esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 2 Proposed Hybrid Harris Hawks-Arithmetic Optimization Algorithm
### Algorithm Complexity  
It is crucial to note that the complexity of the proposed HHAOA algorithm is heavily influenced by three critical processes: initialization of the transition stage, fitness function evaluation, and position updating of Hawks. The initialization process has a complexity of C(n) when there are n hawks. Updating the location vector of all hawks involves searching for the optimal location, which can be complex. This involves C(T \(\times\) n) + C(T \(\times\) n \(\times\) D) operations, where T represents the maximum number of iterations and D is the dimension of the current particular problem in hand. Therefore, it is imperative to understand that the computational complexity of HHAOA can be expressed as C(n \(\times\) (T + TD + 1)).  
```
0: Random WMN routers positions and manually calculated FOPPI controller parameters.
0: Optimal WMN connection and FOPPI parameters
1: Initialize the search agents positions \(y_{j}\) (\(j\)= 1, 2, 3,..., N)
2: Check search space boundary and initiate the \(t\leq T\) case
3:while (\(t\leq\) T) do
4: Attain the initial solution \(y_{B}\)
5:for (All hawks (\(y_{j}\))) do
6: Amend the position update
7: Use (4) to update the E
8: Phase: Exploration
9:if\(|E|\geq\) 1 & \(q\geq\) 0.5 then
10:if\(c\geq\) 0.5 then
11: Update the solution using Equation (1) condition 1
12:else if\(c<\) 0.5 then
13: Update the solution using Equation (1) condition 2
14:endif
15:else if\(|E|\geq\) 1 & \(q<\) 0.5 then
16: Update the solution using Equation (1) condition 3
17:endif
18: Phase: Exploitation
19:if\(|E|<\) 1 then
20:if\((|E|\geq\) 0.5 and r \(\geq\) 0.5) then
21:if\(c<\) 0.5 then
22: Solution update using Equation (5) condition 1
23:else if\(c\geq\) 0.5 then
24: Solution update using Equation (5) condition 2
25:endif
26:else if\((|E|\geq\) 0.5 and r \(<\) 0.5) then
27:if\(c<\) 0.5 or \(c\geq\) 0.5 then
28: Solution update using Equation (7) condition 1
29:else if\(c<\) 0.5 or \(c\geq\) 0.5 then
30: Solution update using Equation (7) condition 2
31:endif
32:else if\((|E|<\) 0.5 & r \(\geq\) 0.5) then
```  
**Algorithm 1** Pseudocode of HHAOA technique.  
```
33:if\(c<0.5\)then
34: Solution update using Equation (8) condition 1
35:elseif\(c\geq 0.5\)then
36: Solution update using Equation (8) condition 2
37:endif
38:elseif\((|E|<0.5\) & r \(<0.5)\)then
39:if\(c<0.5\) or \(c\geq 0.5\)then
40: Solution update using Equation (9) condition 1
41:elseif\(c<0.5\) or \(c\geq 0.5\)then
42: Solution update using Equation (9) condition 2
43:endif
44:endif
45:endif
46:endfor
47:\(t=t+1\)
48:endwhile
49:Return\(Y_{B}\) for optimal WMN connectivity with all the routers and optimal FOPPI controller parameters for real-time pressure process plant.
```  
**Algorithm 1**_Cont._
# FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
## 3 Technical Background
### Basics of the Multi-Layer Perceptron (MLP) Model  
MLP is a fully connected neural network that works solely in the forward direction [32]. A typical MLP network contains at least one hidden layer among the input and output layer, with the task of performing all computations. The number of hidden layers acting as a computational engine can be arbitrarily decided depending on the specific application demand. The overall duty of the MLP relies on the production of a single output as a result of the multiplication of the input by the weights through an activation function. A backpropagation objective is employed in the training phase of the neurons, thereby empowering the network to further increase its accuracy. This algorithm identifies the weights in the hidden layer, alleviating the prediction errors. One of the most critical scientific limitations is the high burden of computational complexity due to the fully connected structure resulting in too many parameters. An example demonstration of a single-layer neural network and MLP can be observed in Figure 2.
# Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
## 3 Results and Discussion
### Comparison with Human Evaluations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Similarity-Based Methods** & **A** & **B** & **C** & **D** & **E** \\ \hline Correlation coefficient & 100 & 68 & 51 & 36 & 12 \\ \hline Pixel coincidence degree & 100 & 82 & 61 & 55 & 32 \\ \hline Cosine similarity & 100 & 94 & 89 & 92 & 85 \\ \hline The human evaluation & 98 & 81 & 59 & 41 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of evaluation results. From the data illustrated in Table 5 and Figure 14, we see that the correlation coefficient method performed the best in evaluation, exhibiting an ability closest to that of humans. The evaluation results of this method were in good agreement with the human results. In addition, the scores converted from the correlation coefficients also showed a good degree of differentiation for recognizing the characters written by five subjects with different writing levels. Meanwhile, the scores converted from the pixel coincidence degree were also very close to the human scores. However, the maximum deviation of the pixel coincidence degree method was over 20% and a little larger than that of the correlation coefficient-based method. Nonetheless, this method can also be regarded as acceptable. The scores converted from the cosine similarity varied from 85 to 100. The most scrawled characters written by subject E got an average score of 85, which was quite different from the score given by the experts. It should be noted that the scores on the characters written by subjects C, D, and E were all quite different from the corresponding human scores as well. This means the cosine similarity method had little ability to distinguish the normative level of Chinese characters. Finally, the results recognized by the neural network verified a comprehensive evaluation. For the 100 characters in five different normative levels written by one person, the recognition accuracy was above 90%. For the 400 characters in three different normative levels written by four different persons, the recognition accuracy was above 80%.
## 5 Performance Evaluation
### Performance Evaluation for the Delay per Packet  
Figure 15 presents the delay experimented for the packets for the routing protocols in the simulation experiments. Both GPRS and GPRS-Q result in the lowest delay because they successfully transfer packets only when the destination is an immediate neighbor of the sender. Spray and Wait results in better performance due to its high data dissemination method. LAROD-LoDiS performed a bit worse than Spray and Wait but better than LECAR and LER. As previously observed, multiple copies of the same packet exist in the network, contributing to this better result. The relatively poor delivery ratio might also be a contributor. In addition, LECAR does not concentrate on quick packet delivery. Instead, it focuses on achieving the highest packet delivery using minimum energy. Hence, LECAR takes a comparatively long time to route the packet while avoiding congested paths and expending a minimum number of transmissions. However, when the number of UAVs increases, the delay drops significantly, resulting in a similar delay for Spray and Wait. This  
Figure 14: Performance comparison of the routing protocols regarding the number of transmissions per packet when the buffer size is (**a**) 25 MB and (**b**) 50 MB.  
Figure 13: Performance comparison of the routing protocols regarding the number of copies per packet when the buffer size is (**a**) 25 MB and (**b**) 50 MB.  
phenomenon is also evidence of the efficacy of LECAR. In contrast, LER takes a bit more time than LECAR because it follows congested paths from time to time due to its lack of awareness about the congestion situation.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 5 Detailed Elaboration of Each Layer
### Functions of Space Base Stations  
At RRSC, service area planning and spectrum scheduling dictate how SBSs allocate resources such as slots, beams (and their direction), frequencies, and power. These allocations are based on a fair principle, ensuring that the SBSs can meet the diverse service data needs of various user terminals. The specific functions of resource scheduling at SBSs are as follows:  
Service area and radio resource configuration: The RRSC coordinates with SBSs to optimize network performance, directs SBSs to adjust their service areas--expanding or contracting coverage based on demand and congestion--and configuresradio resources according to its comprehensive service area and spectrum planning, which involves allocating specific frequency bands to minimize interference and maximize network efficiency.  
User clustering: User clustering is carried out to optimize resource allocation in multi-beam frequency multiplexing or beam-hopping scheduling scenarios. The process considers the service capacity of each beam and the capacity requests of users within the service area of an SBS. Users in a cluster share the same beam resource, while inter-cluster resources are coordinated based on service demands. The number of users in a cluster can be adjusted according to demand changes across different scheduling cycles.  
Beam management: Based on the principles of fairness and capacity maximization, SBSs have the functions of beamforming and beam-hopping pattern calculation. Beam-forming helps to direct signals towards specific users, while beam-hopping patterns are calculated to suppress mutual interference among beams. These functions also enable SBSs to schedule user access slots efficiently.  
User parameter management: Link parameter scheduling is carried out for user terminals, considering the terminal type and link status. The transmission parameters for each user link include power, frequency, and modulation and encoding (MODCOD) sets. These parameters are optimized to accommodate changes in the link status and improve user link capacity. By optimizing these parameters, we can ensure better performance and reduce errors in transmission.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 3 Results
### Distribution of Tree Heights  
The trees here correspond to vegetation with a minimum height of 25 m. Thus, the distribution of tree heights (Table 4) shows that the maximum height of trees across all sites is 65.06 m in November 2018 and 64.07 m in April 2019. The trees have an average height that ranges from 34.29 m to 37.00 m in November 2018 and from 34.63 m to 36.88 m in April 2019. The tallest trees are over 35 m tall and are found in Sites 9 and 11.
## 4 Experimental Results and Discussion
### Discussion  
In this paper, we propose a compound controller for the high-precision trajectory tracking control of the DEAP actuator. It consists of a feedforward hysteresis compensator (i.e., the inverse MPI model) and a feedback controller (i.e., the novel DTSMC). In order to achieve the high-precision trajectory tracking control, the inverse MPI model is first applied to compensate the hysteresis of the DEAP actuator, and then the proposed DTSMC is employed to overcome the estimation error \(\mathbf{D}(k)\) and other perturbations. From Figure 10 and Table 6, it can be seen that the DSMC and the proposed DTSMC can achieve good tracking control when the input signal frequency is 0.1 Hz, that is to say, the hysteresis characteristic is well compensated, and the estimation error \(\mathbf{D}(k)\) and other perturbations are well overcome. From Figures 11-15, and Table 6, one can see that, whether the input signal is a high frequency or a hybrid frequency, the proposed DTSMC can always achieve excellent tracking  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Frequency/Hz** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.1/0.2/0.3/0.4/0.5** \\ \hline \multirow{2}{*}{RMSE (mm)} & DSMC & 0.0565 & 0.0942 & 0.1054 & 0.1514 & 0.2226 & 0.1536 \\  & DTSMC & 0.0536 & 0.0612 & 0.0750 & 0.0761 & 0.0847 & 0.0500 \\ \multirow{2}{*}{MAE (mm)} & DSMC & 0.0207 & 0.0504 & 0.0788 & 0.1255 & 0.1834 & 0.0990 \\  & DTSMC & 0.0122 & 0.0215 & 0.0314 & 0.0389 & 0.0456 & 0.0242 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The tracking-error results of the DSMC and DTSMC controllers at different frequencies.  
Figure 15: The tracking results under the hybrid frequency input signal. (**a**) Displacement output. (**b**) Tracking error. (**c**) Desired displacement versus actual displacement of the DSMC. (**d**) Desired displacement versus actual displacement of the DTSMC.  
control performances. In contrast, the tracking errors of the DSMC will be significantly increased with increasing input signal frequency, and it has large overshoots at the turning points. To summarize, the proposed DTSMC is effective for the high-precision trajectory tracking control of the DEAP actuator. It provides faster error convergence, more precise trajectory tracking control, less chattering and better robustness than the DSMC. However, it should be pointed out that the nonlinear disturbance term upper bound and its change rate need to be known in the design process of the DTSMC, which are difficult to achieve in practice. In future work, we will try to apply the adaptive technology to completely compensate the uncertainty of unknown upper bound. Another possible research topic of this work may be the design of an adaptive discrete-time terminal sliding mode control without a inverse hysteresis compensator to simplify the design process of the controller and optimize the controller performance.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 2 System Model  
The C-RAN architecture under study is illustrated in Figure 2. In this system, the BBUs communicate with multiple user equipments (UEs) belonging to \(M\) cells through \(M\) single-antenna Edge Nodes (ENs). The BBUs are co-located in the so-called BBU pool so that joint processing can be performed, while the ENs are geographically distributed. In particular, we assume here that cells are arranged in a line following the conventional circulant Wyner model ([35], Chapter 2), and each cell contains two single-antenna UEs with different service constraints: one eMBB user and one URLLC user.  
Due to the strict latency constraints of URLLC traffic, the signal for the URLLC UEs is decoded on-site at the EN, while the eMBB signals are forwarded to the BBU through a multi-channel analog fronthaul. In this hybrid cloud-edge architecture, the mobile operator equips the EN with edge computing capabilities in order to provision the services required by the URLLC user directly from the EN. Following the A-RoX concept, the end-to-end channel from the eMBB UEs and the BBU pool is assumed to be fully analog: the EN performs only signal amplification and frequency translation to comply with fronthaul capabilities and forwards the signals to the BBU, where centralized decoding is performed. In practice, as detailed later in the paper (see Sections 4 and 5), we assume that each EN hosts a digital module, responsible for URLLC signal decoding, and an analog module, responsible for the mapping of received radio signal over the analog fronthauling, which is identified as Analog-to-Analog (A/A) mapping.  
While the technology used for the analog fronthauling can be either fiber-optics (A-RoF), wireless (A-RoR) or cable (A-RoC), the system model proposed in this paper reflects mainly the last two solutions. Furthermore, we will focus on A-RoC, and we will use the corresponding terminology to fix the ideas (see Figure 2).
# Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
## 3 Results
### Brain Signal Classification
#### 3.1.3 Epileptic Focal Detection with Limited Data
Most of the classification methods for epileptic EEG signals typically extract features from the raw signals. In general, a conventional yet effective classifier, such as an SVM, requires fewer samples than a deep neural network. Thus, increasing the number of samples in the feature-domain is a strategy to balance positive and negative samples. In the case of epileptic iEEG data, positive (focal) samples are much fewer than negative samples, and this imbalance data can deteriorate the performance of classification accuracy. [PERSON] et al. [56] successfully applied data augmentation based on an adaptive synthetic oversampling approach (ADASYN) [66] to balance an in-hospital dataset of epileptic interictal iEEG signals to identify the seizure onset zones automatically. Figure 4: An artificial signal generation with the DCT. (**a**) Seven intracranial iEEG signals at either focal or non-focal area. (**b**) DCT coefficients in the spectrum-domain. The spectra are segmented into seven physiological sub-bands, and the sub-band components extracted from each spectrum are merged to create an artificial spectrum. (**c**) The inverse DCT leads to the resulting artificial signal.
## 4 Proposed Protocol Design
### Network Assumptions  
The major assumptions of the networks are (i) All sensor nodes are aware of its exact location in the targeted sensing region. (ii) All sensor nodes are identical in terms of mobility, memory, bandwidth, battery life, and compression technique. (iii) The transmission range of the sensor nodes can be adjusted depending on the distance between them (iv) The base station is located at the sensing zone border and the entire sensor node can interact directly with BS.
## 3 Architecture of the Platform and Related Software and Hardware Aspects
### Implementation Aspects and Associated Configuration and Monitoring Processes
#### 3.2.3 Hardware Details
Given the need for mobility, the most flexible way to support the experiments is by using power banks. In view of the power consumption requirements just discussed, a 5000 mAh power bank can enable more than 9 h of experimentation before a need to recharge, which is more than enough for typical experiments, such as those presented in the paper. The cost of an RPi unit, including a typical microSD card providing 16 GB of storage, is less than 60 USD. The external Wi-Fi module costs less than 10 USD, while a GPS unit (of characteristics comparable to those mentioned above) can be purchased at prices around 15 USD. Even when an additional 20 USD for a 5000 mAh power bank is counted in, the total cost per node is kept below 100 USD. This cost is low enough to permit flexible platform deployments, free from any scalability concerns.
## 3 Data and Data Processing
### GNSS Data Analysis  
Continuous GNSS measurements in the study area were obtained by a single station (KAPN) of the Turkish National Permanent RTK Network (CORS-TR) (Figure 1), which has been operated by the General Directorate of Mapping (GDM) since 2008. We processed daily Receiver Independence Exchange (RINEX) observation files using GAMIT/GLOBK V10.70 software [51]. The analyses were carried out in two steps. In the first step, the relative coordinates were estimated based on the weighted least squares algorithm using the ionosphere-free linear combination (LC) of the phase observable by the GAMIT module. The orbital and clock parameters were obtained from International GNSS Service (IGS), and the minimum constraint (with respect to the ANKR site) procedure was used for ambiguity fixing in 5 cm for both horizontal and vertical directions. In the second step, the reference frame definition was performed for the daily solutions using the GLRED module. Then, a 7-parameter Helmert transformation was applied and its parameters were estimated utilizing 10 IGS stations (ANKR, ARUC, BSHM, HAMD, ISTA, MATE, NICO, ORID, TUBI, and ZECK) with coordinates and velocity defined in ITRF14.
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
###### Abstract  
Semantic segmentation of roads in remote-sensing images is a challenging task. This paper proposes a semantic segmentation model, DANet, for remote-sensing image road semantic segmentation. The model addresses the problems of missing, misclassification, and strong jaggedness of segmented target edges faced by other semantic segmentation networks when dealing with complex and diverse remote-sensing images. The proposed model uses two ASPP structures for multi-scale feature fusion and combines the DarkNet network structure for downsampling with the SegNet network structure for upsampling. This improves the model's ability to extract road feature information from remote-sensing images. Using the CHN-CUG Roads Dataset, we have confirmed that the proposed network structure, Re, has demonstrated a 1.15% improvement in accuracy compared to U-Net. Furthermore, the road IoU has shown a 1.09% enhancement compared to HRNet-V2. Additionally, there is a 1.13% increase in F1-score compared to U-Net.  
semantic segmentation; remote-sensing images; roads; dual-ASPP modules Article
## 3 Problem Formulation
### Data
#### 3.1.1 Path Description  
Optical channels (lightpaths) have been described by attributes. The hop_lengths attribute gives the length of each hop that forms an optical channel from its origin transponder to the end. This is an important property because the signal-to-noise ratio depends on the length of the fiber that connects the two transponders. There are usually more active and commercial wavelengths in each such edge (hop). This is because these wavelengths are used by paths other than the ones occupied by the path under consideration. All these optical channels in an edge (hop) interact with each other through so-called nonlinear phenomena, such as four-wavelength mixing, which affects the quality of transmission.  
With this in mind, the num_of_paths_in_hops attribute was considered, which gives the number of neighboring wavelengths (optical channels) of the network in a given edge. The attribute hop_losses denotes the attenuation and, therefore, the optical losses in the considered edge. Again, hop losses affect the signal-to-noise ratio. Hence, the corresponding property is included. Another property, number_of_hops, denotes how many edges occur in a given optical channel from the origin transponder to the destination transponder. The number of hops affects the signal-to-noise ratio due to the optical regeneration taking place at the network node.  
The last two properties are closely related to the specific type of transponder used. The transponder_modulation property stores information about the modulation format of the transponder, e.g., 64 QAM or QPSK. Since the modulation format is closely related to the sensitivity of the receiver, this property is, therefore, very important. On the other hand, the property transponder_bitrate is quite self-explanatory and provides the bit rate of a given transponder. The bit rate of the transponder also affects the sensitivity of the receiver and is therefore also included.  
Figure 3: Network subsection illustrating the meaning of the specific channel attributes occurring in the studied database.  
Figure 2: An example of optical network topology.
# Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter
###### Abstract  
Nanopore detection technology has now developed into an indispensable tool for single molecule experiments, in which useful information on molecular properties can be obtained from the ion current flow induced by individual molecules, but the relatively high background noise affects the data analysis. Therefore, a nanopore signal noise reduction method based on wavelet transform and Kalman filter is proposed, which can achieve multi-scale decomposition and optimal estimation of the nanopore signal. The real measurement shows that the Kalman filter based on the wavelet mode maxima method reduces the root mean square (RMS) of the background noise by 17.8%, and the noise reduction effect is better than the traditional Kalman filter method.  
nanopore; wavelet transform; Kalman filter; noise reduction