esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 3 Experiments and Results
### Network Ablation Study
The \(mAP\) was calculated based on the area enclosed by the precision-recall curve and the axes, representing the performance of the network. The precision-recall curve displays recall on the \(x\)-axis and precision on the \(y\)-axis. A larger area enclosed by the curve and the axes indicates better model performance. The precision-recall curves for the three models, YOLOv8s, YOLOv8s-EC, and YOLOv8s-UEC, are shown in Figure 14. The black curve represents YOLOv8s, the red curve represents YOLOv8s-EC, and the blue curve represents YOLOv8s-UEC. Observing the three curves, the area under the blue curve is the largest, followed by the red curve, and the black curve has the smallest area. Clearly, as the model is continuously improved, the area under the curve increases, providing an intuitive representation of the enhanced network performance. To further verify the performance of YOLOv8s-UEC, we trained other advanced networks, such as Faster R-CNN, YOLOX-s, YOLOv5s, and YOLOv8s, using the established underwater defect dataset. Each experimental group was trained until convergence to ensure optimal results, as shown in Table 4. The detection accuracy of YOLOv8s-UEC improved by 4.9%, 1.6%, 1.0%, and 1.4% compared to Faster R-CNN, YOLOX-s, YOLOv5s, and YOLOv8s. This detection capability met the requirements for efficiency and precision, demonstrating higher application value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Precision** & **Recall** & \(F_{1}\) & \(mAP_{0.5}\) & **FPS** \\ \hline Faster R-CNN & 93.7\% & 78.9\% & 0.8567 & 90.3\% & 39 \\ YOLOv-s & 91.3\% & 90.6\% & 0.9095 & 93.6\% & 84 \\ YOLOv5s & 93.4\% & 91.0\% & 0.9218 & 94.2\% & 101 \\ YOLOv8s & 91.6\% & 90.0\% & 0.9079 & 93.8\% & 121 \\ YOLOv8s-UEC & 93.9\% & 91.6\% & 0.9274 & 95.2\% & 107 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection results of other advanced networks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Name** & **Crack** & **Spalling** & **Exposed Bars** \\ \hline YOLOv8s & 86.3\% & 97.9\% & 97.1\% \\ YOLOv8s-ER & 87.5\% & 97.6\% & 97.2\% \\ YOLOv8s-EC & 87.9\% & 97.8\% & 97.3\% \\ YOLOv8s-IEC & 89.9\% & 98.0\% & 97.5\% \\ YOLOv8s-UEC & 89.3\% & 98.4\% & 97.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of \(mAP_{0.5}\) in different network for various defects.
# Anodic Oxidation of 3D Printed Ti6 Al4V Scaffold Surfaces: In Vitro Studies
## 3 Results and Discussion
### In Vitro Studies  
#### 3.2.1 Cell's Metabolic Activity and Proliferation  
The metabolic activity and cell proliferation of Saos-2 cells cultured on Ti\({}_{6}\)Al\({}_{4}\)V, Ti6 Al4V ac., and Ti6 Al4V ao scaffolds were assessed, as depicted in Figure 8. Despite on Day 1, enhanced metabolic activity was observed for Ti6 Al4V ao samples, it remained unvarying during the time of culture (Figure 8a). In contrast, the metabolic activity of cells cultured on top of Ti6 Al4V and Ti6 Al4V ac. significantly increased during the 14 days. No differences were observed between Ti6 Al4V and Ti6 Al4V ac. scaffolds. In the case of cell proliferation, the same trend was observed as for metabolic activity (Figure 8b). A higher proliferation rate was observed for Ti6 Al4V ao scaffolds after 1 day of culture than Ti6 Al4V and Ti6 Al4V ac. scaffolds, but it remained unaltered on the subsequent days. In contrast, cells cultured on Ti6 Al4V and Ti6 Al4V ac. scaffolds proliferated during the 14 days of culture. Comparing the cell proliferation rates of the Ti6 Al4V and Ti6 Al4V ac. Scaffolds, no differences were observed. In the study developed by [PERSON] et al. [30], it was also observed that Ti6 Al4V scaffolds with a similar structure presented no deleterious effect on cells' metabolic activity and proliferation.  
Figure 8: Metabolic activity and proliferation of Saos-2 cells. (**a**) Metabolic activity of cells cultured on top of developed scaffolds over 14 days. (**b**) Saos-2 cells’ proliferation during 14 days of culture (\(\delta\) represents statistically different from Day 1; \(\varphi\) represents statistically different from Day 3; \(\varepsilon\) represents statistically different from Day 7; \(\#\) represents statistically different from Day 14, \({}^{*}\) represents statistically different from the indicated group).
# Validation of Smartphone Sway Analysis for Fall Prevention
## 2 Materials and Methods  
### Subjects  
Thirty-three healthy college students volunteered to be part of this study. The purpose of the study was explained to each one of them and they consented to participate. They were questioned about their health status and judged to be free from any condition that could affect their balance, as well as not being under the effects of any medication that may affect their state of mind (and therefore their balance) during the acquisition of data. Subjects were asked to remove their shoes but were allowed to wear socks during the data collection process. Subjects' height ranged from 1.65 m to 1.93 m with an average height of 1.75 m (SD 0.09 m). The weight ranged from 556 N (56.7 kg) to 938 N (96.5 kg) with an average weight of 712 N (SD 131 N) (72.6 kg, (SD 13.37 kg). Eight subjects were female and twenty-five subjects were male. All subjects were of a healthy (meaning none were obese or underweight as per body mass index, 23.4 kg/m\({}^{2}\) (SD 2.87 kg/m\({}^{2}\))) weight and height so as to not affect the balance and sway results of the smartphone application in comparison to the utilized force plate.
## 2 Experimental Setup  
Figure 1 shows the proposed fiber laser experimental setup. The linear cavity is formed by two cascaded tunable fiber Bragg gratings (TFBG) in one side and a Hi-Bi FOLM in the opposite side. The gain medium is an EYDCF (Coractive DCF-EY-10/128) (Coractive High-Tech Inc., Quebec, QC, Canada) with length of 3.3 m. The EYDCF is pumped at 976 nm by a multimode high-power laser source (Focuslight Technologies Inc., Xi'an, China) through a beam combiner. A 90/10 coupler is used as the laser power output, measured at the 90% output coupler port (output 1). An optical isolator (ISO) is used to protect the pump source of back light emission. The Hi-Bi FOLM consists of a 50/50 coupler forming a loop with output ports interconnected by a Hi-Bi fiber with length of \(\sim\)52 cm and birefringence of 4.125 \(\times\) 10\({}^{-4}\). The Hi-Bi FOLM whose exhibits a wavelength-dependent periodical transmission spectrum is used as broad bandwidth wavelength reflector. The splices between the Hi-Bi fiber and the 50/50 coupler output ports were placed in mechanical rotation stages to adjust the fringe contrast of the reflection/transmission output spectrum [18]. The Hi-Bi fiber was placed on a Peltier device in order to perform a wavelength displacement of the Hi-Bi FOLM output spectrum by temperature variation applied on the Hi-Bi fiber loop [17; 18; 19]. The input port (output 2) is used to measure the Hi-Bi FOLM transmission spectrum and the laser spectra with an optical spectrum analyzer (OSA, Yokogawa AQ6375) (Yokogawa Corporation of America, Newman, GA, USA) with spectral resolution of 0.03 nm. The output 2 is also used to detect SQS pulses by a photodetector (Thorlabs, Newton, NJ, USA) and observed on an oscilloscope (Tektronix, Beaverton, OR, USA). TFBG1 and TFBG2, used as narrow bandwidth wavelength reflectors, exhibit -99% of maximum reflection at central reflection wavelengths of 1548.6 nm and 1543.86 nm, respectively. The TFBGs were placed in a micrometric screw mechanical system for wavelength tuning by mechanical strain application [17; 18; 19].
## 3 Results and Discussion
### Computational Overhead
#### 3.1.3. Decryption  
Figure 8 shows the comparison of encryption generation for different access schemes. It is observed that the time for generating keys increased concerning attributes. Multi-authority and attribute-based encryption requires a longer time for decryption than the proposed MCP-ABE-BG for 100 attributes. This shows that the proposed MCP-ABE-BG utilizes the same amount of time as encryption for 100 attributes.
# MINC-NRL: An Information-Based Approach for Community Detection
## 5 Experiments
### Benchmarks
[33] to extend modularity to the more general case of overlapping communities, defined as:  
\[Q_{ov}=\frac{1}{m}\sum_{c\in C}\sum_{i,j\in V}\bigg{[}\beta_{l(i,j),c}A_{ij}- \frac{\beta_{l(i,j)}^{out}k_{i}^{out}\beta_{l(i,j)}^{in}k_{j}^{in}}{m}\bigg{]} \tag{17}\]  
Same as that in \(EQ\), \(m\) is the total number of edges in the network. \(A_{ij}\) are the elements of the adjacency matrix. \(i\),\(j\) are nodes of the network. \(k_{i}^{out}\) is the out-degree of Node \(i\), while \(k_{j}^{in}\) is the in-degree of Node \(j\). Instead of the number of communities to which a node belongs to, \(Q_{ov}\) uses the belonging coefficients \(\beta_{l(i,j)}^{in}\) and \(\beta_{l(i,j)}^{out}\) to calculate the weight for each link \(l(i,j)\) existing between node \(i\) and \(j\). Similar to \(EQ\), a higher value of \(Q_{ov}\) indicates a stronger community structure. Another index used in our experiment to evaluate the quality of overlapping community detection result is the average conductance (AC). Conductance is a local measure for the goodness of a node cluster in the network [35]. For a cluster \(c\), conductance is defined as:  
\[f(c)=\frac{m_{c}^{out}}{2m_{c}^{out}+m_{c}^{in}} \tag{18}\]  
where \(m_{c}^{in}\) is the number of internal edges of Cluster \(c\), \(m_{c}^{out}\) is the number of edges that links the cluster to other parts of the network. To evaluate a partition of the network, average conductance (AC) is used, which is defined as:  
\[\Phi(C)=\operatorname*{avg}_{c\in\mathbb{C}}f(c) \tag{19}\]  
where \(C\) is a community partition of a network. The equation averages the conductance of all communities of the partition.
# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 5 Conclusions  
Global forest fires are increasingly frequent, and the challenges associated with their control have risen. Failure to address them promptly results in significant financial losses and safety issues. Flames exhibit a greater diversity and complexity in both morphology and dynamic features compared to other objects, and this is particularly challenging, due to the mutual occlusion of trees in forests. Hence, the identification and recognition of fire types, coupled with corresponding fire suppression measures, hold not only developmental potential but also practical importance.  
In this work, an enhanced model for forest fire classification and detection is presented, leveraging the YOLOv8 architecture. By modifying the C2f network structure and integrating Deformable Convolution and SCConv, resulting in \(DCN\_C2f\) and \(SCConv\_C2f\), the model achieves adaptive adjustment of the receptive field. This leads to a reduction in parameters and FLOPs while enhancing feature representation. The incorporation of Coordinate Attention enhances the Detect section, improving the capturing and utilization of features, consequently enhancing fire detection accuracy. WIoU v3 is incorporated into the loss function for bounding box regression, utilizing a dynamic mechanism that is non-monotonic to establish a more rational distribution strategy for gradient gain. Our experimental results showcased the model's superiority in forest fire performance compared to the YOLOv8 algorithm. Our future research will focus on optimizing the model, particularly in balancing accuracy and detection speed within the fusion module, and deploying the model on drones equipped with different cameras. Additionally, our plans involve expanding the dataset size to enhance fire detection accuracy.  
Conceptualization, Z.Y., Y.S., Y.W. and J.L.; Data Curation, Z.Y.; Formal Analysis, Z.Y. and Y.S.; Funding Acquisition, J.L.; Investigation, Z.Y.; Methodology, Z.Y. and Y.S.; Project Management, Y.S.; Resources, Y.S.; Software, Z.Y.; Supervision, J.L.; Validation, Y.W.; Visualization, Y.S.; Writing--Original Draft, Z.Y.; Writing--Review and Editing, Z.Y. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant No. 12361029, and the Chinese National College Students Innovation and Entrepreneurship Training Program, Grant No. DC20231669.  
Not applicable.  
The data supporting the conclusions of this study are accessible from the corresponding author upon reasonable request. The data are not publicly available due to privacy.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 2 Optical CDMA with MUI Cancellation  
### Optical CDMA System and MPSC  
In optical CDMA systems using LEDs, intensity modulation and direct detection are employed at the transmitter and the receiver, respectively. Figure 1 illustrates a model of the optical CDMA system in which the proposed inverted MPSC is applied. Information bits from each user's information source, where \(1\leq k\leq N_{\mathrm{L}}-1\), are encoded by inverted MPSC at the user's encoder with a codeword (or codewords) assigned to the user. The sequences from each encoder drive each LED and optical signals from all the LEDs are transmitted simultaneously and multiplexed spatially. The multiplexed optical signals are received and converted into electrical signals by a photodetector (PD) at each receiver. The converted signal sequence is fed to each user's decoder, and the decoded data are delivered to each destination.  
This optical CDMA system is assumed to be a synchronous system in that the optical signals' output from each LED are synchronized in codeword units. We let \(N_{\rm max}\) be the maximum number of users for the MUI cancellation scheme under consideration, and \(N_{\rm L}\) be the number of light sources, where \(1\leq N_{\rm L}\leq N_{\rm max}\). It is assumed that each light source such as an LED device is assigned to a single user. Among the \(N_{\rm L}\) light sources, \(N\) light sources are active (\(0\leq N\leq N_{\rm L}\)). In other words, \(N\) users are transmitting some data, while \(N_{\rm L}-N\) users are not active and not transmitting any data. In this study, each active user transmits binary data \(I\), where \(I\in\{0,\ 1\}\).  
Signature codes for optical CDMA systems are expected to be unipolar. MPSCs [16], sometimes called _synchronized prime codes_[12] or _modified prime codes_ (_MPC_) [13], are generally used in synchronous optical CDMA systems. An MPSC is a binary code generated from a prime field GF(\(p\)) and has \(p^{2}\) codewords with code lengths of \(p^{2}\), where \(p\) is a prime number. All the codewords have a unique weight value of \(p\). The set of \(p^{2}\) codewords can be divided into \(p\) groups of \(p\) codewords. The cross correlation between any two codewords is zero when they belong to the same group. Otherwise, the cross correlation is one.
## 2 Method
### Procedure  
After this, the first 300 CI-values with accompanying rotational speed value in each dataset was chosen as training data for each respective case. Two sets of ANNs, one for the enveloped FFT CI-value results and one for the enveloped WPT CI value results, were trained for each failure case. These had three hidden layers with 64, 8 and 64 neurons respectively. In the early stage of this investigation, a multitude of structures concerning the number of hidden layers and neurons, were tested. While it was shown that a simpler one or two hidden layer structure could produce comparable prediction results, as long as the number of neurons was sufficiently high, this three-layer structure was shown to yield more stable and repeatable results without any decrease in performance. Therefore, it was decided to use this structure for each failure case. The rotational speed measurements were used as input and CI-values before normalization as output. The ANNs were set up in Matlab using the fitnet function and trained using the Levenberg-Marquardt algorithm for optimal computational speed. By using these trained ANNs to predict the CI-values for the remaining measurements in the datasets, the effect of the defect on the measurements is separated from the inherent rotational speed dependant, CI-value variance. The difference, \(d_{n}\) between the true CI-values, \(CI_{n}\), and the predicted values, \(p_{n}\), were calculated for each measurement, \(n\), in the datasets by  
\[d_{n}=\sqrt{\left(CI_{n}-p_{n}\right)^{2}}. \tag{3}\]  
Z-score normalization was used to be able to compare the differentiation results between the true and predicted CI-values between the two signal analysis methods, and the detectability performance of the true CI-values by themselves. The normalization of one value of the difference between the true and predicted values is calculated by  
\[Z_{n}=\frac{d_{n}-\mu(d_{1,2,...,20})}{\sigma(d_{1,2,...,20})}, \tag{4}\]  
where the \(\mu\) and \(\sigma\) values are calculated from the first 20 values.
# MicroForest: Lightweight Bottleneck Prediction for Manufacturing Processes on Edge Devices
## 4 Proposed Method
### Manufacturing Process Modeling  
Before delving into the proposed method, we first define the manufacturing process simulation problem that this paper aims to solve.  
We model the manufacturing workflow as a Directed Acyclic Graph (DAG) \(G=(N,E)\), where each node \(n\in N\) represents a distinct production task, and each directed edge \(e\in E\) denotes the flow of an intermediate product from one task to another. The entire process is simulated in discrete time steps (i.e., cycles), yielding a cycle-accurate view of material movements and task throughput.  
Once the number of tasks and the relationship between tasks are given, the simulation is parameterized by two input sets: buffer levels and cycle times. Each edge \(e\) has an initial buffer level specifying the quantity of intermediate product in transit. The cycle time for each task \(n\) denotes the number of cycles required to process one unit. Although cycle times may vary in real manufacturing scenarios, we treat them as fixed for simplicity.  
At each cycle, every task examines its incoming edges; if all required buffers contain sufficient material, it consumes one unit from each and produces one unit of output after its cycle time elapses. The output is then enqueued on the task's outgoing edges. The simulator records, per cycle, (a) cumulative production per task, (b) current buffer occupancy per edge, and (c) which task(s), if any, have become bottlenecks due to buffer starvation or processing delays. This cycle-level simulation enables precise determination of both the timing and the location at which bottlenecks arise.  
With the cycle-level simulation, we pose the bottleneck prediction problem as follows. Given a user-specified horizon of \(\tau\) cycles, the model must decide, for every task \(n\in N\), whether that task will become a bottleneck at cycle \(t+\tau\) based on the system state observed at cycle \(t\). Because the manufacturing process network contains \(N\) distinct tasks, the simulator generates \(N\) prediction queries per decision, each of which is a binary decision (\"bottleneck\" or \"non-bottleneck\").  
For illustration, Figure 2 depicts a simple manufacturing workflow comprising nine tasks. Each node is labeled with its task index and the cumulative count of intermediate products it has produced, while the numbers on the edges indicate the quantity of product currently buffered along each connection. In this example, three tasks (highlighted in red) act as the bottleneck because it receives no input from its predecessor.
## 4 Results and Discussion
### Estimation of Cooling Load Using CLTD and HAP  
Steady-state modeling was done for the room space. The cooling load was calculated using the cooling load temperature difference (CLTD) method, which is one of the family members of the transfer function method (TFM).  
King Saud University is located in Riyadh at a latitude of 24.6\({}^{\circ}\) N and longitude of 46.7\({}^{\circ}\) E. The internal designed temperature for the room was 20 \({}^{\circ}\)C, and the external maximum temperature was 48 \({}^{\circ}\)C with a daily range of 21 \({}^{\circ}\)C. The relative humidity inside the room is assumed to be 50% RH with an electrical light of 20 Watts and a computer of 0.17 HP rater power. The same input conditions are used for estimating the cooling load HAP. Table 6 describes the test room building envelope, and Table 7 gives the results obtained for the cooling load using CLTD and HAP.  
Figure 6: Performance analysis approach.  
The equivalent thermal conductivity (k) and overall heat transfer coefficient (U\({}_{\rm L}\)) of the building wall are 0.081 W/mK and 48.09525495 W/m\({}^{2}\)K, respectively.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology
### Data Labeling  
In the ALICE experiment, the InfoLogger dataset lacked predefined log labels. Addressing this necessitates an understanding of the operational context. During operations, shifters are responsible for managing the data collection process, which involves starting and stopping the collection of physics data from the detector as particle collisions occur. Shifters serve in different functions, such as quality control (QC), first-level processing (FLP), and the detector team, depending on their specific responsibilities. However, all shifters are required to perform a common task: logging actions and errors that occur during the experimental run. These logs are referred to as \"Bookkeeping\" logs, which document all actions undertaken by shifters during a given run. The structure of the bookkeeping logs enables the InfoLogger data to be categorized into potential anomaly topics. In this study, we manually explored the bookkeeping data and mapped them into the InfoLogger dataset to identify log messages associated with anomalous behavior. These manually labeled anomalous logs were then used for model training. For the BGL dataset, the labels were already provided, making the labeling process unnecessary in this case.