esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Insulator and Defect Detection Method Based on Improved YOLOv8
## 3 Improved YOLOv8 Lightweight Object Detection Network
### Integration of CBAM Attention Mechanism  
Considering that insulators have a small spatial proportion in the overall aerial image, they are typical small targets and thus are easily misidentified or missed. To solve this problem, a lightweight attention module CBAM is added to the feature extraction part of YOLOv8s, the specific structure of which is shown in Figure 5. The CBAM module enhances the accuracy of small target detection by adding an attention mechanism to the feature map, which enhances attention to the target features.  
Figure 4: A diagram of the proposed network structure, indicating the revisions in the Backbone, Neck, and Head parts of the original YOLOv8.  
The CBAM module contains two sub-modules: the Channel Attention Module (CAM) and Spatial Attention Module (SAM). The CAM mainly works on the channel dimension. It enhances the representation of useful features by learning the importance weights of each channel while suppressing irrelevant or redundant features. SAM acts mainly on the spatial dimension. It enhances the spatial feature representation of the target object by learning the importance weights of each spatial location.  
The CAM module first performs global average pooling and global maximum pooling on the input feature maps and then inputs the pooling results into a shared MLP, which adjusts the feature strengths of each channel by the weight output from the MLP. This approach effectively maintains the stability of the channels and compresses the spatial extent, allowing the network to focus more on the key feature information in the input image. The SAM module first performs global average pooling and global maximum pooling on the input feature map in the channel dimension and then concatenates the pooling results in the channel dimension to form a feature map. This feature map is passed through a convolutional layer that outputs a spatial attention map, which is used to adjust the feature intensity at each spatial location in the input feature map. This approach effectively maintains spatial stability and compresses the number of channels, allowing the network to focus more on the spatial information of the target object.  
Figure 5: A diagram of the CBAM’s structure, indicating the composition of the CAM and SAM substructures.
## Appendix A Algorithm by [PERSON] and [PERSON]  
In [26], [PERSON] and [PERSON] developed an algorithm for determining whether an interval graph \(G=(V,E)\) has a bandwidth less than or equal to a given integer \(k\). Their algorithm plays an important role in the proof of Lemma 5. To be self-contained, we give the details of their algorithm below:  
```
Input : An interval graph \(G=(V,E)\) and a positive integer \(k\). Output : A layout realizing \(\mathsf{bw}(G)\leq k\) if it exists.  Set \(Label(i)=0\) and \(Mark(i)=n\) for all \(i\in V\) where \(n=|V|\). Set \(U=\{i\in V\mid Label(i)=0\}\) and \(q=0\);  Select \(i\in U\) with smallest \(L(i)\) (break ties by selecting the interval with smallest \(R(i)\)) and set \(q=q+1\);  Set \(Label(i)=q\) and \(U=U\setminus\{i\}\). If \(U=\emptyset\), stop, all vertices have been labeled;  If \(r\in U\) overlaps \(i\) and \(Mark(r)=n\) set \(Mark(r)=\min\{Label(i)+k,n\}\);  Let \(S_{j}^{q}=\{r\in U\mid Mark(r)\leq q+j\}\). If \(\left|S_{j}^{q}\right|\leq j\) for all \(j\geq k-q+1\), go to step 7;  There is a \(j\) such that \(\left|S_{j}^{q}\right|>j\). Stop, for the bandwidth of the graph is \(>k\);  Find the smallest value \(j_{0}\), such that \(\left|S_{j_{0}}^{q}\right|=j_{0}\);  Select \(i\in S_{j_{0}}^{q}\) with smallest \(L(i)\) (break ties as in Step 2). Set \(q=q+1\) and go to Step 3.
```  
**Algorithm 4**Algorithm KV
## 3 Results  
### Determining the Validity of the Model's Assumptions  
Evaluating Equation (13) for all 1020 parameter identification experiments led to the results displayed in Figure 7. For all six investigated geometries, the mean errors of the fluid's line approximation rise with the operating speed \(n\). Furthermore, the corresponding standard deviations also rise with the operating speed. Observing the six different graphs, it is noticeable that every single graph can be divided into two different ranges of operating speeds. At first, an almost horizontal course can be noticed, followed by a range of a strongly rising course. The defined limit value for \(\overline{\varepsilon}\) of \(0.5\,\text{mm}\) forms the boundary between these two ranges and thus leads to the sought maximum operating speed \(n_{\text{max}}\) for every chosen geometry. The maximum operating speeds are stated in Figure 7 for each geometry and differ from one another. It can be noted that the container's width and the maximum operating speed are inversely proportional to each other.
###### Abstract  
The aim of the present study was to evaluate the pulpal and periapical status of vital teeth within the area of a lateral sinus floor elevation (LSFE). The files of patients treated by LSFE between 2009 and 2015 were retrospectively evaluated. The radiographic bone measurements and the periapical status of the teeth adjacent to the LSFE area were evaluated preoperatively and 6 months following the surgery by CBCT. The pulpal status of the adjacent teeth was clinically evaluated using thermal pulp vitality testing. Vital teeth within the sinus floor elevation area were the main inclusion criteria for this study. A total of 158 LSFE procedures were evaluated; 18 cases fulfilled the inclusion criteria, and 20 adjacent teeth were vital. There were no cases of periapical pathology or loss of pulp vitality. There was a statistically significant difference in the ridge height between the baseline and 6 months postoperatively and in the distance from the apex to the sinus floor between pre- and 6 months postoperatively (\(p<0.01\)).The pulpal and periapical status of vital teeth was not affected following LSFE.  
tooth vitality; maxillary sinus; bone augmentation; dental diseases; oral diseases; dentistry; diagnosis; prognosis +
Footnote †: journal: Applied Sciences
# Phase Alignment of an Array Optical Telescope System Using Balanced Detection
## 5 Conclusions  
In conclusion, this study proposed and demonstrated a method of phase alignment of an array optical telescope system using balanced detection. In this paper, the phase difference of the signal optical path was combined with the DPSK demodulation module. The electrical signal information of the differential output port of the balance detector was used to highly accurately solve the phase difference between the two optical signals. Specifically, the sign was used to demodulate the digital signal, and the value was used to calculate the phase difference.  
The simulation results showed that the calculated phase difference information of the optical signal was compensated by feedback, so the corrected power value remained around 2.75 mW, the bit error rate was less than \(10^{-9}\), and the Q parameter was improved. It is demonstrated that the scheme proposed in this paper can support signal combination, coupled signal power stability, and communication link reliability. As a result, the influence of the balance detector asymmetry can be ignored, if \(K>0.97\) holds. Thus, a more reliable performance of the communication system can be achieved by utilizing the SNR data in engineering applications.  
In addition, this paper verifies the detection of phase differences between two apertures, which can be extended to more aperture experiments using similar principles. Furthermore, atmospheric turbulence data can be used to improve the system.  
Conceptualization, Q.W. and Y.H.; methodology, Y.L., Q.W. and Y.H.; software and simulation, Y.L.; validation, Y.L., Q.W., Y.H. and R.M.; formal analysis, Y.L.; writing--original draft preparation, Y.L. and R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are available within the article.  
The authors declare no conflict of interest.
## 2 Literature Review and Conceptual Context
### Traffic Prediction
#### 2.1.2 Using Social Media  
Traffic forecasting methodologies using social networks address the problem of inaccurate urban forecasts, especially where there is limited availability. The \"human\" sensors use Twitter as a hub. An application was developed to create a traffic network on Twitter using spatial features. Thus, the problem of the scarcity of data was overcome [35]. This technique, called Traffic Prediction (CTP), can predict traffic without historical data. The correlation between traffic conditions and tweets [36] was investigated, but the results indicate that systems based on historical data are superior. T-MAPS was an application created to imitate the Routing of Google Maps. Its operation has the following steps: segmentation to a personalized section of the city, creating static networks with a timeline, and applying weights to the network's edges based on the traffic.  
On the other hand, these networks do not merge with the dataset's features. This merging could be performed with a GCN; therefore, T-MAPS routes achieved 60% accuracy compared to Google Maps. However, it is the first traffic prediction model that uses social networks and is designed for route generation.  
Rarely does the traffic forecast need social networks to be more accurate. However, as accidents increase, some studies use event detection for real-time and dynamic forecasting using tweets and the weather [37; 38]. This research incorporated a full set of variables (social media, timestamp, weather, and traffic information) influencing traffic congestion. This makes such studies unique, and they are examples of good predictions.  
GCN and LSTM embedded social media using queries and travel analysis [39]. Users searched for routes, points of interest, and travel times. These searches affected traffic flow. The model predicted traffic in an urban environment using a seq2 seq. Therefore, the model required a dataset that included traffic flows, queries, and road networks.  
These studies are applicable to increasing the accuracy and reducing the delivery time of routes in a vehicle routing problem. However, frameworks incorporating social media in routing problems are for touristic purposes and visiting points of interest (POIs) [40]. Even though analyzing POIs in road networks can benefit last-mile deliveries, no known research includes social media in a situation-aware route or last-mile methodology.  
Figure 4: GCN-LSTM System Architecture (based on [34]).
# Cross-Domain Person Re-Identification Based on Feature Fusion Invariance
## 3 Experimental Results
### Comparison Experiment with the Current First-Class Algorithm  
To compare the effectiveness of cross-domain pedestrian rerecognition based on local semantic feature invariance proposed in this paper, the experiments in this section conducted a comparative verification of cross-domain pedestrian rerecognition performance on the Market-1501 and DukeMTMC-reID datasets. As shown in Table 3, in the cross-domain pedestrian rerecognition experiment of DukeMTMC-reID to Market-1501, the performance is as follows: rank-1 is 90.6%, rank-5 is 96.4%, rank-10 is 98.3%, and mAP is 82.2%. Compared with the current first-class cross-domain pedestrian rerecognition algorithms, the performance is significantly improved. Similarly, in the cross-dataset experiments of Market-1501 to DukeMTMC-reID, the proposed method outperforms the pedestrian rerecognition algorithm. Therefore, the cross-domain pedestrian rerecognition method based on local semantic feature invariance in this paper effectively improves the performance of cross-domain pedestrian rerecognition.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**DukeMTMC-reID \(\rightarrow\) Market-1501**} \\ \cline{2-6}  & **R-1** & **R-5** & **R-10** & **R-20** & **mAP** \\ \hline Minibatch & 76.4 & 79.5 & 82.3 & 85.6 & 66.2 \\ Minibatch + Memory & **90.6** & **96.4** & **98.3** & **99.3** & **82.2** \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison of memory and minibatch.
## 4 Conclusions  
In this study, VOCs in pear fruit were analyzed using HS-SPME combined with GC-MS. Salt addition used during homogenization enhanced dissociation of VOCs and prevented browning of pear. A total of 121 VOCs were quantified in 12 different cultivars of pear fruit. The number and contents of VOCs in different pear cultivars varied dramatically, ranging from 13 to 71 and from 3.63 to 55.65 mg/kg FW, respectively. The Dr. Guyot cultivar showed the highest level of VOCs. The 12 pear cultivars could be divided into three groups on the basis of the results of PCA. The first group included one cultivar, Dr. Guyot. The second group consisted of three sand pear cultivars. The third group was composed of the other eight cultivars.  
After 21 days of storage at 4 \({}^{\circ}\)C, the total concentration of VOCs increased from an initial content of 50.76, to 101.33 mg/kg FW. Storage at 20 \({}^{\circ}\)C largely contributed to a higher production of VOCs than storage at 4 \({}^{\circ}\)C. During 21 days of storage at 4 \({}^{\circ}\)C, the content of esters showed a gradual increase, while the content of alcohols and aldehydes decreased. Based on the results presented, the related alcohols were recognized as the intermediates in the conversion of aldehydes to esters in pear fruit after harvest.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11233778/s1](https://www.mdpi.com/article/10.3390/foods11233778/s1)]([https://www.mdpi.com/article/10.3390/foods11233778/s1](https://www.mdpi.com/article/10.3390/foods11233778/s1)), Figure S1: Photographs of 12 pear cultivars fruits; Figure S2: Photographs of pear sample with NaCl addition before (a) and after (b) homogenization; Figure S3: Principal component analysis (PCA) of VOCs in 12 pear cultivars; Figure S4: The conversion pathway of VOCs in pear fruit during storage; Table S1: Concentration of VOCs in fruits of 12 pear cultivars.  
Data curation, G.G. and Z.Y.; formal analysis, X.Z. and Z.Y.; funding acquisition, G.X.; investigation, X.Z.; methodology, H.L.; project administration, G.G. and G.X.; resources, X.Z. and H.L.; supervision, Y.C.; validation, Y.C.; writing--original draft, G.G.; writing--review and editing, G.G. and G.X. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences (grant no. CAAS-ASTIP and grant no. CAAS-ZDRW202011).  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 3 Neutron Test Measurements
The measured radial averaged scattering curves are plotted in Figure 7 (open squares). The data were shifted in intensity for better comparison. Normalization of SANS data of foams is still challenging and was not an aim of this experiment. The determination of the foam's liquid volume fraction based on the neutron transmission is prone to errors, since most foams show a high transmission close to 1 [26; 29]. In general, the trend of measured scattering curves are in good agreement with the literature data, showing that there are no intrinsic artefacts caused by the foam cell. Data recorded at a height of 2 cm are similar to the one of a wet foam reported in literature and the data recorded at a height of 9.5 cm are in good agreement with the one of a drained foam. The data measured at a height of 16 cm correspond to a foam in an even more drained state. The shoulder at \(q\approx 0.08\) A\({}^{-1}\) is attributed to surfactant micelles present in the liquid foam films. Consequently, the intensity  
Figure 7: Radial averaged scattering data from a 25 g L\({}^{-1}\) SDS foam (86.7 mM) at three different foam heights in a steady-state foam (open symbols) and comparison with reference data of a wet continuous foam and a foam after drainage [26]. of this shoulder decreases with increasing foam height, as the liquid drains out of the foam films over time. The shoulder at \(q\approx 0.02\,\text{\AA}^{-1}\) occurs due to scattering at the liquid foam films and is related to their thickness [26]. The fact that this shoulder was not observed by [PERSON] et al. in their wet continuous foam could be explained by an even lower measuring position or higher gas flow rate in the steady-state. Both factors would results in a wetter foam, leading to a less ordered foam structure with a higher polydispersity regarding the film thickness and, therefore, in a smearing out of the corresponding scattering feature. Since it was possible to reproduce two different experiments reported in the literature by scanning along the foam height of a steady-state foam, the presented approach is valid for accessing the foam in different drainage stages with varying liquid volume fractions in one experiment.
## 4 Analysis of Results
### Effect of \(H_{t}\) on the Stability of the Ventilation Network  
The height of the upper step should be determined by the height of the tunnel section, the stability of mechanical equipment and the surrounding rock. The height of the upper step excavation \(H_{\rm t}\) should be 2.5-3.5 m. The flow rate and resistance of each branch of the tunnel under different \(H_{\rm t}\) conditions are shown in Figures 13 and 14.  
It can be seen from Figure 13 that when \(H_{\rm t}\) = 2.5 and 3.5 m, \(Q_{\rm 1}\) = 28.1 and 28 m\({}^{3}\)/s, respectively, after the air flowed through the cross passage, the cross-section flow of the advance side tunnel decreased by 1.1 and 1.9 m\({}^{3}\)/s (\(Q_{\rm 1}\)-\(Q_{\rm 3}\)), and the cross-section flow of the undercut side tunnel increased by 1.7 and 0.8 m\({}^{3}\)/s (\(Q_{\rm 4}\)-\(Q_{\rm 2}\)). When \(H_{\rm t}\) = 3 m and \(Q_{\rm 1}\) = 26.2 m\({}^{3}\)/s, the cross-section flow rate of the advance side tunnel increased by  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(L_{\rm p}\)/(m) & \multicolumn{2}{c}{**Difference in the Air Flow Demand of Two Holes**} & \(R_{\rm 1}\)/(N-\(s^{2}\)/m\({}^{8}\)) & \(R_{\rm 2}\)/(N-\(s^{2}\)/m\({}^{8}\)) & \(R_{\rm 3}\)/(N-\(s^{2}\)/m\({}^{8}\)) & \(R_{\rm 4}\)/(N-\(s^{2}\)/m\({}^{8}\)) & \(K\) \\ \hline \multirow{2}{*}{80} & \(Q_{\rm a}>Q_{\rm u}\) & \(9\times 10^{-4}\) & \(1\times 10^{-2}\) & \(1.4\times 10^{-4}\) & \(7.4\times 10^{-4}\) & 0.45 \\  & \(Q_{\rm a}<Q_{\rm u}\) & \(2.4\times 10^{-4}\) & \(3.9\times 10^{-3}\) & \(3.1\times 10^{-4}\) & \(1.8\times 10^{-1.7 m\({}^{3}\)/s, and the cross-section flow of the undercut side tunnel increased by 2 m\({}^{3}\)/s. In Figure 14, there was a significant difference in resistance under different operating conditions. Combined with Figures 13 and 14, Table 5 shows the \(K\) and \(R_{\rm m}\) pair flows of each branch under different \(H_{\rm t}\) conditions.  
It can be seen from Table 5 that when \(H_{\rm t}\) = 2.5 and 3 m, \(K\) = 0.261 and 0.687, both of which were less than 1, and the air flowed from the advance side tunnel to the undercut side tunnel. When \(H_{\rm t}\) = 3.5 m, \(K\) = 2.879 \(>\) 1, the air flowed from the undercut side tunnel to the advance side tunnel. In fact, with the increase of \(H_{\rm t}\), the section area of the upper step decreased, and the local air flow resistance of the sudden expansion of the overflow section changed significantly, and the vortex area here changed the flow velocity of the section and the direction of air flow. Therefore, in the range of 3 m \(<\)\(H_{\rm t}\) \(<\) 3.5 m, there may be a certain \(H_{\rm t}\) that makes \(K\) = 1, and the air flow of the double-hole tunnel does not interfere with each other.
## 4 Mobile Peer-to-Peer Architectures
### Architecture
#### 4.2.2 Integrated  
Integrated approaches reduce routing overhead by combining the MANET and P2P protocols at the network layer as shown in Figure 6(c). This way, the number of required routing tables is reduced as no separate MANET and P2P routing tables are required. Further, the routing algorithm may benefit from synergy effects such as updating overlay routing tables based on overheard underlay messages as described below.  
By reducing the complexity and the number of required entries in the routing table, routing and maintenance overhead may be reduced. Further, knowledge of the physical neighborhood may improve the routing efficiency of the routing protocol. Scalable Source Routing (SSR) proposed by [PERSON] [16] introduces an MP2P architecture based on Chord. Similar to Chord, SSR is based on a ring architecture. However, SSR simplifies routing maintenance as no finger tables are used but only direct virtual neighbors. Further, physical neighbors are listed in the routing table. During routing, a node decides whether a virtual or physical neighbor is closer to the destination. Due to the simplified routing table, maintenance overhead is reduced. Further, by combining MANET and P2P routing tables, the route length can be reduced without increasing the complexity or the routing maintenance required.  
Additionally, awareness of physical neighbors at intermediate nodes between source and destination in the underlay can improve the efficiency of the routing protocol. The Virtual Ring Routing (VRR) algorithm presented in [17] combines the set of virtual and physical neighbors in a single routing table at the network layer. As VRR is based on a Ring architecture, the set of virtual neighbors consists of \(r\) nodes with a node-ID next to the node-ID of the routing table owner. Those \(r\) nodes consist of the \(r/2\) virtual neighbors clockwise and the \(r/2\) virtual neighbors counter-clockwise. VRR provides bidirectional links to the neighbors and maintains them proactively. The physical node set consists of the nodes in direct transmission range. The routing table provides the next underlay hop with an endpoint closest to the requested ID when an object is requested. As each intermediate node checks its own routing table before forwarding the request, requests may be rerouted when intermediate nodes are aware of shorter routes. This way, routing is simplified and overhead is reduced.  
As a further optimization, every overheard or forwarded packet may be harnessed to update the routing tables in order to reduce maintenance overhead. This is the basic concept of EKTA [18] and DPSR [19]. Both integrated approaches implement a Pastry DHT on the network layer. Similar to Pastry, a primary and a leafset routing table are provided. Complete multi-hop routes are stored at these routing tables to avoid underlay flooding during overlay routing. Shortest routes are preferred to reduce overhead. Whenever a message is overheard, the routing table is updated by routing information obtained from the message. Due to this, overhead generated by routing table maintenance is strongly reduced.