esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Status
### Attention Mechanism Models  
With the widespread adoption of the self-attention mechanism in transformer, researchers have found that incorporating attention mechanisms can help models capture more subtle features of traffic patterns and achieve precise time-series prediction. Some research [23] introduced an attention mechanism to adjust the importance of traffic information at different time points, thus solving the problem of traffic prediction based on static spatio-temporal dependencies in software-defined networking (SDN) traffic engineering. Another study [24] also introduced an attention mechanism and combined it with the GRU neural network. This approach fully exploited the self-similarity and long-range correlation characteristics of traffic data sequences, focused on the importance of traffic data and hidden states, and learned the time-dependent characteristics of the input sequences. In general, accurately grasping the key features and long-term dependencies of data is crucial. The emergence of attention mechanisms has brought new ideas for solving time-series prediction problems. When combined with recurrent neural network models, they complement each other and greatly enhance the performance of time-series prediction models.
_Int. J. Mol. Sci._**2015**, _16_, 18368-18383; doi:10.3390/ijms160818368  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
**The Essential Role of Vitellogenin Receptor in Ovary Development and Vitellogenin Uptake in**  
_Bactrocera dorsalis_ (Hendel)  
**[PERSON] 1,2, [PERSON] 1,3, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1,**  
Key Laboratory of Entomology and Pest Control Engineering, College of Plant Protection, Southwest University, Chongqing 400715, China; E-Mails: iamconglin(@126.com (L.C.); yangwenjia10(@126.com (W.-J.Y.); jxzzby(@163.com (X.-Z.J.); [EMAIL_ADDRESS] (J.-Z.N.); blackaet(@163.com (G.-M.S.)  
Citrus Research Institute, Southwest University, Chongqing 400712, China; E-Mail: ranchun(@)cric.cn  
College of Biology and Environmental Engineering, Guiyang University, Guiyang 550005, China  
Author to whom correspondence should be addressed; E-Mail: wangjinjun(@swu.edu.cn; Tel.: +86-23-6825-0255; Fax: +86-23-6825-1269.  
Academic Editor: [PERSON]  
_Received: 29 June 2015/Accepted: 30 July 2015 / Published: 7 August 2015_  
**Abstract:** The vitellogenin receptor (VgR) functions as an essential component in uptaking and transporting vitellogenin (Vg) in female adults, which is involved in ovary development and oviposition. This study aimed to clarify the molecular characteristics and function of VgR in the oriental fruit fly _Bactrocera dorsalis_ (Hendel). Here, we identified the full-length of _BdVgR_ (GenBank Accession No. JX469118), encoding a 1925 residue (aa) protein with a 214.72 kDa molecular mass and several typical motifs of low-density lipoprotein receptor superfamily (LDLR). Phylogenetic analysis suggested that _BdVgR_ was evolutionary conserved with other Dipteran VgRs. The expression of _BdVgR_ was exclusively detected in the ovaries rather than head, thorax or other tissues. The developmental expression patterns showed that the signal of _BdVgR_ was detectable in very beginning of adult stage, and positively correlated with the growth rate of ovaries and the expression levels of its ligands. In addition, we also demonstrated that the expression level of _BdVgR_, and ovary development were significantly suppressed after being injected with _BdVgR_-targeteddsRNA. Together, all of these results indicated that \(Bd\)VgR was critical for yolk protein absorption and ovary maturation in \(B\). _dorsalis_, playing a vital role in female reproduction.  
**Keywords:**_Bactrocera dorsalis_; vitellogenin receptor; yolk protein; ovary; RNA interference
## 3 APD Breadboard Design and Implementation
### TIA Results
An example of the choice of this current value is shown in Figure 12.  
The calculated gain utilizing the effective photocurrent (EPHC) value for the four different temperatures is presented in Figure 13.  
Figure 11: Measured IV (current voltage) curves, using the TIA circuit, for dark current and photocurrent, with the laser set to 1.33 mW. Source bias refers to the APD cathode/anode voltage drop. In Figure 13, the highest gain reachable is shown is 346.1 for 18.4 \({}^{\circ}\)C and 184.3 for \(-\)23.3 \({}^{\circ}\)C. The APD was operated in avalanche mode with sustained high gains, much higher than the ones reported in the datasheet (in the datasheet, the APD is only operated up to gains = 40). For the \(-\)23.3 \({}^{\circ}\)C, the breakdown voltage was reached at 58.7 \(\pm\) 0.1 V (cathode/anode voltage), but not crossed, due to saturation of the TIA APD. These can be seen in Figure 14; therefore, Geiger mode operation was not reached. Figure 12: Calculated EPIC, with the chosen primary effective photocurrent (orange data label) used to parameterize the \(-\)23.3 \({}^{\circ}\)C temperature measurement for an optical power of 1.33 mW, using the TIA circuit. Figure 13: Calculated gain using the EPHC and the TIA circuit for 1.33 mW. Labeled values indicate the maximum gain for each data curve. Source bias refers to the APD cathode/anode voltage drop. To operate the APD at these high gains, it is important to have an accurate temperature control since a temperature variation can induce a high variation in the gain. Figure 15 gives the gain variation as a function of temperature. As an example, for a bias of 58.7 V, the gain varies more than 40 times for temperatures ranging from 22 \({}^{\circ}\)C to \(-\)23 \({}^{\circ}\)C, showing that a fine temperature control is needed. In Figure 15, it is possible to see that by decreasing the temperature at \(-\)23.3 \({}^{\circ}\)C by half a degree, the gain for a polarization of 58.7 V is reduced by roughly 24.
## 5 Experiments and Results
### Experimental Results and Analysis

Table 1: Root mean square errors (RMSEs) of boresight errors in different cases. Figure 19: Autonomously tracking a flying drone with the experimental platform. (**a**) the target drone and the interceptor (the quadrotor platform); (**b**) the searching phase; (**c**) start the tracking phase when the target is recognized; (**d**) keep following the target drone. Figure 21: The actual position (**a–c**) and 3D trajectory (**d**) of the UAV and the target drone in the local NED frame. Figure 20: The attitude and approaching speed of the UAV while autonomously tracking a flying drone. (**a**) roll angle measured by the camera IMU, encoder and UAV navigation system; (**b**) pitch angle measured by the camera IMU, encoder, and UAV navigation system; (**c**) yaw angle measured by the camera IMU, encoder, and UAV navigation system; and (**d**) the desired and actual approaching speed of the UAV.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 4 Experiment and Result Analysis
### Experimental Environment  
In the experiment, the information interaction between the three SWIM access node departments was simulated, and the service information involved all originated from the real civil aviation network. According to SWIM's architectural characteristics and business processes, and the experimental environment was built to test this scheme. Using the open-source enterprise service bus as the service infrastructure, the service registration node is designed to enable the service provider to publish  
Figure 7: The name-based data forwarding process in SWIM.  
service information, query the service called by the service requester and return the description of the service to help the service requester and the service provider Bind to complete the service call in SWIM. The network topology is shown in Figure 8.  
The simulation platform is composed of PC, server, router, and switch. Among them, a PC as a service acquirer obtains SWIM services, and a PC as a service provider provides SWIM core services, including service registration, query, and management functions.  
The basic configuration of each server in this experiment is shown in Table 2.  
The SWIM access node is mounted on the ActiveMQ server, MySQL is used as the database to store SWIM service data, and the flight information service and aviation information service are provided externally with the SWIM flight information exchange model and the SWIM aviation information exchange model as standards.  
In the experiment, take aviation, flight, and weather data separately, take values of different lengths and numbers, and then process them in layers. The name prefix table is used as the data set, and the size is 3 m and 10 m respectively. The 3 m prefix table contains 4,532,167 name prefix entries, the data set size is 56 MB, and the 10 m prefix table is 896,262 entries. The name hierarchy and length of the two prefix tables are shown in Tables 3 and 4, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Computer Setup** & **Remarks** \\ \hline processor & Intel(R) Core (TM) i5-3470 \\ main frequency & 3 GHz \\ RAM & 4 GB \\ operating system & Linux \\ database systems & MySQL 5.6 \\ software & MyEclipse 10 \\ application server & Tomcat 6.0 \\ browser & IE 9.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Environment configuration.  
Figure 8: Experimental test environment.  
Table 5 shows all the data sets collected and used in the experiment, including a total of 10 data sets from test 1 to test 10. The data source is randomly selected from the blacklist data set. Test 1 is a data set with a data set size of 1 MB, and the subsequent data sets are sequentially increased by 1 MB data.
## 6 Results and Discussions
### Validation of the Present Method
#### 6.2.1 Comparison with Published Literature  
Figure 5 shows the comparison of the pressure distribution with different deadrise angles before the flow separation. The similarity solution was reported by [PERSON] and [PERSON] [4]. It can be seen that the results of the present method generally agree well with those of the similarity solution, even though some deviations appear in the result with the 20\({}^{\circ}\) deadrise angle. Figure 6 shows the comparison of the impact forces in the stages before and after the flow separation, where \(F_{Z}\) is the vertical impact force of the half wedge. The methods adopted by [PERSON] et al. [21] and [PERSON] et al. [7] were the FVM in OpenFOAM (version 1.5) and the ALE in LS-DYNA, respectively. It can be seen that results of the present method agree well with those of the published data.  
Figure 4: Boundary conditions of the stiffened panel.  
Figure 5: Comparison of the pressure distribution before the flow separation. (**a**) 10\({}^{\circ}\) deadrise angle; (**b**) 20\({}^{\circ}\) deadrise angle.  
The present method is applied to the water entry of 2D elastic wedges with the consideration of the added mass. Figure 7 shows the comparison of the time-history deflection result at the center of the length \(L\) between the present numerical predictions and the published results reported by [PERSON] and [PERSON] [22]. The fluid domain in [PERSON] and [PERSON] [22] was solved by using the CFD open source code OpenFOAM. It can be seen that results of two methods not only agree well before the flow separation, but also after. The agreement of the vibration response after the flow separation also proves that the previous assumption in the calculation of the generalized hydrodynamic coefficients after the flow separation is physically sound.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 2 Routing in the Data Center
### BGP in the Data Center  
Among other reasons, the choice of BGP [18] as a routing protocol for data centers is motivated by (i) the presence of robust implementations, (ii) reduced control plane flooding, (iii) native support for many protocols, such as IPv4 and IPv6, Multiprotocol Label Switching (MPLS), and VPNs, and (iv) multi-path support. BGP was designed for single-path inter-domain routing, and, therefore, it must be specifically configured for multi-path data center routing. Indeed, the dense connectivity of the data center network is vastly different from the relatively sparse connectivity among administrative domains on the Internet. In inter-domain routing, stability is preferred over rapid notification of changes. Therefore, BGP speakers typically hold off sending notifications about changes for a while. Instead, operators want routing updates to be as fast as possible in a data center network. In addition, due to its default behavior as a path-vector protocol, any single link failure  
Figure 1: A fat-tree topology with \(k=4\): four-port switches arranged in four PoDs with four switches each.  
can result in a large number of BGP messages passing between all the nodes, a situation that should be avoided in data centers. Finally, by default, BGP speakers build a single best path when a prefix is learned from many different ASes (because they typically represent a separate administrative domain), while in data centers, multiple paths selection is needed. BGP configuration needs to be specifically tailored for the data center. Firstly, eBGP is preferred over iBGP since it is simpler to understand and configure, especially for multi-path support. Secondly, Autonomous System numbering in the data center is different from the traditional one. Only private ASN is used and, to potentially support more than 1024 nodes, the 4-byte ASN address space [19] is preferred. However, even though the most straightforward approach to ASN assignment is that every router is assigned a different one, this approach leads to the _path hunting_ problem, which is a variation of the _count-to-infinity_ problem suffered by distance vector protocols [20]. To avoid this problem, the practical guideline for ASN assignment in a fat-tree topology is the following: (1) Each Leaf node is assigned a distinct ASN; (2) Spines in the same PoD have the same ASN that is different for each PoD; (3) All ToFs share the same ASN. However, the drawback of this assignment model is that route aggregation is not possible because it can lead to _black-holing_. Thirdly, some additional tweaks are needed; that is, the only attribute to consider in the decision process is the AS_PATH, and, in order to support Equal Cost Multi-Path (ECMP), a group of routes for a given destination is considered equal if the AS_PATH length is the same, relaxing the criterion that the ASNs in the AS_PATH should match exactly. Refer to [8; 21] for an in-depth description.
## 3 Results
### Statistical Tests
#### 3.4.2 5 \(\times\) 2 Cross-Validation Paired \(t\)-Test
counterparts on the ROC-AUC metric, while no statistically significant differences were detected for SVM under the remaining metrics. Across the accuracy metric, LR was significantly outperformed by all models (for instance, on RF vs. LR: t = 6.3684, \(p\) = 0.0014; ANN vs. LR: t = 7.4070, \(p\) = 0.0007; CB vs. LR: t = 5.2129, \(p\) = 0.0034). Inspection of precision revealed similar patterns; for example, LR showed a t-score of 4.1429 (\(p\) = 0.0090) relative to RF and a t-score of 5.5737 (\(p\) = 0.0026) when tested against CB. Under the recall metric, the disadvantages of LR were again confirmed by statistically significant comparisons such as XGB vs. LR (t = 2.0102, \(p\) = 0.0034) and SVM vs. LR (t = 2.9035, \(p\) = 0.0337). The F1 results underpin the general weakness of LR, with p-values (typically below 0.03) for all pairs, for example, (RF vs. LR: t = 5.4417, \(p\) = 0.0028; CB vs. LR: t = 3.9013, \(p\) = 0.0114, etc.). Finally, ROC-AUC analyses provided particularly strong evidence for LR's weakness, as seen in RF vs. LR (t = 8.5058, \(p\) = 0.0004), LGBM vs. LR (t = 9.1800, \(p\) = 0.0003), and XGB vs. LR (t = 7.6399, \(p\) = 0.0006). Under ROC-AUC, the SVM demonstrated inferior performance. Comparative analyses against models such as RF, LGBM, and CB frequently resulted in statistically significant differences, as indicated by small p-values and relatively large t-scores. These findings suggest that SVM's ranking capability deviated substantively from that of its counterparts (except LR), highlighting potential limitations in its discriminative effectiveness within the given classification framework (for example, RF vs. SVM: t = 4.8027, \(p\) = 0.0049; LGBM vs. SVM: t = 5.8171, \(p\) = 0.0021; XGB vs. SVM: t = 6.3247, \(p\) = 0.0015). By contrast, other metrics did not show marked differences for SVM when tested against the same methods. These findings are consistent with the graphical depictions in Figure 3, which illustrated a noticeable inferiority for SVM under the ROC-AUC metric performance. The overall impression is that most classifiers outside of LR did not differ significantly from one another across multiple measures, but that SVM stands out in its area-under-curve behavior, while LR sits at a clear disadvantage on virtually every metric.
###### Abstract  
The timely and accurate monitoring of high-seas fisheries is essential for effective management. However, efforts to monitor industry fishing vessels in the central-eastern North Pacific have been hampered by frequent cloud cover and solar illumination interference. In this study, enhanced fishing extraction algorithms based on computer vision were developed and tested. The results showed that YOLO-based computer vision models effectively detected dense small fishing targets, with original YOLOv8 achieving a precision (P) of 89% and a recall (R) of 79%, while refined versions improved these metrics to 93% and 99%, respectively. Compared with traditional threshold methods, the YOLO-based enhanced models showed significantly higher accuracy. While the threshold method could identify similar trend changes, it lacked precision in detecting individual targets, especially in blurry scenarios. Using our trained computer vision model, we established a dataset of dynamic changes in fishing vessels over the past decade. This research provides an accurate and reproducible process for precise monitoring of lit fisheries in the North Pacific, leveraging the operational and near-real-time capabilities of Google Earth Engine and computer vision. The approach can also be applied to dynamic monitoring of industrial lit fishing vessels in other regions.  
nighttime lights; fishing monitoring; deep learning; VIIRS DNB Article  
1
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 2 Taxonomy
### Communication Technologies  
Various smart health-care services are highly dependent on the communication range (i.e., short and long-range) between devices and servers. Bluetooth, ZigBee, and Wi-Fi are the most noticeable short-range wireless technologies for smart health-care, such as Body Area Network (BAN). LTE and WiMAX are evident long-range technologies used to transfer data between local server to BS in the smart health-care system. Furthermore, LTE-M is considered to be an enhancement to assist IoT. However, 3 GPP needs to further provide improvements to address coverage, battery life-time, and device complexity [34]. Besides protocols currently in use, LoRa standardises the LoRa-WAN protocol for smart health-care use to ensure interoperability among several service providers. Moreover, SIGFOX offers an exceedingly adaptable worldwide network by considering smart health-care applications having low power utilisation. A comparison of notable communication technologies is presented in Table 2.
# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 5 Experiments and Discussion
### Ablation Analysis
#### 5.2.2 Loss Analysis  
On the NURCNet architecture, we verify the rationality of hybrid loss through a series of comparative experiments using different loss terms, as shown in Figure 8 and Table 2. From Figure 8, we can observe that the proposed model with fusion loss captures more rich border details of defect objects, and that the interference of non-defective information is well removed; thus, our NURCNet output a saliency defect map of the clear boundary. Table 2 shows that NURCNet (i.e., the fusion loss items) offers better performance compared to other variations. In addition, compared to the counterpart adopting widely-used cross-entropy loss \(\mathcal{L}_{bce}\), the WF and SM are, respectively, increased by 2.5% and 0.56%, while the MAE is reduced by 14%.
# Influence of Climate Conditions on Deficiencies of Building Roofs
## 3 Results
### Analysis of Climate-Geographical Conditions
#### 3.4.4 Condensation Moisture (TCD4)  
This deficiency occurs more frequently in pitched roofs (PR = 58.82%). It is less prevalent in the Mediterranean (31.38%) climate than in the Continental (33.33%) or Oceanic (35.29%) climates and appears more in the North (70.59%) latitude than in the Central (17.65%) or South (11.76%) latitudes. It is also less prevalent Inland (35.29%) than in Coastal (64.71%) zones. Equally, it was found that this construction deficiency appears more often in zones with Medium (50.98%) precipitation than in zones with High (35.29%) or Low (13.73%) precipitation.  
The higher percentage of these deficiencies in flat roofs may be due to the fact that many workers tend to place the vapor barrier on the outside of the insulation. It is also easy to find zones where the insulating material has not been placed, and others in which it is common to find few ventilation openings in the roof plans. In turn, there is a higher percentage of this deficiency in zones to the North and where there is a medium or high precipitation, given that in these zones the thermal difference between the outer and inner layers of the roof is greater; and also because the placement on site of insulation is made less carefully, as it is thought that it is not as necessary given the lack of high temperatures--even though they may lead to thermal bridges that cause condensations whenever they are not properly placed.
# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 1 Introduction
Therefore, by quantifying and clarifying the influence of the light environment on images during crop measurement, novel methods to measure the SPAD value of many leaves at once can contribute to the development of efficient and productive crop production. In this study, we constructed a SPAD value prediction model using Random Forests [21], XGBoost [22], and LightGBM [23] based on RGB values obtained from images of radish leaves, as well as HSV and HSL values obtained by conversion from RGB values, and color temperature and illuminance as input variables. We focus on the effect of the light environment at the time of photography on the pixel values of digital images and model the relationship between leaf color and SPAD values under varying photographic conditions with respect to the color temperature and illuminance of the light source. Furthermore, based on information such as the variable importance calculated by Random Forests, we examined the influence of photographic conditions on the prediction of SPAD values for agriculture where domain knowledge remains qualitative.