esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios
## 2 Related Work
### Small Object Detection  
For small object detection scenarios, [PERSON] et al. [25] added a prediction head and replaced all prediction heads with the transformer prediction head structure to enhance the performance in detecting small objects. [PERSON] et al. [26] used a cross-layer attention network to obtain better features of small objects. [PERSON] et al. [27] acquired high-resolution features for small object detection by deconvolution and sub-pixel convolution. [PERSON] et al. [28] used a shuffle attention structure and fused different feature maps to enhance the ability to perform multi-scale detection. The above methods were developed by adding network structures to enhance the detection of small objects. However, this inevitably results in an increase in the training time and model parameters. Consequently, they are not suitable for UAVs due to their limited energy and hardware resources.  
Enhancing the performance of small object detection is achievable through the optimization of the feature enhancement and detection framework [11; 29; 30; 31]. [PERSON] et al. [11] proposed the single-shot multi-box detector (SSD), which uses multiple feature maps to predict objects at different scales. The shallow feature map is responsible for predicting small objects due to its high-resolution in the SSD. This is based on the consideration that shallow features with rich geometric information are very useful for the detection of small objects. [PERSON] et al. [29] employed multi-scale prediction by adding branches, and the high-resolution features are responsible for small objects in YOLOv3. [PERSON] et al. [30] added a prediction head to better retain small object feature information. By analyzing the relationship between the down-sampling of the input images and the features of small objects, [PERSON] et al. [31] removed the network structure of the deep-level detection head to reduce the effect of the features of small objects. As shown in Figure 1, the UAV image is down-sampled several times; Figure 1a has the lowest resolution due to multiple down-sampling processes, and Figure 1c has the highest resolution with less down-sampling. This indicates that the low-resolution feature map corresponds to large anchor boxes, which are more difficult to match to the ground-truth boxes of small objects. Meanwhile, the low-resolution feature map corresponds to the largest receptive field, causing some specific information about small objects to be lost.
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### RNA Im\({}^{5}\)C Modification in GBM  
In mammals, both DNA and RNA m\({}^{5}\)C are subject to oxidative processing by the ten-eleven translocation (TET) family (TET1/2/3), forming 5-hydroxymethylcycltidine (hm\({}^{5}\)C) and 5-formyl-cytidine (f\({}^{5}\)C), which are considered as mediating substances during the demethylation pathway [128]. However,  
Figure 2: Emerging functions of RNA modifications (except m\({}^{6}\)A) and their writers, erasers and readers in GBM. Abbreviations: Modifications: A-to-I, adenosine-to-inosine; m1A, N1-methyladenosine; m5C, 5-methylcytosine; m6 Am, N6, 2\({}^{\prime}\)O-dimethyladenosine; hn5C, 5-hydroxymethylcytosine; I, inosine; \(\psi\), pseudouridine. RNA-modifying proteins (RMPs): ADAR1/2/3, adenosine deaminase that acts on RNA 1/2/3; ADYFP, ALYREF export factor; DCP2, mRNA- decapping enzyme 2; DKC1, dyskerin pseudouridine synthase 1; DNMT2, DNA (cytosine-5-)-methyltransferase 2; METTLE4, methyltransferaselike 4; NSUN2/5, NOP2/Sun RNA methyltransferase2/5; PCIFI, phosphorylated CTD interacting factor 1; PUSI/3/7/10, pseudouridine synthase 1/3/7/10; PUSL1, pseudouridine synthase like 1; PUSI/1, pseudouridine synthase 7 like; RPUSD1/2/3/4, RNA pseudouridylate synthase domain-containing 1/2/3/4; TET1/2/3, ten-eleven translocation (TET) family; TRMT6/61A, tRNA methyltransferase6/61A; TRMT61B, tRNA methyltransferase61 homolog B; TRMT10C, tRNA methyltransferasease 10C, mitochondrial RNase P subunit; TRUB1/2, TruB pseudouridine synthase family member 1/2. Others: c-Myc, avian myelocytomatosis viral oncogene homolog; CDC14B, cell division cycle 14B; NQO1, Quaking gene isoform 6; HIF1A, hypoxia-inducible factor 1 alpha; Mods, modifications; p7056K, p70 56 kinase;Skp2, S-phase kinase associated protein 2; PDCD4, programmed cell death 4; Skp2, S-phase kinase associated protein 2.  
a recent study shows that m\({}^{5}\)C oxidation is a conserved process that could have critical regulatory functions inside cells [129]. For example, hm\({}^{5}\)C is distributed at lncRNA loci, involved in long-range chromatin interactions and positively correlated with lncRNA transcription [130]. TET1 is elevated in GBM [85], while TET2 expression is downregulated in glioma [86], and TET3 is epigenetically repressed in glioma [87]. 5-hydroxymethylcyclidine in DNA has been recognized as important markers in GBM [131; 132; 133], however, the direct evidence of hm\({}^{5}\)C modification in RNA has never been explored in GBM (Figure 2, hm\({}^{5}\)C part).
# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 5 Experiments and Analysis
### Results
The path is improved and the attacker node is recognized using ABC in conjunction with ANN and SVM. As a result, throughput rose and 61.38, 74.98, 88.71, 92.78, 91.82, and 91.62 were the average values examined for AODV under Threat, AODV, AODV with ABC, after prevention, using DT, and using RF classifiers, respectively. Delay represents the total time taken by the packets to arrive at the destination node (D) via the source node(s). The analyzed values of delay are shown in Table 6.  
Figure 15 depicts the delay values studied for various methodologies. The blue color, red color, green color, violet color, sky blue color, and the orange color reflects the values of delay under threat, AODV, AODV with ABC, after prevention from BHA, using DT, and using RF, respectively. The average delay utilizing AODV under danger, AODV without threat, AODV with ABC, after prevention, using DT, and RF was 0.2304 s, 0.145 s, 0.079 s, and 0.04 s, 0.046 s, and 0.048 s, respectively. The following hypothesis was established and tested using a t-test in order to validate the outcome before and after the application of the suggested work set. * Hypothesis  
**H0. ** There is no significant difference in the performance of the routing protocol, before or after the preventions structure. **H1. ** Otherwise. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Under** & **AODV** & **AODV** & **After Pre-** & **Using DT** & **Using RF** \\
**of Nodes** & **Threat** & **AODV** & **with ABC** & **vention** & **Using DT** & **Using RF** \\ \hline
100 & 0.123 & 0.112 & 0.062 & 0.033 & 0.0363 & 0.0375 \\
200 & 0.1504 & 0.123 & 0.0694 & 0.036 & 0.0392 & 0.0412 \\
400 & 0.2416 & 0.135 & 0.0767 & 0.043 & 0.0481 & 0.0496 \\
600 & 0.2527 & 0.145 & 0.0781 & 0.0431 & 0.0495 & 0.0499 \\
800 & 0.2718 & 0.156 & 0.892 & 0.0433 & 0.0501 & 0.0521 \\
1000 & 0.2781 & 0.17 & 0.904 & 0.0437 & 0.0512 & 0.0534 \\
2000 & 0.2956 & 0.178 & 0.922 & 0.0439 & 0.0523 & 0.0554 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average Delay(s). Figure 15: Delay. A \(t\)-test is a type of statistical test used to find out a significant difference between the means of two values analyzed before and after applying the optimization approach to detect malicious nodes. The analyzed values before and after the prevention algorithm are mentioned in Table 7.  
The average values analyzed before and after the prevention algorithm are summarized in Table 7, which proved the H0 hypothesis to be wrong.
# A Cluster-Based Optimal Computation Offloading Decision Mechanism Using RL in the IIoT Field
## 2 System Model
The task execution delay \(L_{d}(t)\) of a task created by device \(d\) in time slot \(t\) is given as:  
\[L_{d}(t)= \begin{cases}\frac{s_{d}(t)}{f_{d}}+W_{d}(t),&\text{when }o_{d}(t)=0\\ \frac{s_{d}(t)}{f_{m}}+\frac{v_{d}(t)}{R_{d,m}^{cell}(t)}+W_{m}(t),&\text{when }o_{d}(t)=1\\ \frac{s_{d}(t)}{f_{j}}+\frac{v_{d}(t)}{R_{d,j}^{d2}(t)}+W_{j}(t),&\text{when }o_{d}(t)=2\end{cases}  \tag{3}\]  
where \(o_{d}(t)\) denotes the offloading strategy for \(c_{d}(t)\). \(o_{d}(t)\) is determined by the CH of the cluster where the task occurs. \(o_{d}(t)=0\) indicates that \(c_{d}(t)\) is executed locally on the device where it first occurs, \(o_{d}(t)=1\) means that \(c_{d}(t)\) is offloaded to the MECS \(m\) (\(m\in M\)) with the least load, and \(o_{d}(t)=2\) denotes that \(c_{d}(t)\) is executed by device \(j\) (\(j\in D,\ d\
eq j\)) with the least load. \(s_{d}(t)\) denotes \(w_{d}(t)\cdot o_{d}(t)\), the number of CPU cycles required to process the task. Let \(f_{d}\), \(f_{j}\), and \(f_{m}\) be the computation capabilities of devices \(d\), \(j\), and MECS \(m\), respectively. \(W_{d}(t)\), \(W_{j}(t)\), and \(W_{m}(t)\) are the queuing delays of devices \(d\), \(j\), and MECS \(m\), respectively, in a time slot \(t\). In addition, as per the task execution delay (Equation (3)), the energy consumption of the device is defined differently according to the offloading strategy. When the task generated by device \(d\) in a time slot \(t\) is executed according to the offloading strategy \(o_{d}(t)\), the energy consumption with respect to the device is stated as follows:  
\[E_{d}(t)= \begin{cases}\varepsilon\cdot(f_{d})^{2}\cdot s_{d}(t),&\text{when }o_{d}(t)=0\\ p_{d,m}^{cell}(t)\cdot\frac{p_{d}(t)}{R_{d,m}^{cell}(t)},&\text{when }o_{d}(t)=1\\ \varepsilon\cdot(f_{j})^{2}\cdot s_{d}(t)+p_{d,j}^{d2d}(t)\cdot\frac{v_{d}(t) }{R_{d,j}^{d2d}(t)},&\text{when }o_{d}(t)=2\end{cases}  \tag{4}\]  
where \(\varepsilon\cdot(f_{d})^{2}\) denotes the energy consumption per CPU cycle and \(\varepsilon\) is a constant dependent on the hardware architecture [5].
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 2 Hybrid VLC/RF Networks in Indoor Scenarios under Ideal Conditions
### Network Selection and Resource Allocation

Table 4: Summary of the studies of load balancing in hybrid VLC/RF networks. In hybrid VLC/RF networks, resource allocation is closely related to network selection, as the selection of one of the two networks depends on the allocation of resources to each network, just as the allocation of resources to each network has a significant impact on network selection to optimize system performance [73]. An optimal combination of resource allocation and network selection enables overall system performance in terms of data rate, delay, and energy efficiency. Several types of research on resource allocation in hybrid VLC/RF networks are found in the literature [20; 74; 75; 76]. A study of resource allocation in two different scenarios of hybrid VLC/RF networks to maximize user throughput was recently performed in [74]. In the first scenario, a hybrid system was modeled in which VLC is powered by light waves to radiate only optical energy and the throughput maximization problem is formulated by optimizing the uplink time allocations. In the second scenario, time-switching is performed to simultaneously transfer information and power over the lightwave of the VLC network and a multi-objective optimization problem is formulated through a set of Pareto-optimal resource allocation policies. On the other hand, in [20] a resource allocation scheme that maximizes the data rate, taking into account user fairness was proposed for a VLC/RF network, with both networks served by a common backhaul and the channel state information able to be either perfect or imperfect. The authors of [75] developed a resource allocation algorithm with the objective of maximizing the energy efficiency (throughput per unit power) of the hybrid system. The results obtained by simulating the algorithm show that the energy efficiency can be increased if all the VLC access points are evenly distributed within the scenario. Lastly, in [76] a centralized algorithm for joint downlink resource allocation in a hybrid VLC/RF network was developed.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.3 Rateless Deluge and ACKLess Deluge  
These protocols are based on the Deluge protocol, and use rateless-coding to reduce the need for retransmissions [30]. Rateless coding, with random linear codes, allows a receiving node need only inform the transmitting node of the _number_ of packets received, rather than the _specific_ packets, in order for the sender to retransmit the required data (any \(m\) out of \(n\) packets being sufficient to recover the entire message). In addition, other nodes can overhear both the NACK and the coded result, and use this to recover missing data. Pre-coding of subsequent packets is used to reduce wait times. In ACKLess Deluge, forward error correction (FEC) is used to further reduce the need for retransmissions, by adding redundant packets to reduce the probability that any NACKs will be required. This also improves the efficiency of the pre-coding process. These improvements reduce energy use and latency, and improve scalability through reduced communications.  
Rateless and ACKless Deluge provide dissemination and activation of updates, with significant performance optimizations that might be useful for autonomous updates, but no other significant features supporting autonomous updates.
# Transmission Delay-Based Uplink Multi-User Scheduling in IEEE 802.11 ax Networks +
## 6 Performance Evaluation
### Software-Defined Radio Equipment-Based Experiments  
We verified the proposed method with SDR equipment-based experiments as shown in Figure 13. We deployed one Universal Software Radio Peripheral (USRP) as an AP and four USRPs as stations trying to transmit uplink data to the AP. The host computer performs uplink scheduling of stations, and scheduled information is transmitted to the stations through the Ethernet. After, selected stations perform uplink transmissions to the AP, which is connected to the computer for analyzing the received data. With the USRP-based testbed, we examined the channel use of simultaneous uplink transmissions.  
Figure 11: Network throughput performance (MCS of 80% stations changes, \(\lambda=1.9\)).  
Figure 12: Network throughput performance (MCS of 40% stations changes, \(\lambda=1.9\)).  
With the parameters in Table 1, channel use was measured by estimating the average transmission delay compared to the maximum among the scheduled stations.  
Figure 14 shows the experimental results of channel use in a simultaneous uplink scenario. The optimal re-clustering method performs re-clustering every time slot and produced the highest channel use performance. This method may significantly increase the overhead for the network with a large number of stations. However, in the experiments with four uplink stations, the optimal re-clustering method showed the best performance.  
The results showed that the proposed delay-based clustering with the re-clustering method shows higher channel use performance compared to the other methods, except the method performing re-clustering every time. Although the proposed method without re-clustering shows lower channel use performance than the proposed method with re-clustering, its performance is still higher than the round-robin selection or random selection methods. With the SDR-based testbed, we verified the efficiency of the proposed method for uplink multi-user transmissions.  
Figure 14: Channel use performance.  
Figure 13: Software-defined radio equipment-based testbed for uplink transmissions.
## 4 Computer Simulation Results
### Frequency Conversion to Radiation with the Wavelength 4 \(\mu\)m  
Here, we present the computer simulation results of the energy conversion from HFW to LFW, possessing wavelength 4 \(\mu\)m, occurring in the crystal AgGaS\({}_{2}\). The SOD coefficients are chosen as follows: \(D_{1}=-0.0002\), \(D_{2}=-0.0125\), \(D_{3}=-0.0217\). So, the frequency of LFW is near the zero-dispersion point and the SOD of the HFW is a large one. In our notations, the negative sign of the SOD coefficient corresponds to the normal dispersion of a medium. The coefficient of nonlinear coupling \(\gamma\) is chosen equal to unity, which is achieved at a value of \(6.12\,\mathrm{GW/cm^{2}}\) of the sum intensity of the incident pulses.
# High-Precision Chip Detection Using YOLO-Based Methods
## 3 Results
### Experimental Results
#### 3.3.3 DNMS Improvement Experiment  
In DNMS, there are four NMS thresholds to be set: regular parameters \(\alpha\) and \(\beta\) in Equation (10), left chip threshold \(a1\), and leaving chip threshold \(a2\). In this study, we conducted an orthogonal experiment to optimize parameter selection, with some of the better parameter combinations shown in Table 3. Ultimately, we set the left chip threshold at 0.5 and the leaving chip threshold at 0.4, \(\alpha=0.4\), and \(\beta=0.7\). The chip extraction results obtained using DNMS and the model presented in Table 2 are displayed in Table 4. As shown in Table 4, reference [28; 29] introduced the ghost module to YOLOv8n-seg and YOLOv5s, respectively. However, to ensure a fair comparison while maintaining a comparable number of model parameters, this work applied their enhancement methods to YOLOv8n and YOLOv5n, with all hyperparameters configured according to Ultralytics' recommendations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **[EMAIL_ADDRESS]** & **FLOPs** & **MS** & **FPS** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(G)** & **(MB)** & **FPS** \\ \hline YOLOv5n & 92.66 & 92.44 & 92.55 & 91.10 & 7.73 & 1.9 & 116.68 \\ YOLOv8n & 90.22 & 88.72 & 89.46 & 89.53 & 8.75 & 3.2 & 149.02 \\ YOLOv9t & 89.67 & 90.12 & 89.89 & 92.24 & 8.23 & 2 & 73.78 \\ YOLOv10n & 91.14 & 86.66 & 88.84 & 87.05 & 8.57 & 2.3 & 100.2 \\ YOLOv11n & **94.11** & **95.42** & **94.76** & **94.68** & 6.48 & 2.6 & 154.34 \\ GM-YOLOv11-backbone & 93.60 & 94.08 & 93.84 & 93.63 & 5.81 & 2.2 & 167.34 \\ GM-YOLOv11-head & 93.36 & 93.67 & 93.51 & 93.66 & 6.33 & 2.5 & 156.23 \\ GM-YOLOv11 & 93.80 & 93.96 & 93.88 & 93.81 & **5.72** & 2 & **173.41** \\ Literature [29] & 92.27 & 91.69 & 91.98 & 90.12 & 6.44 & **1.7** & 146.34 \\ Literature [28] & 91.22 & 90.63 & 90.92 & 90.29 & 7.84 & 2.8 & 135.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of chip detection for different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regular** & **Regular** & & \\
**Parameters \(\alpha\)** & **Parameters \(\beta\)** & **Left Chip** & **Leaving Chip** & **Recall** \\
**in Equation** & **in Equation** & **Threshold \(a1\)** & **Threshold \(a2\)** & **(\%)** \\ \hline
0.3 & 0.6 & 0.4 & 0.3 & 96.27 \\
0.3 & 0.7 & 0.5 & 0.3 & 95.89 \\
0.4 & 0.7 & 0.5 & 0.4 & **96.38** \\
0.5 & 0.4 & 0.6 & 0.5 & 95.75 \\
0.8 & 0.7 & 0.7 & 0.5 & 95.75 \\
0.9 & 0.6 & 0.7 & 0.6 & 95.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Partial results from the orthogonal experiment on the hyperparameters in the DNMS algorithm.
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 2 Related Work
### Luminance Adjustment in Low-Light Image Enhancement  
Luminance adjustment is the most important task in low-light image enhancement. Some methods exploit the structural characteristics of the generative network itself to directly enhance image lighting. Given the phenomenon that enhanced images are prone to overexposure or underexposure under different lighting conditions, [PERSON] et al. proposed the Retinex-based deep unfolding network URetinex-Net, which combines an implicit prior regularization model with Retinex theory to effectively suppress noise and retain more feature details [15]. The low-light image enhancement method based on the diffusion model improves the quality of low-light images by simulating light's diffusion and propagation process. ExposureDiffusion [16] solves this noise problem by integrating the diffusion and physically based exposure models. It proposes an adaptive residual layer using dynamic denoising strategies for areas with different signal-to-noise ratios. Some luminance adjustments are accomplished through adaptive brightness curves. [PERSON] et al. proposed a self-referenced depth adaptive curve estimation method (SelfDACE), which uses a neural network to map each pixel through a depth-adaptive adjustment curve to preserve the local image structure [17]. For more accurate luminance adjustment, some algorithms designed special luminance adjustment components. [PERSON] et al. were inspired by Retinex and proposed the KinD network [18]. The network consists of two components. One component is used to adjust the image brightness, and the other is used to remove image degradation. Based on KinD, [PERSON] et al. added a mapping function and proposed the KinD++ algorithm to make the enhanced image more realistic [19].
## 3 Results
### Confirmation of FRVE Detoxification and Main Components (Fustin and Fisetin) of FRVE
Notably, the FRVE and MT-treated groups showed significantly reduced histological features of nonalcoholic fatty liver compared to HFD-fed mice (Figure 4A). Furthermore, Oil-red O staining showed that hepatic lipid accumulation significantly increased in HFD-fed mice than in the NCD group. In contrast, FRVE and MT treatment markedly ameliorated hepatic intracellular lipid accumulation in HFD-fed mice (Figure 4B). Consistently, we observed elevated hepatic TG levels in HFD-fed mice and reduced TG levels in FRVE and MT-treated groups (Figure 4C). Figure 3: Effect of FRVE on serum liver function and lipid markers in HFD-induced nonalcoholic fatty liver model. Plasma (**A**) ALT, (**B**) AST, (**C**) TC, and (**D**) TG levels were analyzed by biochemistry analyzer. Values were expressed as mean \(\pm\) SD in each group (\(n=6\)). \({}^{*}\)\(p<0.05\) and ***\(p<0.001\) (compared to HFD control group). \(\#\)\(p<0.05\) and ***\(p<0.001\) (compared to NCD control group). FRVE Downregulated the mRNA Levels of Lipogenesis-Related Genes in HFD-Induced Nonalcoholic Fatty Liver Model  
RT-qPCR was used to determine the expression levels of proprotein convertase subtilisisin/kexin type 9 (PCSK-9), apolipoprotein B (APOB), sterol regulatory element-binding protein 1 (SREBP-1), fatty acid synthase (FAS), and sterol element-binding protein 2 (SREBP-2) in the liver tissue to assess the regulation of lipogenesis-related key factors by FRVE. Our results showed that the mRNA expression  
Figure 4: Effect of FRVE on hepatic steatosis and lipid accumulation in HFD-induced nonalcoholic fatty liver model. (**A**) The difference between each group by the morphology of the liver. Hepatic lipid droplet accumulation was assessed via H&E and Oil Red-O (ORO) staining. (**B**) Bar graph showed a percentage of hepatic lipid accumulation by Oil Red-O staining. * \(p<0.05\) (compared to HFD control group). *** \(p<0.001\) (compared to NCD control group). (**C**) Hepatic TG levels were analyzed by the biochemistry analyzer. Values were expressed as mean \(\pm\) SD in each group (\(n=6\)). * \(p<0.05\) and (compared to HFD control group). *** \(p<0.001\) (compared to NCD control group). levels of PCSK-9, APOBEC, SREBP-1, FAS, and SREBP-2 were higher in the HFD group than in the NCD group, whereas the mRNA levels were markedly downregulated in FRVE and MT treated groups (Figure 5).