esa-sceva/satcom-chunk-collection huggingface dataset:
# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 3 Experiment
### Adaptive Image Enhancement Module Experiment  
To validate the superiority of the SCINet image enhancement network in low-illumination detection scenarios, experiments were conducted on the ExDark dataset. Specifically, various deep learning-based image enhancement networks were integrated into the backbone of YOLOv8n, and their parameter counts and mean Average Precision ([EMAIL_ADDRESS]) were evaluated. The experimental results are presented in Table 2. In these experiments, the YOLOv8n model was combined with image enhancement networks, including Zero-DCE [44], RUAS [45], and SCINet [46], and subjected to end-to-end training. Compared to the baseline YOLOv8n model, all enhanced models exhibited improvements in [EMAIL_ADDRESS] (%). Notably, the integration of the SCINet network into YOLOv8n yielded the most significant enhancement, with [EMAIL_ADDRESS] increasing by 1.8%. The detailed results are shown in Table 2.  
In the study of low-light image enhancement, different image enhancement networks exhibit distinct characteristics and advantages. To provide a more intuitive evaluation of their performance under low-illumination conditions, four image enhancement networks, namely Zero-DCE, RUAS, EnlightenGAN, and SCINet, were assessed. The specific results are presented in Table 3. Experimental results on the same dataset demonstrate that SCINet outperforms the other methods in terms of both PSNR and SSIM metrics.  
We conducted experiments on both the original ExDark dataset and the enhanced version of the ExDark dataset, training for 200 epochs each. Feature maps from training results under extremely low-light conditions were extracted for visualization analysis, as  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Set** \\ \hline Batch size & 16 \\ Optimizer & SGD \\ Epochs & 200 \\ Learning Rate & 0.01 \\ \hline \end{tabular}
\end{table}
Table 1: Hyperparameter Settings for Model Training.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Parameters/M** & **[EMAIL_ADDRESS]** \\ \hline YOLOv8n & 3.01 & 68.3 \\ YOLOv8n + Zero-DCE & 3.02 & 68.5 \\ YOLOv8n + RUSA & 4.62 & 68.4 \\ YOLOv8n + SCINet & 3.01 & 68.6 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison Experiment of Low-Light Enhancement Network.  
shown in Figure 8. The experimental results indicate that the enhanced images exhibit more complex and detailed features compared to the original images, demonstrating that the adaptive image enhancement module effectively enriches the information content of the images.
# Interactive Part Segmentation Using Edge Images
## 5 Results  
### The IOU Accuracy of the Proposed Models for Each Edge Image  
Since our purpose was to train the proposed models with only the edge images instead of the color images, we conducted the first experiment to find out which edge image has the highest accuracy of object segmentation and how much the highest accuracy differs from the accuracy of the interactive segmentation model trained only with color images.  
In the first experiment, proposed model 1 was trained with the training data in PASCAL VOC 2012 [5] dataset and the SBD [6] dataset and evaluated with the evaluation data in PASCAL VOC 2012 [5] and the SBD [6] datasets. As shown in Tables 5 and 6, both RCF [18] and DexiNed [19] had the highest accuracy in the edge images of Layer 3, and the result edge images, calculated by finally combining the results of all layers from each edge detector, had the second-highest accuracy. Both edge detectors had the lowest accuracy in the edge image of the first layer. Comparing two edge detectors for each layer, DexiNed [19] had higher IOU accuracy than RCF [18] for all layers. The edge images of DexiNed [19] had less noise than the edge images of RCF [18] and the edge inside the object was more detailed than the edge of RCF [18], so the accuracy of object segmentation using DexiNed [19] was higher than when using RCF [18].  
The IOU accuracy of proposed model 1 trained with the edge images of DexiNed [19], which has several advantages over the edge images of RCF [18] was calculated when segmenting part from the PASCAL VOC Part dataset. As shown in Table 6, results similar to those obtained in the object segmentation experiment were obtained in the part segmentation experiment.  
In the part segmentation experiment, the IOU accuracy was the highest when the edge images from layer 3 were used, the IOU accuracy was the second-highest when the result edge images were used, and the IOU accuracy was the lowest when the edge images from the first layer were used. The mIOU is the average value of the IOU accuracy of each part.
## 4 Integrated FE/FTC  
### System Description  
Considering actuator faults and system interference, the fault model of EPS system is expressed as follows:  
\[\left\{\begin{array}{c}\dot{x}(t)=Ax(t)+Bu(t)+Ff_{a}(t)+D\epsilon(t)\\ y(t)=Cx(t)\end{array}\right. \tag{8}\]  
where \(x(t)\in R^{n}\) is the system state vector; \(u(t)\in R^{m}\) is input vector for the system; \(y(t)\in R^{p}\) is the system measurable output; \(\epsilon(t)\in R^{l}\) is the interference; \(f_{a}(t)\in R^{q}\) is the actuator fault vector; \(A\in R^{n\times n}\), \(B\in R^{n\times m}\), \(C\in R^{p\times n}\), \(D\in R^{n\times l}\), and \(F\in R^{n\times q}\) are the known constant matrix. The following hypotheses on the system (8) are made.  
**Hypothesis 1 (H1).** (\(A,C\)) _is observable, (\(A,B\)) is controllable and \(rank(B,F)=rank(B)=m\)._  
**Hypothesis 2 (H2).** _The \(\epsilon(t)\epsilon[0,\infty)\) and \(f_{a}(t)\epsilon[0,\infty)\), and \(f_{a}(t)\) are continuously smooth with bounded first-time derivative._  
**Remark 1.** _Hypotheses 1 and 2 are usually made for FE-based FTC systems with actuator faults. The condition \(rank(B,F)=rank(B)=m\) ensures that the influence of the fault can be compensated by controlling the input._The actuator fault is defined as an auxiliary state system, then System (8) is augmented into:  
\[\left\{\begin{array}{c}\dot{\overline{x}}(t)=\overline{A}\overline{x}(t)+ \overline{B}u(t)+\overline{D}\overline{\epsilon}(t)\\ y=\overline{C}\overline{x}(t)\end{array}\right. \tag{9}\]  
where \(\overline{x}t=\left[\begin{array}{c}x(t)\\ f_{a}(t)\end{array}\right]\), \(\overline{A}=\left[\begin{array}{cc}A&F\\ 0&0\end{array}\right]\), \(\overline{B}=\left[\begin{array}{c}B\\ 0\end{array}\right]\), \(\overline{C}=\left[\begin{array}{cc}C&0\end{array}\right]\), \(\overline{D}=\left[\begin{array}{cc}D&0\\ 0&l_{q}\end{array}\right]\), \(\overline{\epsilon}(t)=\left[\begin{array}{c}\varepsilon(t)\\ f_{a}(t)\end{array}\right]\).  
**Remark 2**.: _It can be verified below that the observability of Systems (8) and (9) is equivalent, since the matrix (\(A,C\)) is observable,_  
\[rank=\left[\begin{array}{c}sI_{n}-A\\ C\end{array}\right]=n,\forall s\in C,Re(s)\geq 0\]  
_which leads to_  
\[rank=\left[\begin{array}{c}sI_{n+q}-\overline{A}\\ \overline{C}\end{array}\right]=rank\left[\begin{array}{cc}sI_{n}-A&F\\ 0&sI_{q}\\ C&0\end{array}\right]=n+q,\forall s\in C,Re(s)\geq 0\]
## 4 System Model  
### Loss Path (LP) Model  
There are many models to simulate a telecommunication channel. The first one is the LP [10] model, which defines the range of a system when the transmitter and receiver are in line of sight (LOS). The safety system will be installed outside: as a first approximation, the range of the system can be predetermined by assuming the rail convoy and the Hi-Rail pickup are in line of sight (Figure 9).  
Figure 8: Illustration of the radio coverage simulation at Esker camp (latitude: 53.874178; longitude: \(-66.419625\)) on the railway between Sept-Iles and Schefferville.  
\[Pr=Pt.Gt.Gr\left(\frac{c}{4\pi d\text{Fc}}\right)^{2} \tag{1}\]  
where _Pt_: Transmitter power;  
_Pr_: Signal power at the receiver;  
_Gt_: Transmitting antenna gain;  
_Gr_: Receiver antenna gain;  
_c_: Propagation speed in the air;  
Fc: Carrier frequency.  
Fixing _Pr_ as the sensitivity of the receiver (_Sr_), d becomes the range (_R_) of the telecommunication system:  
\[R=\frac{c}{4\pi\text{Fc}}.\sqrt{\frac{Pt.Gt.Gr}{Sr}}. \tag{2}\]  
The antennas used [11] are specific for railways. We mention this choice now, during the theoretical study, because we need the antenna parameters to go ahead with range calculation. These are omnidirectional UHF antennas with a gain of 6 dB tuned to 915 MHz. For the system, with the XTend radio-frequency modules manufactured by the company Digi and the associated high gain antennas:  
\[\text{Fc}=915\text{ MHz};\,\text{\emph{Gt}}=Gr=6\text{ dB};\,\text{\emph{Sr}}=-100 \text{ dBm};\,\text{\emph{Pt}}=1\text{ Watt}.\]  
At a rate transmission of 125 kbps, the sensitivity is \(-100\) dBm for XTends boards: therefore, the ideal theoretical range of the system is from (2) of 328.565 km.  
Obviously, the propagation of the signal will not be in the line of sight and without any obstacles. Therefore, we must go a little further in the modeling.
## 3 Analysis and Discussion of Test Results
### XRD and SEM Results  
#### 3.4.1 XRD X-ray Diffraction Analysis  
Figure 13 shows the XRD pattern of remolded loess in a subgrade slope in Qilihe District, Lanzhou. The main mineral composition is quartz (SiO\({}_{2}\)), In addition, there are albionite (NaAlSi\({}_{3}\)O\({}_{8}\)), KAlSi\({}_{3}\)O\({}_{8}\), calcite (CaCO\({}_{3}\)), dolomite (CaMg(CO\({}_{3}\))\({}_{2}\)), muscovite (KAl\({}_{3}\)Si\({}_{3}\)O\({}_{10}\)(OH)\({}_{2}\)), kaolinite (Al2 Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\)), and chlorite ((Mg, Al, Fe)\({}_{6}\)(Si, Al)\({}_{4}\)O\({}_{10}\)(OH)\({}_{8}\)), and other crystal phases exist. Figure 14 displays the XRD patterns of the solidified soil with various polymers. Figure 13 shows that the primary component of the solidified soil remains quartz (SiO\({}_{2}\)) after the addition of geopolymers, which is consistent with the diffraction pattern of reshaped loess. However, there is a decrease in the diffraction intensity of minerals, particularly quartz (SiO\({}_{2}\)), albite (NaAlSi\({}_{3}\)O\({}_{8}\)), and calcite (CaCO\({}_{3}\)), which can be attributed to the increased proportion of geopolymers. Additionally, the presence of dense and low amorphous peaks suggests that the geopolymer has undergone hydration reactions and formed new substances. Under the stimulation of a sodium silicate alkaline agent, steel slag powder releases a significant amount of Ca\({}^{2+}\) ions. These ions then combine with the silicon (aluminum) oxygen tetrahedron formed through the hydration of fly ash, resulting in the formation of calcium silicate hydrate (C-S-H) and calcium aluminosilicate hydrate (C-A-S-H) gels in an amorphous state. The specific reaction formulae are shown in (3) and (4).  
\[Ca^{2+}+[SiO_{2}(OH)_{2}]^{2-}+H_{2}O\to CaO\cdot SiO_{2}\cdot H_{2}O \tag{3}\]  
\[3 Ca^{2+}+2[(OH)_{3}Al-O-SiO_{2}(OH)]^{3-}+H_{2}O\to CaO\cdot Al_{3}O_{2} \cdot SiO_{2}\cdot H_{2}O \tag{4}\]  
Figure 11: Disintegration rate curves of solidified soil with different terpolymer content under different curing periods.  
Figure 12: Disintegration phenomenon of the solidified soil containing 20% geopolymer at 300 min.  
Over time, the gelling substance continues to increase. This is especially evident when water glass is used as an alkaline activator, as it accelerates the hydration reaction of steel slag micropowder. This reaction promotes the formation of C-S-H and C-A-S-H at a faster rate. The gelatinous substances formed continuously fill the pores between the loess particles, resulting in a more compact specimen. Ultimately, this improves the overall stability and compressive strength of the solidified soil.
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 2 Pipeline Safety Monitoring Theory
### Leakage and Corrosion Distinction  
Both leakage and corrosion will cause the hoop strain variation. Generally, pipeline leakage occurs within a few seconds, and the instantaneous pressure drop causes the hoop strain to decrease. However, for pipeline corrosion, which directly leads to the pipeline wall thickness reduction, will induce the hoop strain increase in a few months or years. Thus, leakage and corrosion can be distinguished by the hoop strain decrease or increase.  
Based on the theory of hoop strain monitoring, pipeline leakage and the corrosion level can be measured. Therefore, whether the hoop strain can be measured accurately is very crucial. An FBG strain hoop sensor, which can be attached on the external surface of the pipeline and measure the overall circumferential deformation on the same cross-section, is designed and applied in the model tests.
## 3 Compaction Effect Analysis
### Compactability Characterization  
The previous sections have provided qualitative insights into the relationship between particle movement and compaction when considering mixing degrees. However, the strength of the correlation could not be adequately described. To address this, particle self-diffusion coefficients were introduced to quantify the association between displacements, system compactness, and mixing homogeneity.  
On a microscopic level, particle rearrangements, approaching, and void-filling are a result of random motions induced by external excitation. Loading leads to random velocity fluctuations, similar to molecular thermal diffusion or eddy diffusion in turbulence. These fluctuations in velocity result in a non-uniform distribution of force chains and the continuous formation and breakage of contacts, prompting rearrangements and void filling. This particle diffusion process under vibration signifies a key mechanism of compaction.  
Similar to the thermal velocities of gas molecules, the average squared magnitude of particle velocities is referred to as \"granular temperature\". [PERSON] et al. [29] computed the self-diffusion coefficient \(D\) by analyzing the growth rate of displacement variance over time, using the following formula:  
\[D=\frac{\left\langle\left[s(t)-s(0)\right]^{2}\right\rangle}{2t} \tag{17}\]  
where \(t\) is elapsed time; and \(s(t)\) and \(s(0)\) are particle displacements at times \(t\) and \(0\), respectively. In this study, \(t=130\)T was used.  
Figure 6 illustrates the values of D for different mixing degrees. A consistent decrease was observed with improved homogeneity, closely aligning with the void ratio trends shown in Figure 4. This indicated that the particle diffusion ability, which represented the range of single particle motion, decreased, and compatibility was reduced at higher mixing degrees.  
Figure 5: The average displacement and rotation angle of samples with different mixing degrees vary with load period: (**a**) displacement; and (**b**) rotation angle.
## 3 Results and Discussion  
### Severity in Autohydrolysis Treatment  
Figure 1 presents the heating and cooling temperature profiles for the nonisothermal autohydrolysis treatment of Paulownia leaves, petioles, and barks. In all cases, the temperature curves exhibited similar thermal behaviors between tested Paulownia fractions. Namely, the time and temperature influence on the autohydrolysis processing was analyzed based on the severity factors (Table 1). It should be indicated that severity values (S = log R\({}_{0}\)) were determined using 80 \({}^{\circ}\)C as initial and final temperature. As expected, severity values increased with increasing autohydrolysis temperature. At fixed temperature, no notable differences were identified between petioles, leaves, and barks treatments. This is consistent with the results previously reported for _Lentiuns edodes_[14] and wheat straw [21] treated using autohydrolysis at similar experimental conditions.  
Figure 1: Temperature profile of different experiment of Paulownia bark (dashed line), leaves (dotted line), and petioles (continuous line).
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 3 Methods
### Construction of Digital Fingerprint  
Table 2 presents the fingerprint generation process for ensuring data integrity. Key generation and exchange are performed by the logger. The logger generates a secret key (SK) and a public key (PK), storing the private key in a secure location and transmitting  
Figure 5: Encoded data using filter.  
Figure 6: Secure delivery of metadata and sensitive objects.  
the public key to the verifier. The verifier uses the public key for subsequent operations. The logger generates a hash value for each video frame, signs it, and constructs a hash tree with 30 video frames, transmitting the root hash value. The verifier then conducts hash verification using the root hash, detecting any video frames with integrity violations.
# A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering
## 6 Conclusions  
In this paper, we have proposed an effective imputation method for missing data to improve the poor recommendation accuracy due to data sparsity in CF. The proposed method, k-RRI, is a recursive imputation method based on reliability. We have also proposed a new similarity measure that weights common interests and indifferences between users and items. As a result, the proposed k-RRI has achieved the following advantages over existing methods: (1) poor imputation can be avoided because the threshold values are adjusted depending on the reliability level required in each step; (2) the importance of missing data is reflected in imputation because priority is given to the nearest neighbors with important information on the data to be predicted; and (3) a sufficient number of candidate data for imputation can be ensured because the data imputed in the preceding step are used in each step. We compared the prediction accuracy of k-RRI with EMDP and AutAI through the experiments, using the benchmark dataset, MovieLens. Experimental results show that the prediction accuracy of k-RRI outperforms existing methods by 13.8%.  
As for the future work, we are planning to improve the processing time of k-RRI by developing an efficient neighbor searching method according to the reliability. We are also planning to study an algorithm that automatically determines parameters such as the number of steps, the similarity threshold, and so on. Furthermore, we are planning to incorporate the trust constraint into the proposed method in the future work as trust-awareness can also alleviate the data sparsity problem and further improve the accuracy of our method. Lastly, we are planning to apply the proposed method in various fields. For example, airplane services can greatly benefit from the proposed method considering that achieving high recommendation accuracy in airplane services is essential to satisfy customers.  
[PERSON] and [PERSON] designed methodology and wrote the original draft. [PERSON] and [PERSON] shared their expertise concerning the overall paper. [PERSON] reviewed the paper and helped with editing it. [PERSON] supervised the entire process. All authors have read and agreed to the published version of the manuscript. This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2016-0-00406, SIAT CCTV Cloud Platform). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable.  
The authors declare no conflict of interest.
# Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection
## 5 Results and Discussion
### Impact of In-Distribution Complexity on OOD Detection Performance  
In this section, we delve into an intriguing observation regarding the performance of our OOD detection method concerning the complexity of the in-distribution data. Specifically, our experiments reveal a notable distinction in performance when the in-distribution data comprises 10 classes as opposed to 100 classes. One key aspect influencing OOD detection is the complexity of the feature space that the model needs to learn. In scenarios where the in-distribution data are derived from 10 classes, the feature space is inherently simpler and less diverse. This simplicity facilitates the model's capacity to discern outliers or anomalous patterns associated with OOD samples. With a higher number of classes (100 classes), there is an increased risk of the model overfitting to class-specific features during training. This overfitting might hinder the model's generalization to OOD samples that deviate from these specific class features. The presence of a larger number of classes introduces the potential for class imbalance, where some classes may have fewer instances than others. Class imbalance can impact the model's ability to generalize, particularly when confronted with OOD samples that do not align with the more prevalent classes. The augmented number of classes introduces more intra-class variability, posing a challenge for the model to capture generalizable features. This variability may adversely affect OOD detection, given that the model grapples with distinguishing between unusual patterns and normal intra-class variations. A higher number of classes typically leads to more intricate decision boundaries. The model may encounter difficulties in delineating between classes, potentially hindering its ability to generalize effectively to OOD samples.
## 3 Results and Discussion
### Performance Analysis
#### 3.1.2 Cross-Layer Solution  
The proposed scheme is designed on cross-layer metrics from MAC layer to application layer, which selects the best route by achieving high probability and low time delay as well as channel availability. For instance, a source vehicle \(S\) wants to establish a reliable route to the destination vehicle \(D\). In terms of the prediction of topology map in Figure 4, the flows from \(S\) to \(D\) can be split into multiple paths (\(S\)\(\rightarrow\)C\(\rightarrow\)F\(\rightarrow\)D, \(S\)\(\rightarrow\)A\(\rightarrow\)B\(\rightarrow\)D, \(S\)\(\rightarrow\)G\(\rightarrow\)K\(\rightarrow\)L\(\rightarrow\)D and \(S\)\(\rightarrow\)G\(\rightarrow\)C\(\rightarrow\)F\(\rightarrow\)D). Suppose Vehicle \(C\)'s available bandwidth is less than the given threshold value that means it has not enough channel bandwidth to forward data, so the paths, i.e., \(S\)\(\rightarrow\)C\(\rightarrow\)F\(\rightarrow\)D and \(S\)\(\rightarrow\)G\(\rightarrow\)C\(\rightarrow\)F\(\rightarrow\)D are not available for the specified business type. In addition, vehicle \(L\)'s reception probability in path \(G\)\(\rightarrow\)K\(\rightarrow\)L\(\rightarrow\)D is less than the threshold probability, hence, this path also cannot be used. Finally, the path \(S\)\(\rightarrow\)A\(\rightarrow\)B\(\rightarrow\)D is selected as the reliable path at the next cycle.  
If the Dijkstra algorithm is used in Figure 3, the shortest path could be \(S\)\(\rightarrow\)C \(\rightarrow\)f\(\rightarrow\)D in terms of inter-vehicle distance. However, selecting this path will lead to link quality degradation or even link damages since the available bandwidth of node C is not enough. Therefore, compared to the Dijkstra algorithm, our algorithm performs better.  
Figure 4: A flowchart representing two cases of local controllers finding a final route.