esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 5 Conclusions  
In this paper, we propose a technique based on Retinex theory and local steering kernel to improve low-light images. To effectively improve the low-light images and address the color mismatch problem, the proposed technique estimates the illumination component of the low-light images by using the mean and distribution constants, and the improved images are obtained by using the Retinex theory. Then, color correction is used to highlight the hue of the improved image, and a denoising algorithm based on the local steering kernel is used to obtain the final image.  
To evaluate the performance of the proposed algorithm, low-light images from the LOL dataset are used to compare it to existing methods. In terms of visual assessment, the existing method improves the overall illumination of the resulting image and amplifies the pixel values in dark areas, revealing hidden objects. The results are superior to those of existing methods, exhibiting sharper color contrast and less distortion. Additionally, during the process of enhancing low-light areas, the proposed method effectively suppresses the amplified noise signals. For quantitative evaluation, the LOE method is used to compare the naturalness of the resulting images, and the proposed method shows improved results with lower LOE values compared with existing techniques.  
Conceptualization, B.-W.C. and N.-H.K.; software, B.-W.C.; validation, N.-H.K.; formal analysis, B.-W.C. and N.-H.K.; investigation, B.-W.C.; data curation, B.-W.C.; writing--original draft preparation, B.-W.C.; writing--review and editing, B.-W.C. and N.-H.K.; visualization, B.-W.C. and N.-H.K.; project administration, N.-H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 5 Distributed Protocols
### PCDs  
Based on Zipf's law, with the ever-growing increase in content delivery services (for instance Spotify, Netflix, Hulu), it is found that out of all available content on any streaming sites, only a small percentage of content accounts for a majority of the requests/streams over and over. In [67], the authors addresses this issue with a scheme called popular content downloading scheduling (PCDS) for the mmWave networks. It is an extended version of the previously discussed D2D [45] protocol.  
In the PCDS protocol, the small cells in the mmWave architecture consist of one AP, and the others are users. Both the AP and users are rigged with steerable directional antennas. Thus, it is ensured that any two nodes can execute a directional propagation. In the 60 GHz wavelength, two approaches are used for neighbor discovery, named direct discovery and gossip-based discovery [68] for PCDS. The nodes can have a maximum of one established link with the surrounding nodes.  
Popular contents are downloaded from the AP and are distributed to the users. This distributive operation operates sequentially. In the sequence, the scheduling and transmission phase mechanisms are similar to the other two-stage protocols such as the FD-MAC. Even the non-overlapping time dividing sequence is also maintained on PCDS. Furthermore, the packets or contents which the users download are sourced from the network layer.  
* Advantages: The PCDS protocol incorporates a heuristic transmission path selection algorithm technique for establishing multi-hop transmission paths, which hasexcellent utilization of D2D communications and spatial reuse. Moreover, PCDS has outstandingly short delay and significant throughput performance according to numerous simulations.
* Disadvantages: Even though the overall performance is excellent, the computational complexity is high for the PCDS protocol. Also, adjacent lines cannot be scheduled for concurrent transmissions due to the half-duplex assumption in the PCDS protocol. Consequently, connections that share similar nodes cannot be scheduled together.
## 3 Results  
### Color and Chlorophyll Content Analysis  
The Hunter L*, a*, b* values and chlorophyll contents of green and black pepper from India and Sri Lanka are shown in Table 1. The chromaticity analysis results showed that GPI and GPS had high L* (48.21 \(\pm\) 0.04 and 49.47 \(\pm\) 0.06) and b* (19.16 \(\pm\) 0.01 and 19.43 \(\pm\) 0.02) values and low a* (\(-0.95\pm 0.02\) and 0.27 \(\pm\) 0.05) values, respectively. Compared to green pepper, black pepper from India (BPI) and Sri Lanka (BPS) had high a* values (3.44 \(\pm\) 0.01 and 3.64 \(\pm\) 0.02) and low L* (42.03 \(\pm\) 0.01 and 36.66 \(\pm\) 0.03) and b* (11.43 \(\pm\) 0.01 and 12.35 \(\pm\) 0.02) values, respectively. The color of green pepper is closely associated with the content of chlorophyll [(29)]. GPI had the highest chlorophyll a content (28.97 \(\pm\) 0.09 mg/L), followed by GPS (21.92 \(\pm\) 0.07 mg/L), BPI (13.68 \(\pm\) 0.01 mg/L), and BPS (7.15 \(\pm\) 0.02 mg/L). The mean chlorophyll b contents in GPI and GPS were 15.41 \(\pm\) 0.06 mg/L and 8.62 \(\pm\) 0.03 mg/L, respectively. The chlorophyll a and b contents of green pepper were higher than those of black pepper.
## 2 Basic Concepts and Work Scope
### Fingerprinting-based Positioning
In 2D, with ground truth position coordinates \((x_{gt,\mathcal{F}},y_{gt,\mathcal{F}})\), and estimated position coordinates \((\hat{x}_{\mathcal{F}},\hat{y}_{\mathcal{F}})\), for a set of fingerprints \(\mathcal{F}\), the error distance, \(e_{\mathcal{F}}\), is  
\[e_{\mathcal{F}}=\sqrt{\left(x_{gt,\mathcal{F}}-\hat{x}_{\mathcal{F}}\right)^{ 2}+\left(y_{gt,\mathcal{F}}-\hat{y}_{\mathcal{F}}\right)^{2}}. \tag{1}\]  
Figure 5: Fingerprints-to-position mapping using a pattern-matching method. Throughout this article, we use \(\mathcal{F}\) to represent a set of fingerprints. We use the same notation to either represent the set of fingerprints that forms the elements of a vector of fingerprints associated with a position or to represent all of the fingerprints that can form multiple fingerprint vectors, associated with multiple positions.
# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 3 The Proposed Hybrid Deep Learning Approach
### BERT-Based Encoder for Image Segmentation Dataset
#### 3.2.1 Segmentation Output  
The segmentation process aims to produce a mask that identifies the regions of the cotton leaf affected by disease. The output embeddings \(Z_{i}\) from the BERT-based encoder are fed into a segmentation head, which typically consists of a series of up-sampling layers. This generates a pixel-wise classification mask that distinguishes diseased areas from healthy regions. The segmentation mask is expressed as:  
\[M=\text{SegmentationHead}(Z) \tag{4}\]  
where \(M\) is the segmentation mask, and \(Z\) is the matrix of encoded patch representations.  
To optimize the segmentation process, a combination of Dice loss and Cross Entropy loss is utilized, balancing the need for accurate boundary delineation and pixel-wise classification accuracy. The combined segmentation loss is given by:  
\[L_{\text{seg}}=\lambda_{1}\times\text{Dice Loss}(M,G)+\lambda_{2}\times\text{ Cross Entropy Loss}(M,G) \tag{5}\]  
Here, \(\lambda_{1}\) and \(\lambda_{2}\) are weights that balance the contributions of each loss component, and \(G\) is the ground truth segmentation mask.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 5 System Implementation and Experiment Results  
In this section, the design and implementation details of the STSC system are discussed. The integration of the RSU controller and traffic signal controller is introduced, and the integration test is presented. Unit and functional test of OBU and RSU communication, and demonstration of the field experiment in EVSP are presented in this section.
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 3 Results and Discussion  
### Results of the Tests  
Four linear path-tracking tests are designed under different initial states (\(d\),\(\theta\)). Variables \(d\) and \(\theta\) are the same as the previous, and the initial deviation settings should be representative to make the test results credible. The initial deviation contains several limit deviations of the prototype in actual operation, namely, (1 m, 90\({}^{\circ}\)), (1 m, 0\({}^{\circ}\)), (1 m, \(-\)90\({}^{\circ}\)), and (0 m, 90\({}^{\circ}\)). The tracking accuracy and convergence efficiency evaluation indicators are the same as the above simulation test.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 3 System Model
### Load-Balancing Model  
After migrating the containers on all faulty devices to healthy devices, we need to evaluate the overall load-balancing degree of the network from two aspects: (1) the resource utilization balance of the same type of resources among edge devices as defined in Definition 1, which is denoted by \(U\), and (2) the resource balance of different types of resources on the edge device, denoted by \(V\). The load-balancing formula is as follows:  
\[Load_{balance}=U_{balance}^{total}+V_{balance}^{total} \tag{3}\]  
Let \(R\) denote the type set of all resources on the device, expressed as \(R=\{r_{1},r_{2},\ldots r_{n}\}\). For any resource type \(r_{k}\), the resource utilization of \(r_{k}\) in the edge network occurs after the container on the faulty device migrates. The equilibrium degree is the variance of the resource utilization of \(r_{k}\) on all edge devices. The method  
Therefore, the remaining resource equalization degree of edge device \(n_{j}\) can be expressed as follows:  
\[V_{balance}^{j}=\sum_{\forall\tau_{h_{1}},\tau_{h_{2}}\in R}max\left\{0,\frac{S_{ j}^{k_{1}}}{W_{j}^{k_{1}}}-\frac{S_{j}^{k_{2}}}{W_{j}^{k_{2}}}\right\} \tag{7}\]  
Here, \(\frac{S_{j}^{k_{1}}}{w_{j}^{k_{1}}}\) represents the proportion of the remaining available \(k_{1}\)-th resources on \(n_{j}\) to the total number of resources on the \(k_{1}\)-th resource. This formula reflects whether the utilization of the various resource types in \(n_{j}\) is balanced.  
Therefore, the total remaining resource balance degree of the edge network is the sum of the remaining resource balance degrees for all edge devices. The calculation method is shown as follows:  
\[V_{balance}^{total}=\sum_{n_{j}\in I}V_{balance}^{j}=\sum_{n_{j}\in I}\sum_{ \tau_{1},\tau_{2}\in R}max\left\{0,\frac{S_{j}^{k_{1}}}{W_{j}^{k_{1}}}-\frac{ S_{j}^{k_{2}}}{W_{j}^{k_{2}}}\right\} \tag{8}\]  
It can be seen from the above definition that the smaller the values of the resource utilization balance degree and the remaining resource balance degree, the more balanced the load of each device in the network.
## 4 Results and Discussions
### Feasibility of a New Discrimination Characteristics Test Method  
In this experiment, we used an FC as a neutron detector. The FC is coated with UO\({}_{2}\), a fissile material. When a neutron interacts with the UO\({}_{2}\), it produces fission fragments. Since the UO\({}_{2}\) emits alpha particles for alpha decay, pulses derived from alpha particles become background noise [20]. Figure 12 shows the discrimination characteristics graph created using the proposed method with pulse heights collected at 0.01 W reactor power. In Figure 12, a region where the counts remain constant between 0.03 V and 0.08 V is obtained, indicating the separation of pulses from alpha particles and neutrons. Figure 13 represents the spectrum of the FC obtained with the MCA-8000D, and the pulses from alpha particles and neutrons are separated around 0.05 V, indicating the validity of the discrimination characteristics graph shown in Figure 12.  
Figure 11: FPGA output with respect to reactor output.  
Figure 12: Discrimination characteristics graph obtained using the proposed method.  
In conventional methods, obtaining the discrimination characteristics graph required changing the discrimination level and collecting counts, which took a considerable amount of time. However, with the proposed method, the graph can be created in a very short time, enabling a reduction in workload for maintenance through the utilization of a digital technique.
## 2 Materials and Methods
### Valid Pixels and Plant Functional Types  
The yearly MODIS land cover type dataset (MCD12Q1, Collection 5.1 IGBP Land Cover) at 0.5\({}^{\circ}\) spatial resolution from 2001 to 2012 [35], as well as the aggregated GPP dataset, were used to identify the valid satellite-image pixels in the Northern Hemisphere. This involved using the following criteria: at least 10 years of data records of the constant land cover type; less than 20% gap of data records of GPP in each year; summer active ecosystem of which summer (June-August) GPP was the highest and winter (December-February) GPP was the lowest in an annual cycle. Additionally, non-vegetation types (e.g., built-up, barren) and permanent wetlands were also excluded. Then, each valid pixel was assigned to a plant functional type (PFT), which was defined by both the Koppen climate classification [36] and MODIS IGBP land cover type. For example, boreal evergreen needleleaf forests is referred to as D2-ENF, of which D2 indicates continental subarctic or boreal climates, and ENF indicates evergreen needleleaf forests (Table 1). After filtering, a total of 27,484 valid pixels within 33 PFIs in the Northern Hemisphere were included in our present work, and the details and spatial distribution of PFTs are shown in Table 1 and Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Climate Types** & **Köppen Climate Classifications** & **Plant Types** & **MODIS Land Cover Types** \\ \hline \multirow{2}{*}{B} & Dry (arid and semiarid) climates: & \multirow{2}{*}{ENF} & \multirow{2}{*}{Evergreen Needleleaf Forests} \\  & _BWh/BWk/BSh/NSk_ & & \\ \hline \multirow{2}{*}{C1} & Dry-summer or Mediterranean & \multirow{2}{*}{EBF} & \multirow{2}{*}{Evergreen Broadleaf Forests} \\  & climates: _Csa/Csb_ & & \\ \hline \multirow{2}{*}{C2} & Humid subtropical climates: & \multirow{2}{*}{DNF} & \multirow{2}{*}{Deciduous Needleleaf Forests} \\  & _Craw/Cfa_ & & \\ \hline \multirow{2}{*}{C3} & Maritime temperate climates or & \multirow{2}{*}{DBF} & \multirow{2}{*}{Deciduous Broadleaf Forests} \\  & Oceanic climates: _Cwb/Cwc/Cfh/Cfc_ & & \\ \hline \multirow{2}{*}{D1} & Hot or warm summer continental & \multirow{2}{*}{MF} & \multirow{2}{*}{Mixed Forests} \\  & climates or hemiboreal climates: & \multirow{2}{*}{SHR} & \multirow{2}{*}{Closed Shrublands, open Shrublands} \\  & _Dsa/Dsb/Dwa/Dwb/Dfa/Dfb_ & & \\ \hline \multirow{2}{*}{D2} & Continental subarctic or boreal & \multirow{2}{*}{WSA} & \multirow{2}{*}{Woody Savannas, savannas} \\  & climates (with extremely severe & \multirow{2}{*}{GRA} & \multirow{2}{*}{Grasslands} \\  & winters): _Dsc/Dsd/Dwc/Dwd/Dfc/Dfd_ & & \\ \hline \multirow{2}{*}{E} & Polar and alpine climates: ET & \multirow{2}{*}{CRO} & \multirow{2}{*}{Cropland, cropland/Natural vegetation mosaic} \\ \cline{1-1} \cline{5-5}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of the climate and plant types in this study based on the Köppen climate classifications and the MODIS IGBP land cover types, respectively. The details regarding the codes of Köppen climate classifications are provided by [PERSON] and [PERSON] [36].
## 4 Guideline for Applications  
### Requirements  
In order to implement the proposed method for real-world editing expert services, the proposed method should be divided into two processes, online and offline, as it is computationally demanding and time-consuming to execute every time the editing service is requested. The estimation process of \(\hat{F}\) can be done in advance as an offline process, while the recommendation should be carried out in real-time as an online process. The process is described in Algorithm 1.  
```
INPUT: User feedback logs (users, experts, user feedback), a user \(u_{i}*\). OUTPUT: The optimal editing expert \(e_{j}*\) to be recommended with \(u_{i}*\). Training phase (Offline) (Step 1: Constructing a feedback rating matrix \(F\)) (Step 2: Performing MF on \(F\) to estimate \(\hat{F}\)) Test phase (Online) (Step 3: Recommending the optimal editing expert based on \(\hat{F}\)) An expert is selected whose feedback rating is highest in the \(i*\)-th row of \(\hat{F}\).
```  
**Algorithm 1** Editing expert recommendation process  
For the real-world implementation, it is important to find the maximum possible number of unknown values in a feedback rating matrix that does not degrade performance as a previous study reported that the performance of a method is not fully dependent on the amount of data but on the  
Figure 10: Surface plots illustrating the frequencies of user feedback logs according to the sentiment scores of review texts (\(x\)-axis) and the (**a**) original ordinal ratings or (**b**) estimated feedback ratings (\(y\)-axis).  
ratio of unknown values over known values [33]. We denoted the ratio of the unknown values over the known values by \(\rho\) as Equation (15).  
\[\rho=N_{u}/N_{k}, \tag{15}\]  
where \(N_{u}\) and \(N_{k}\) respectively indicates the number of unknown and known values in a feedback rating matrix. Therefore, experiments were conducted using diverse \(\rho\)s to find the maximum possible number.  
Figure 11 shows the results in terms of RMSE (\(y\)-axis) according to \(\rho\)s (\(x\)-axis). We can observe that RMSE was steady when \(\rho\) was smaller than 4. However, RMSE increased rapidly as \(\rho\) got bigger. Therefore, it is recommended for companies utilizing the proposed method to maintain \(\rho\) under 4.  
Lastly, we evaluated the time duration for performing expert recommendation by using the proposed method. We utilized a server with 3.5 GHz Intel Core i7 and repeatedly ran the online process of the proposed method for 20 times under the same environment. As a result, the average service time for online process was 0.78 seconds which was short enough for actual implementation.
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 6 Results and Discussions
### Sinr  
Figure 11 presents performance of the proposed ICIC scheme in term of SINR. The figure also shows performance of RF1, SFR, fuzzy logic system [27] and uniform transmit power schemes for comparison. We chose to present SINR performance as cumulative distributions that show the range in which SINR values belong. Recall that SINR is calculated according to Equation (1) (Section 3). The thermal noise was \(-174\) dBm. We drew the cumulative distribution functions of SINR for these methods from results shown in [27].  
The figure clearly shows that our proposed ICIC scheme performed better than the other ICIC schemes with SINR values in the range \([-90,\,-40]\). Fuzzy logic system scheme followed with most SINR values in the range \([-90,\,-80]\). Fuzzy logic system outperformed RF1 and SFR, which had SINR values in the range \([-90,\,-87]\). However, SFR had better performance than RF1. Uniform transmit power scheme had the worst performance among the all of the schemes.  
RF1, SFR and Fuzzy logic system have in common the partitioning of the bandwidth, which has a disadvantage. A user may not be allocated a resource block because there is no resource block available in its area, while at the same time a resource block may be unused in another area. This will negatively impact the performance of the network in terms of packet loss.  
Figure 11: Cumulative Distribution function of SINR. Proposed ICIC scheme was clearly suboptimal.
## 2 Review Available Literature
### AA5052-Based MMCs
When compared to the base material, the processed UFGed nanocomposite increased hardness by up to 140%, yield stress by up to 75%, and ultimate tensile strength by up to 60%. A mixed ductile-brittle rupture behaviour was seen in the fractographic characteristics, with the ductile phase being more prominent and maintaining up to 30% of the nanocomposite elongation. A dislocation-based model was used to examine the tensile flow behaviour of the processed nanocomposite, and the results showed that grain boundary strengthening is the main mechanism at play. [PERSON] et al. [38] studied an AA5052-carbon-fibre composite's microstructural, tribological, and mechanical properties. The fabrication of carbon-fibre-reinforced AA5052 bulk composites using multi-pass FSP was successfully used in this work to enhance AA5052 wear resistance. According to microstructure tests, there was no apparent Al\({}_{4}\)C\({}_{3}\) layer between the carbon fibres and the parent material in the composites, where they were uniformly distributed across a considerable volume. The composite's carbon-fibre orientation was random, due to the considerable plastic deformation caused by FSP. Further mechanical testing revealed that the hardness of the composite improved by 46.8 percent when compared to the parental metal, and the composite produced at 1000 revolutions per minute and 75 mm per minute had an 18.6 percent higher UTS and a 13.0 percent greater elongation than the parental material. The wear tests demonstrated that the composite's wear process was more stable, with wear volume loss decreased by more than 70%. Carbon-fibre GNDs, fracture deflection, and load transfer have all been connected to enhanced mechanical properties. [PERSON] et al. [39] investigated how titanium dioxide nano-particle reinforcement affected AA5052 using FSP. A 6-mm square pin tool was used with various parameters, such as a spindle rotational speed of 700 revolutions per minute, 1000 revolutions per minute, and 1300 revolutions per minute, as well as a traverse rate of 50 mm per minute, 65 mm per minute, and 80 mm per minute. At rotational and traverse speeds of 1000 mm per minute and 65 mm per minute, respectively, the maximum hardness value of 78 \(\pm\) 1 Vickers hardness and tensile strength value of 193.1 \(\pm\) 3 MPa were reached. The grain boundaries of the cast aluminium alloy 5052 were found to have micro-voids. Because of dynamic recrystallization generated by tool movement during the procedure, micro-voids were eliminated following friction stir processing. Because of dynamic recrystallization during the procedure, the grain size of the AMMCs decreased. Hardness was improved up to a 1000 rpm tool-rotating speed due to grain refinement and homogenous reinforcement distribution. However, after 1000 rpm, there was a decrease in hardness. According to the authors, excessive centrifugal force caused the reinforcement to be thrown away from the stir zone, resulting in a drop in hardness. During the specimen's tensile test, a ductile fracture was discovered.