inside the rack is 160 W; the ratio of air-cooled heat transfer is 58.7%, and the active heat transfer efficiency of the whole rack is 85.96%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name of Payload** & **Object** & **K\(\mathbf{v_{i,j}}\) (\%)** & \(\mathbf{\varphi_{i}}\) (W)** & \(\mathbf{\Delta}\boldsymbol{T_{i}}\) (\({}^{\circ}\)C) & \(\mathbf{\phi_{i}}\) (W) & \(\mathbf{\eta_{i}}\) \\ \hline \multirow{3}{*}{Payload rack 1} & Branch 1 & 35 & & 3.5 & 211.51 & \\  & Branch 2 & 30 & & 1.3 & 41.30 & \\  & Total of rack & & 345 & 2.4 & 296.55 & 85.96\% \\  & Branch 1 & 32 & & 1.5 & 55.14 & \\  & Branch 2 & 5 & & 2.2 & 0.00 & \\  & Total of rack & & 420 & 3.3 & 368.35 & 87.70\% \\  & Branch 1 & 28 & & 2.9 & 93.95 & \\  & Branch 2 & 5 & & 2.2 & 0.00 & \\  & Total of rack & & 185 & 1.8 & 165.03 & 89.21\% \\  & Branch 1 & 38 & & 2.7 & 165.41 & \\  & Branch 2 & 44 & & 11.6 & 799.77 & \\  & Total of rack & & 1050 & 7 & 911.45 & 86.80\% \\  & Branch 1 & 22 & & 1.8 & 82.80 & \\  & Branch 2 & 40 & & 2.8 & 209.62 & \\  & Total of rack & & 360 & 2.5 & 302.16 & 83.93\% \\ The whole module & & & 2723 & 3.3 & 2279.99 & 83.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: On-orbit data of the integrated thermal management system in the module.  
Figure 23: Heat transfer characteristic curve of payload rack 1 on-orbit: (**a**) Hydraulic parameters; (**b**) Temperature characteristic.
# Applied Research of the UAV Illumination Measurement System in Sports Stadiums
## 3 Results
### Illumination Acquisition Experiment  
The UAV illumination acquisition experiment in this paper was chosen to be carried out in the sports stadium located at Dalian Polytechnic University. The school sports stadium is a versatile facility measuring 40 m in length, 20 m in width, and 10 m in height. It houses designated areas for badminton, basketball, and volleyball. The stadium is well-lit with evenly distributed overhead lighting, ensuring optimal visibility for athletes. And the illuminance measurement experiment was chosen to be carried out at night to avoid the influence of sunlight. The experimental scenario was built as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Static Data (Ix)** & **Dynamic Data (Ix)** & **Relative Error** & **Error Rate** \\ \hline
1 & & 349 & \(-6\) & \(-1.70\%\) \\
2 & & 352 & \(-3\) & \(-0.85\%\) \\
3 & & 348 & \(-7\) & \(-1.98\%\) \\
4 & & 353 & \(-2\) & \(-0.57\%\) \\
5 & 355 &  
As the stadium floor is yellow, green illuminance collection points were chosen to be arranged on the ground in order to facilitate the accurate identification of measurement points during the UAV illuminance collection process. According to the stadium illuminance standard, the point was set up at 2 m intervals, and illuminance measurement was carried out according to the center-point method. In addition, during the experiment, the illuminance measurement area stadium lamps were arranged as shown in Figure 12.  
In the illumination acquisition experiment, the UAV flew at a constant speed of 1 m/s.  
The position of the UAV was determined based on the fact that the UAV could record and return the current flight altitude in real time, and the distance traveled in a single flight. It can be determined whether the drone is above the measurement point or not based on the live feed from the vertical head of the drone. The drone's live image feed is shown in Figure 13.  
Although the illuminance measurement experiments are carried out indoors, the drone recorded the flight data during the experiment, including flight height, flight distance, and flight speed. The UAV flight log is shown in Figure 14.  
Figure 12: Illumination measurement area layout map.  
Figure 13: UAV captures measurement points.  
As shown in Figure 14, the graph shows the recorded data of the UAV's flight separately. It can be known that the flight altitude is 4.0 M and 4.1 M, and the flight distance is 2.0 M and 2.1 M, respectively, in a single flight, which coincides with the position of the measuring point. The flight altitude recorded by the UAV can help determine whether the drone has risen to the specified measurement altitude, and the flight distance recorded by the UAV can help determine if the drone is flying above the measurement point. In conclusion, it is possible to locate more accurately whether the current drone is in the position of the measurement point by using the flight data recorded by the UAV in real time and the image information returned from the cloud platform.
# A Robust Heuristics for the Online Job Shop Scheduling Problem
## 4 Our Approach
### Priority Queues at Machines
whilethere are unfinished jobsdo forach machine \(M_{i}\)do if\(M_{i}\) is idle and \(B_{i}\) is not emptythen  Calculate the priority \(\omega_{ji}\) for all operations \(J_{j}\in B_{i}\);  Compute \(\Omega_{ji}(t)=e^{\omega_{ji}(t)}\) for all operations \(J_{j}\in B_{i}\);  Select an operation \(O_{ji}\) from \(B_{i}\) (randomly, with probability \(\frac{\Omega_{ji}(t)}{\sum\int k\in B_{i}\Omega_{ki}(t)}\));  Assign \(O_{ji}\) to \(M_{i}\) and remove \(O_{ji}\) from buffer \(B_{i}\);  end for for each buffer \(B_{i}\)do  Recalculate the priority \(\omega_{ji}\) for all operations in \(B_{i}\); iftwo or more operations have the same priority \(\omega_{ji}\)then  Apply FIFO (First In First Out) rule;  end for  end for  Perform the scheduled operations on all machines;  Update the status of jobs, buffers, and machines;  end while
```  
**Algorithm 1**Pseudocode for Heuristic JSSP with Randomized Selection  
**Example 1**. : _The proposed heuristic algorithm prioritizes jobs based on calculated weights, allowing for probabilistic selection. Assume there are four jobs (\(A\), \(B\), \(C\), \(D\)) in the buffer queue, each with assigned probabilities reflecting their priority (see Figure 1). In this case, the most likely selection order is \(A\), \(D\), \(B\), \(C\), as shown in the initial queue state._  
_However, due to the randomness of the probabilistic selection mechanism, a lower-priority job may be selected occasionally, thus changing the typical order. In this example, operation \(C1\) of job \(C\), which has a lower probability of being selected, was processed earlier due to the stochastic nature of the algorithm._  
_The diagrams below illustrate the process:_The new formula has been tested on various JSSP problems and has shown promising results. By assigning weights based on the remaining processing time of each operation and the sum of remaining processing times of all operations, the proposed method has been able to improve the scheduling performance and obtain better results than the traditional uniform random function method. After implementing the new weight calculation formula, we found that our algorithm performed better than before, but we still saw some room for improvement. We realized that introducing some level of randomness in the weight calculation could help the algorithm explore the search space more effectively.
# Genetic Programming to Remove Impulse Noise in Color Images
###### Abstract  
This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment.  
digital image processing; color image denoising; genetic programming; impulse noise; adaptive filters +
Footnote †: journal: Medical Image Computing