esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In some satellite Internet of Things (IoT) devices with terrain shielding, the qualities of the direct source-destination (S-D) channel are poor, requiring cooperative communications with multi-relays to be employed. In order to solve error propagation of current decode-and-forward (DF) on such occasions, an efficient polar coded selective decode-and-forward (SDF) cooperation method is proposed with a new decision threshold derived from channel state information (CSI). First, the proposed threshold is derived from the CSI by exploiting the channel gain ratio of optimal relay-destination link (R-D) with source-relay (S-R) link. The above R-D link possesses good channel quality among all links in the system. Second, when the channel gain ratio of certain relay links is larger than the aforementioned decision threshold, the source and all these relays cooperatively send messages together to the destination to accomplish perfect SDF transmission. Otherwise, all relays are frozen and the messages are directly transmitted through the S-D link. If it fails anyway, a retransmission is subsequently tried in the next transmission cycle. In addition, a polar code for fading channels is designed and adaptively adjusted to a proper code rate according to channel quality to attain good bit error rate (BER) performance. Simulation results show that the proposed scheme achieves about 0.9 and 0.5 dB gain at BER of \(10^{-4}\), respectively, in multi-relay cooperative communications with multi-path fading channels compared with those of non-cooperation and existing polar coded cooperation channels. Therefore, the proposed polar coded SDF (PCSDF) scheme can improve both the BER and the outage probability (OP) performance in multi-relay cooperative systems, making it quite suitable for heterogeneous network applications in cooperative satellite IoT systems involving sixth-generation (6G) communications.  
c 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
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## 4 Privacy and Security Enhanced Methods
### Credit-Heterogeneity-Based Executor Scheduling Strategy
#### 4.2.2 Executor Scheduling Algorithm  
Our algorithm implements the scheduling function through two distinct triggering mechanisms.  
**Periodic Scheduling Process.** When executors have been running consistently for a time exceeding the preset threshold \(T\):  
1. Generate a random number \(k\in[1,m]\), where \(m\) is the number of online executors;
2. Sort the \(m\) executors in descending order by their online runtime, and take the \(k\) executors with the longest runtime offline for cleansing;Select \(k\) executors from the standby pool to bring online, maximizing the objective function \(\theta(\textbf{P}_{s})=\max(H^{*}+TR)\), where \(\textbf{P}_{s}\) represents the set of candidate executors for scheduling, \(H^{*}\) is the aggregate heterogeneity vector, and \(TR\) is the aggregate credibility vector. **Event-Triggered Scheduling Process.** When inconsistencies in executor outputs are detected: 1. Calculate the total credibility \(TR_{k}\) for each set of executors producing different outputs using \[TR_{k}=\sum_{i=1}^{k}R_{i}-\frac{k-1}{k}\times\beta_{k}\times\sum_{i=1}^{k}R_{i},\] (15) where \(\beta_{k}\) represents the common-mode vulnerability factor: \[\beta_{k}=1-\max(H_{i,j}),i,j\in[1,k],i\
eq j.\] (16) 2. Compare the total credibility of different output groups, and take offline the executor set with lower total credibility for cleansing.  
When executors remain inconsistent after scheduling, and previously uncleansed executors maintain consistency, reduce the total credibility of the uncleansed executor set by a penalty weight \(\pi\) before comparison:  
\[TR^{\prime}_{k}=TR_{k}-\pi. \tag{17}\]
## 3 Imaging Attitude Control
### Coordinate Frames
#### 3.1.2 Navigation (N) Frame  
The navigation frame is the reference in which navigation computations are performed. The center \(O_{N}\) for this frame is located at the POS location, and the frame is rotated at the Earth's inertial rate. \(Z_{N}\) lies along the local geodetic vertical and is positive downward. \(X_{N}\) is normal to \(Z_{N}\) in the local horizontal plane and points to the north. \(Y_{N}\) completes the RH orthogonal set pointing to the east.
# Geotechnical Property Modeling and Construction Safety Zoning Based on GIS and BIM Integration
## 2 Research Methods  
In an effort to support the geotechnical information modeling and safety zoning, integration of BIM and GIS approach is proposed. The steps involved in the proposed method are illustrated in Figure 1. The integration of BIM-GIS for the geotechnical property modeling and division into safe construction zones comprises three parts, namely, geotechnical investigation data collection, three-dimensional spatial interpolation using GIS, and BIM-based visualization, management, and volume calculation. First, to model and evaluate the geotechnical properties of the area, the geotechnical investigation data should be surveyed and gathered into a database from the geotechnical and geospatial experts. Any geotechnical parameters obtained from the geotechnical site investigation can be used and not limited to certain parameters. Second, the geospatial interpolation technique is applied using GIS on the collected data to manipulate it statistically for the whole area and combine geographic and geotechnical information in a digital map having a high spatial resolution. Finally, the geotechnical zonation map is visualized and more parameters are appended to the geotechnical BIM model to enhance its functionality using BIM. Furthermore, the volume of each soil layer is calculated in the BIM environment based on the surface-to-surface method. The spatial interpolation of one-dimension geotechnical investigation data using GIS and its integration with BIM for 3D visualization and management makes it easy and helpful for the practitioners in site selection for safe construction.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### UAV Handover Based on Machine/Deep Learning  
In 5G networks, the use of mm waves with higher frequencies will present new challenges for HO management, which will be difficult to overcome using traditional methods. Significant attenuation in these frequency ranges will initially be present, limiting their transmission distance. As a result, more BSs are required to cover the same area as those using microwave frequencies [56]. Directional beams are used in mm-wave transmission. Obstacles in the path of the transmitted beam may prevent the user from connecting to the network or deteriorate signal quality. As a result, users in mm-wave communication networks must determine which beam to connect to at any given time to optimize their QoS. Deciding on the best beam has become a new factor to consider in the HO management process. The large number of beams which the user must choose from makes the HO technique significantly more difficult [57; 58; 59; 60].  
The network's self-optimization will improve with the use of machine learning techniques. ML techniques can learn diverse attributes from data provided by the network to maximize different network sections. They can detect hidden network features and patterns in network data that analytical methods are unable to detect [61]. They are self-adaptive, which means they can respond to changes in the network environment and, in some cases, anticipate future organizational or user needs. This allows the network to prepare for them when they occur [62]. They can be written in such a way that the preparatory stage of the calculation, which is often computationally expensive, can be completed offline before moving on to the actual calculation [47]. The planned program is broadcast online to allow for real-time optimization, yet the model is rarely updated since it encounters unnecessary data [63].
# Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction
## 4 Experiments
### Ablation Studies
#### 4.3.3 Influence of Positional Embedding and Linear Time Interval Embedding  
Table 6 shows the performance comparison of FESeq without positional embedding or linear time interval embedding. \"FESeq w/o Pos\" represents the proposed model without positional embedding, and \"FESeq w/o Time\" is the proposed model without linear time interval embedding. We can obtain two observations:  
* Both positional embedding and linear time interval embedding can help improve the model performance because the two types of embeddings reflect the order and the time intervals of the behaviors in a sequence, respectively, and can help extract the temporal dynamics from the user behaviors.
* The influence of positional embedding on the model performance is more significant than linear time interval embedding. On the one hand, positional embedding encodes the order relations of different behaviors from a user and can show the logic of causality in the user behaviors. On the other hand, notice in Table 2 that the maximum sequence length of both datasets is short (10 on the dataset Ele.me and 8 on the dataset Bundle), which leads to insignificant time interval differences in different user behaviors for the same user. Therefore, compared with positional embedding, linear time interval embedding has a smaller impact on the model performance.
## 4 Discussion
### Influences of Temperature Control Function Parameter Settings on Model Precision  
There are two parameters to set in the temperature control function, namely extremum \(u\) and median point \(r\), each of which affects the model's performance in a different way. From the perspective of mechanism analysis, the magnitude of \(r\) affects the curvature of the function; for small \(r\), the function value converges fast, which means that the model is going fast into a stable operation state but also that the odds that the model will run into a locally optimal solution increase. The magnitude of extremum \(u\) determines by what  
Figure 7: When the same “multi-scene semantics” RS image is retrieved by different statements, the weight distribution of each region of the image is significantly different. The change in color from white to red represents an increase in weight. This shows that CABIR can realize semantic recognition and shield image regions irrelevant to current retrieval. From left to right, the retrieval statements are: (**a**) “A plane is parked on the apron” and “There are large areas of brown farmland”; (**b**) “There is a large area of neat red buildings in the residential area” and “A playground with several basketball fields”.  
factor the probability of area selection increases during stable operation of the model. In the standard version of CABIR, we set \(r=3\) and \(u=5\). Therefore, by means of control variables, we let \(r=3\) and \(u=[1,\,5,\,10,\,15,\,20]\) and \(u=5\), respectively, to get the results shown in Figure 8. According to the results, the variation in model precision is consistent with our theoretical expectation: neither \(u\) nor \(r\) can be set too large or too small, and the model is more sensitive to the variation of \(r\). In this experiment, the wide value interval only indicates a variation trend (the settings in the standard version are not necessarily the optimal values of the parameters). But as long as the temperature control parameters are set within some relatively reasonable intervals, say \(r\in[2,\,10]\) and \(u\in[3,\,15]\), the model performance would boost dramatically. Moreover, even the relatively unreasonable values of the parameters have not made the results worse than when the temperature control function was removed. This suggests that our proposed temperature control function has indeed enhanced the stability of the model.
# Satellite Image Categorization Using Scalable Deep Learning
## 3 Materials and Methods
### Proposed Methodology  
This research aims to classify satellite images into different categories. The proposed methodology takes the satellite image as input and categorizes it into four different classes of terrains. Pre-processing is first applied to the input images, then a feature map is extracted, and, finally, the images are classified. A complete architectural diagram is presented in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Problem** & **Feature Extraction Approach** & **Machine Learning Model** \\ \hline [PERSON] et al. [45] & Scene classification & Deep learning based & CNN \\ \hline [PERSON] et al. [46] & Change detection & Deep learning based & Clustering \\ \hline [PERSON] et al. [47] & Land cover classification & Deep learning & CycleGAN and EfficientNet \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Samples of satellite images from Dataset-RSI-CB256.
## 3 Results
### Experimental Results in Urban Canyon 3
#### 3.4.2 Positioning Results Comparison  
The effectiveness of the adaptive GNSS-RTK/LIO fusion method for continuous position estimation is validated on urban-3. The trajectories generated by the adaptive integrated pipelines with thresholds \(el_{th}\) for the GNSS-RTK availability assessment equals \(90^{\circ},35^{\circ},25^{\circ},\) and \(15^{\circ}\) are presented respectively in Figure 11a-d. Fixed u-blox RTK solutions processed by RTKLIB [2] are reported on urban-3 as on urban-2. Among the fixed ones, the corresponding mean elevation angle mask of which is smaller than \(el_{th}\), are fused with LiDAR and IMU measurements as Equation (11) in our experiments. As \(el_{th}\) decreasing, the estimated trajectory achieves better accuracy which would be in better agreement with the ground truth. The box plot in Figure 11d compares the precision of the four trajectories produced by the adaptive fusion pipelines quantitatively.  
Table 5 shows the precision of the positioning results from alone GNSS-RTK measurements, the LIO, the LIO-SAM [42], and the four adaptive integrated pipelines quantitatively. Overall speaking, the ATE of the positioning results on urban-3 are large. The RMSE of ATE of the pure GNSS-RTK reaches up to 22.23 m. The LIO achieves the next best precision with the RMSE of 11.43 m suffering from accumulated drift. Among the four adaptive integrated pipelines, when \(el_{th}\) varies from \(90^{\circ}\) to \(15^{\circ}\), the RMSE of ATE is gradually improved. The precision of the LIO-SAM is comparable with the proposed method with \(el_{th}\) equal \(90^{\circ}\), both of which are categorized as conventional fusion methods. The pipeline with \(el_{th}\) set to \(15^{\circ}\) achieves the best precision with the RMSE of 10.02 m as marked in bold font in Table 3.  
Figure 11: **(a–d)** plots the trajectory generated by the adaptive fusion pipelines with the thresholds \(el_{th}\) for the GNSS-RTK availability assessment set as \(90^{\circ},35^{\circ},20^{\circ}\) and \(15^{\circ}\) respectively on urban-3. The black trajectory is the ground truth. The ATE of each estimated position is mapped on the trajectory referring to the color bar with the same scale on the right, on which the largest and the smallest tick label represents the maximum and the minimum ATE respectively while the middle one represents the midrange of them. **(e)** shows the boxplot of the ATE on the whole trajectory produced by the four pipelines.
## 3 Results
### Dexterous Grasping  
The grasping configuration results, as illustrated in Table 2, showcase the performance of LSTM, Gaussian Process (GP), and Transformer models across various objects using two-, three-, and five-finger grasps. All configurations demonstrated adaptability for stable objects like the Apple, Ball, and Box, which were successfully grasped by each model. Figure 10 shows the three-finger grasping of a Ball, Banana, Orange, and Tomato, highlighting the models' ability to adjust grasp configurations dynamically.  
Figure 10: Real-time grasping results for three-finger grasp configuration: (**a**) Ball, (**b**) Banana, (**c**) Orange, (**d**) Tomato.  
Figure 9: Real-time detection and segmentation for three-finger grasp configuration: (**a**) Apple, (**b**) Ball, (**c**) Banana, (**d**) Bottle, (**e**) Box, (**f**) Lemon, (**g**) Orange, (**h**) Strawberry, (**i**) Tomato misclassified as Ball.  
However, challenges arose with objects such as the Banana and Bottle when using the five-finger grasp, resulting in instability and suboptimal performance across all models. The LSTM model exhibited force fluctuations, struggling to maintain consistent force application, particularly when grasping objects with irregular shapes. The GP model, although effective in providing uncertainty estimates, tended to under-predict the required grasp force, resulting in occasional slippage. The Transformer model outperformed both LSTM and GP by dynamically adjusting grasp forces based on contextual relationships rather than relying solely on past states. This adaptability was evident in the three-finger grasp configuration, where the Transformer model maintained a 95.6% efficiency rate compared to 91.3% for LSTM and GP models.  
For more delicate items like Lemon, Orange, Strawberry, and Tomato, the models consistently opted for two- or three-finger configurations instead of five-finger grasps, as these proved to be more stable and effective. The grasp configuration system dynamically predicted these results based on object-specific characteristics, including geometry, grasping area, and stability levels, thereby enhancing the precision of the outcomes. Despite some limitations, the combination of grasp prediction with the advanced models ensured a balanced approach, achieving stable grasps for a wide variety of objects.
## 4 Mathematical Model
### Notations  
The definition of the developed mathematical model's parameters relies on the following sets and indexes:  
* \(M\): set of all material resources, where \(m=|M|\).
* \(N\): set of jobs (dishes to prepare), where \(n=|N|\) and \(\{0,n+1\}\) are two dummy jobs.
* \(J_{i}\): set of operations of job \(i\in N\), such that the operation \(j\in J_{i}\) is done before the operation \(j+1\in J_{i}\) and \(|J_{0}|=|J_{n+1}|=1\).
* \(Q_{i}\): number of portions (quantity) of job \(i\in N\).
* \(q_{i}\): number of portions in each sub-lot of job \(i\in N\).
* \(L_{i}\): set of sub-lots of job \(i\in N\), with \(|L_{0}|=|L_{n+1}|=1\) and \(l_{i}=|L_{i}|\) such that \(l_{i}=\lceil\frac{Q_{i}}{q_{i}}\rceil\).
* \(d_{i}\): due date of job \(i\in N\).
* \(M_{ij}\subset M\): set of material resources that can perform the operation \(j\in J_{i}\) of job \(i\in N\).
* \(R_{k}\): maximum capacity in number of portions of the material resource \(k\in M\).
* \(P^{\prime}_{ijk}\): unit processing time of operation \(j\in J_{i}\) of job \(i\in N\) on the material resource \(k\in M_{1}\).
* \(P_{ijk}\): processing time of operation \(j\in J_{i}\) of job \(i\in N\) on the material resource \(k\in M_{2}\cup M_{3}\).
* \(s_{ijhkg}\): setup time of material resource \(k\in M_{ij}\cap M_{hg}\) if the operation \(j\in J_{i}\) of job \(i\in N\) directly precedes the operation \(g\in J_{h}\) of job \(h\in N\) on the material resource \(k\in M_{ij}\cap M_{hg}\).
* \(T_{k}\): preparation time of the material resource \(k\) at the beginning of scheduling.
* \(E_{k}\): cleaning time of the material resource \(k\) at the end of scheduling.
* \([A_{k},Y_{k}]\): time window of availability of the material resource \(k\in M\).
* \(B\): big integer.
# Characteristics of Collimators Based on the Large-Mode-Area CMCF for Coupling Laser Beam
## 3 Experiments and Discussion
### Measurement and Discussion of Geometric Deviations  
A new collimator based on the large-mode-area single-mode CMCF was fabricated. Its alignment redundancy test was to verify whether the angular deviation performance was improved by the theoretical analysis in Section 2. When the CMCF-based collimator was used in the test system, a mode mismatch occurred at the fusion point with the SME, which usually results in high fusion loss. Our team fused SMFs to CMCFs by the manual mode of a special fiber fusion splicer (FujikuraTM, FSM-100 M/P), and the loss was as low as 0.1 dB. Beam expanding fibers were obtained by the manual fusion mode, the principal reason was Ge ion diffusion in SMF by an electrical discharge. Therefore, the fusion loss was reduced.  
The experimental setup for measuring the coupling deviation is shown in Figure 13. Two fiber collimators were fixed on two six-axis fiber alignment stages by homemade clamps. This experimental architecture was developed, which consisted of a homemade single-mode fiber laser as the light source, and an optical power meter for coupling loss measurement. This light source had a stable output at 1550 nm, whose power was set at 5 dBm. The fiber alignment stage was utilized for position alignment to minimize the coupling loss, which served as a starting point. Then the quantitative measurements were performed in three types of geometric deviations, from which the corresponding coupling loss was obtained.  
The coupling loss with the collimators with the CMCF was carried out by experimental measurements and study, and the ones with Coning SMF-28 were set as the experimental control group. Under the same optimal conditions, the coupling loss between the two SMF-based collimators was 0.25 dB, while the one between the two CMCF-based collimators was 0.37 dB. The coupling loss of off-axis, angular, and axial deviations was measured in the laboratory, as shown in Figure 14. Due to the limitation of the fiber alignment stage, the axial deviation was only measured to 36 mm. Table 1 gives the geometric deviations caused by misalignment for the two control experiments (SMF- and CMCF-based) when the  
Figure 12: The measuring R(\(\lambda\))\(\sim\)\(\lambda\) curve by the improved transmission power method.  
Figure 13: The experimental architecture of measuring deviations between two collimators.  
coupling loss \(\eta\) was certain. The experimental results show that the angular redundancy improved by 20% and off-axis redundancy decreases = d by 31.6% for \(\eta\) = 1 dB. The axial redundancy decreased by 68.75% for \(\eta\) = 0.2 dB. The results of the simulation and experiments, which were consistent with each other, indicate that the collimators with the large-mode-area CMCF improved the angular deviation, which is significant for the design of the collimators used in laser beam coupling.