esa-sceva/satcom-chunk-collection huggingface dataset:
# Diffusion Model as a Base for Cold Item Recommendation
## 3 Cold Item Recommender with a Diffusion Model Base
### Preliminary: Diffusion Model
#### 3.2.1 Overview of The Proposed Method  
We first describe the overview of the proposed method and then explain the details in the following subsections. Figure 1 shows the two-phase recommendation of the proposed method. In the first phase, the DM-based generator generates collaborative embeddings of cold items using their side information as input. Then, the generated collaborative embeddings of cold items and the collaborative embeddings of users calculated by the base recommender are fed into the refiner to calculate more accurate recommendations for each user. The generated embeddings contain some errors that originated from information deficiency. For instance, coarse-grained side information of the cold items is given as the generation condition, and valuable user-side information, the ground-truth collaborative embeddings of users, is not considered. We employed the refiner to correct the error in the generated collaborative embeddings by the DM-based generator.  
We train the DM-based generator first and then train the refiner using the trained generator. Although we adopted the refiner, it is worth noting that we can directly use the collaborative embeddings of cold items generated by the DM-based generator to calculate recommendation scores just by applying dot products with user collaborative embeddings.
# DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors
## 7 Conclusions  
A parallel hierarchical scheduling framework based on DAG tasks with limited deadlines is proposed in this article. Under this framework, the kinds of tasks are not distinguished, and a rule-based scheduling method is proposed which maximizes node parallelism to improve the schedulability of single DAG tasks. Based on the rules, a response-time analysis is developed that provides tighter bounds than existing analysis for (1) any scheduling method that prioritizes the critical path, and (2) scheduling methods with explicit execution order known a priori. We demonstrate that the proposed scheduling and analyzing methods outperform existing techniques. The scheduling framework has been implemented on avionic real-time systems platform, and through experiments, we know that the extra overhead brought by the method proposed in this chapter is acceptable. Finally, a simulation experiment is constructed to verify the adjustability of the framework. The experimental results demonstrate that the strategy proposed have better performance in the article.  
Based on the research conclusion of this article, there are still many directions worthy of further analysis in the future. First, we can consider scheduling multi-DAG task models on a given virtual computing platform according to different resource interfaces. Moreover, sharing resource models among DAG tasks is also an important direction for us to consider. Energy-sensitive scheduling algorithm for the DAG task model is also worthy of attention.  
Conceptualization, H.Y. and S.Z. ([PERSON]); methodology, H.Y. and S.Z. ([PERSON]); software, H.Y. and S.Z. ([PERSON]); validation, Y.G., H.Y. and X.S.; formal analysis, Y.G.; investigation, H.Y.; resources, S.Z. ([PERSON]); data curation, X.S.; writing--original draft preparation, H.Y.; writing--review and editing, Y.G.; visualization, X.S.; supervision, S.Z. ([PERSON]); project administration, S.Z. ([PERSON]); funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Scientific Research Projects of China, grant numeral MJ-2018-S-34.  
Not applicable.  
The work was supported by the school of computer at Northwestern Polytechnical University, some colleagues in University of York and Xi'an Aeronautical Computing Technique Research Institute.  
The authors declare no conflict of interest.  
Figure 11: Comparison of different synthetic workload; (**a**) Standardized utilization, (**b**) Density.
###### Abstract  
For cable-stayed bridges, cables are very important components to maintain the safety of the whole bridge structure. It is well-known that change in cable force reflects the health of the cable-stayed bridge. Therefore, it is necessary to detect and quantify local damage in cables prior to the occurrence of a failure. To this end, an improved residual force algorithm independent of static load vector was proposed in this study. The proposed method mainly makes use of the particularity that only a few coefficients in the residual force and static load vectors are nonzero. By combining two different static loading modes, a new damage indicator vector was defined in the method for damage localization and quantification. Compared with existing static residual force methods, the significant advantage of the proposed algorithm is that the specific value and loading position of the static load are not required in the damage identification process. This special advantage causes this method to not require special static loading, but instead uses any load vehicle. This advantage can make the operation process of structural damage identification based on static tests easier and faster. A single tower cable-stayed bridge structure was used to verify the feasibility of the proposed method in cable damage identification. It was shown that the proposed method successfully identified cable damage, even if the value and loading point of the static load were uncertain.  
damage identification; residual force vector; static load; displacement; single tower cable-stayed bridge +
Footnote †: journal:  
Article  
An Improved Static Residual Force Algorithm and Its Application in Cable Damage Identification for Cable-Stayed Bridges
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 5 Parametric Study
### Influence of Steel Strength  
As shown in Figure 20, the steel strength had little influence on the pull-out behavior of notched perfobond shear connectors. When the yield strength of the perfobond rib was increased from 235 MPa to 345 MPa, 390 MPa, 420 MPa, and 460 MPa, the variation of the pull-out resistance of the notched perfobond shear connector was no greater than 4%. The possible reason for this was that the pull-out failure of the notched perfobond shear connector was characterized by the shear of the concrete dowel and the perforating rebar in the hole. No obvious deformation of the notched perfobond rib was observed at the specimen failure. The shear failure of the concrete dowel and the perforating rebar in the hole occurred before the yield of the perfobond rib could happen. As a result, the pull-out resistance of the notched perfobond shear connector was hardly influenced by changing the strength of the structural steel.  
Figure 19: Influence of rebar strength. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 3 AntOR-Based Protocol  
AntOR is inspired in the AntHocNet algorithm, more specifically, in the specified version by the [PERSON]'s thesis [12], inheriting its characteristics of hybrid (reactive and proactive), multipath and adaptive protocol. Like its predecessor, it presents the following phases:  
* Routing setup: When starting the data session, the source node, on demand, sends agents to discover the available routes to the destination.
* Data routing: the data are sent out through the nodes to the destination using the route information, being able to utilize the multi-hop technique, i.e., sending data through intermediate nodes that act as routers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol Property** & **Type of Routing** & **Type Routes** & **QoS** & **Disjoint** & **Observations** \\ \hline Adaptive-SDR & Proactive & - & NO & NO & \begin{tabular}{c} High overhead. \\ Resource use. \\ \end{tabular} \\ \hline \hline \multirow{2}{*}{MABR} & Proactive & - & NO & NO & \begin{tabular}{c} Pheromone evaporation. \\ Theory Model. \\ \end{tabular} \\ \hline PERA & Proactive & - & NO & NO & High overhead. \\ \hline AntNet-RSLR & Proactive & - & NO & NO & High overhead. \\ \hline ARA & Reactive & - & NO & NO & No multimedia data. \\ \hline ADRA & Reactive & - & YES & NO & \begin{tabular}{c} Pheromone evaporation. \\ Better performance than DSR. \\ \end{tabular} \\ \hline PACONET & Reactive & - & NO & NO & Probabilistic Algorithm. \\ \hline Ant-AODV & Hybrid & - & NO & NO & Less latency and end-to-end delay. \\ \hline AntHocNet & Hybrid & Multipath & NO & NO & \
\begin{tabular}{c} Adaptive. \\ Equal or better performance than AODV. \\ \end{tabular} \\ \hline AntHocNet & Hybrid & Multipath & NO & NO & Less overhead. \\ (Ducatelle thesis) & Hybrid & - & NO & NO & With few nodes more delay than other hybrids. \\ \hline HOPNET & Hybrid & Multi-path & NO & YES &
\begin{tabular}{c} Pheromone separation. \\ Distance metric. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scheme of the ACO protocols.  
* Established path maintenance and exploration of new routes: it is the updated information of existing routes, and it tries to discover new ones. This phase consists of two stages: pheromone diffusion and ant proactive sending.
* Management of link failures: These occur because a node is out of the reach of the network or because it does not receive control messages, which are responsible for informing a node of its closest neighbors.  
In so far as the main differences of AntOR regarding AntHocNet, basically these consist of the introduction of the following elements/processes:  
* Specification of the disjoint link or node routes.
* Separation between the pheromones in the diffusion process.
* Using the distance metric in path exploration.  
These three characteristics influence especially Phases 1 and 3 of the algorithm, that is in the setup phase and maintenance and exploration of new routes.
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Localization Measurements  
To illustrate the potential application of the proposed bearing measurement method an indoor measurement setup was built. In this section, the performance of the system is analyzed through the localization accuracies with various amounts of reflections and tilt.  
The system was installed in a 5 m by 4 m furnished office room, with several white areas on the walls. The measurements utilized five or six beacons as anchor points. The nine reference positions were on the grid shown in the center of Figure 14. At each reference point the sensor was rotated,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reflective Material/Method** & **1.5 m** & **1.0 m** & **0.5 m** \\ \hline White paper (LS) & 0.8\({}^{\circ}\)/0.6\({}^{\circ}\) & 1.0\({}^{\circ}\)/0.6\({}^{\circ}\) & 3.0\({}^{\circ}\)/0.9\({}^{\circ}\) \\ Alu. Foil (LS) & 0.9\({}^{\circ}\)/0.6\({}^{\circ}\) & 1.5\({}^{\circ}\)/0.7\({}^{\circ}\) & 4.3\({}^{\circ}\)/1.1\({}^{\circ}\) \\ White paper (FD) & 1.7\({}^{\circ}\)/0.7\({}^{\circ}\) & 2.0\({}^{\circ}\)/0.7\({}^{\circ}\) & 6.2\({}^{\circ}\)/0.8\({}^{\circ}\) \\ Alu. Foil (FD) & 2.0\({}^{\circ}\)/0.7\({}^{\circ}\) & 3.5\({}^{\circ}\)/0.7\({}^{\circ}\) & 9.3\({}^{\circ}\)/0.8\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: The mean and standard deviation of the bearing estimation errors.  
Figure 13: Measured relative angle estimation error with different amount of tilt. (**a**) LS estimator, (**b**) FD estimator.  
taking 72 independent measurements. From the measured bearing values, the sensor position was estimated using a least-squares ADoA-based position estimator.
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 4 Evaluation  
To validate the performance of the proposed path tracker, simulations and real-car experiments were conducted. The performance verification compared the proposed 4 WS path tracker (proposed 4 WS) with a conventional 2 WS path tracker (conventional 2 WS), which uses a Stanley method to steer only the front wheels, and a conventional 4 WS path tracker (conventional 4 WS), which applies 30% of the front steering angle, calculated using the Stanley method, to the rear wheels in counter-phase [11]. The Stanley method was selected as the benchmark control algorithm because it is widely known to outperform pure pursuit among geometric-based path trackers. Furthermore, since this study focuses on low-speed driving, it does not include comparisons with dynamic-based path trackers, which have higher computational costs and rely more on dynamic models. The RMS (root mean square), MAX (maximum) value, and SD (standard deviation) of the absolute result values are extracted as numerical metrics for comparing the performance of each controller, validating the performance of the proposed controller. These additional metrics offer a more detailed representation of the variability in tracking performance across different trials. The SD provides insight into the consistency of the path tracker's performance, while the max error highlights the worst-case deviations.
## 3 Materials and Methods  
### Data Sources  
The strong motion data utilized in this study were acquired from Turkey's Disaster and Emergency Management Authority (AFAD). The AFAD operates the National Strong Motion Network that consists of 762 stations distributed all over Turkey. The network records the ground acceleration during earthquakes with magnitudes greater than 3.0. The data are stored and transmitted in a MiniSEED format, which contains the waveform samples, the station metadata, and the timing information. Following the occurrence of the earthquake, the aforementioned agency promptly disseminated pertinent strong motion record data on its official website. Subsequently, the data underwent several calibrations and updates [26]. The data used in this study are from the version published on 1 June 2023, comprising records from 379 stations. The distribution of peak ground acceleration (PGA) and station locations is shown in Figure 7. The strong motion instruments and sensor models for each station are listed below. There are a total of 87 stations that are equipped with the GEOSIG GMS AC-73 seismic instrument. Additionally, there are 140 stations that utilize the Guralp CMG-5T instrument. Furthermore, there are 107 stations that make use of the SARA ACEBOX sa10 instrument. Lastly, there are 45 stations that employ various other seismic instruments [27]. Table 2 presents a compilation of strong motion records acquired from the earthquake under consideration. The table includes relevant details such as station numbers, latitude and longitude coordinates, peak ground acceleration (PGA) values, epicentral distances, fault distances, and station locations. This information has been included in the table due to spatial constraints.  
Figure 5: Comparison of a landslide before (**left**) and after (**right**) the earthquake, showing the movement and deposition of soil and rocks on a slope. The images were obtained from Google Earth Pro, with the lat long coordinates of 37\({}^{\circ}\)00\({}^{\prime}\) N, 36\({}^{\circ}\)35\({}^{\prime}\) E and the date of 27 December 2022 (before) and 7 February 2023.  
Figure 6: Comparison of site liquefaction before (**left**) and after (**right**) the earthquake, showing the formation and expulsion of water and sand on a flat area. The images were obtained from Google Earth Pro, with the lat long coordinates of 36\({}^{\circ}\)15\({}^{\prime}\) N, 36\({}^{\circ}\)14\({}^{\prime}\) E and the date of 22 December 2022 (before) and 14 February 2023.
## 2 Materials and Methods
### Characterization of LA-1 and Loganin
#### 2.3.3 NMR Spectrometer  
The LA-1 was freeze-dried, and 5 mg was dissolved in 500 \(\mu\)L of DMSO-d6. Proton (1H) spectra were collected using an AVANCE 300 spectrometer (Avance II 300 MHz NMR, Bruker, Billerica, Germany).
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 2 Model and Method
### Model Description  
YOLO is mainly composed of a backbone, neck, and head. With the continuous improvement in lightweighted models, at present, YOLOv5 is a widely used object detection algorithm. However, people may occlude each other and become relatively small targets in chaotic and crowded scenes, and YOLOv5 often lacks sufficient feature information to cope with these problems. Therefore, in order to solve the occlusion problem and overcome the difficulty of insufficient sensitivity to small targets, it is necessary to add an appropriate attention mechanism to extract important features efficiently. Based on the analysis of the COP dataset in the previous section, the P (precision) value is the most important performance indicator. However, the backbone carries out feature extraction many times, resulting in a serious loss of fine-grained information in the upper layer of the network.  
Figure 4: Detailed information on COP dataset. (**a**) The number of instances for each category. (**b**) The distribution of instances being aligned. (**c**) The distribution of instances’ center points after being normalized. (**d**) The proportion of width and height.  
Therefore, BiFPN is introduced in the neck part to increase the cross-layer information interaction ability so that the information with the characteristics of occlusion and dense crowds can be well transmitted to the output layer.
## 2 Optimization of Sand Control Methods in Hydrate Reservoirs
### Characteristics Analysis of the Sand Control Screen  
The commonly used mechanical sand control screens at the present stage mainly include slotted screens, wire-wrapped screens, slit screens, pre-filled screens, metal cotton screens, etc. [17; 18; 19; 20]. The slotted screens are milled on a milling machine using a saw blade milling cutter, and the slots allow the formation fluid to pass through and prevent the formation of sand. However, the seepage area of the screen is quite small, the applicable sand control range is relatively narrow, and the gap is easily plugged or worn by fine silt sand. Their self-cleaning ability is poor, and the annular air of the screen and the screen pipe and the well wall are easily plugged by the formation sand particles or mud, resulting in an inability to produce sand. The seepage area of the wire-wrapped screens is small, and their applicability to highly curved wells and horizontal wells is relatively poor. Compared with slotted screens, slit screens have larger overflow areas, and a strong anti-erosion and anti-deformation ability due to the lateral opening of the overflow protection sleeve, but the applicable sand control range is narrower. Although prefilled screens can prevent the formation of sand, they have a high permeability, allowing the formation fluids to pass through, and have a higher sand prevention accuracy than ordinary screens. However, they are easily plugged by the formation of sand particles or mud. The metal cotton screens have a high strength, a strong anti-deformation ability, a good permeability, a large filtration area, and are suitable for different well types such as vertical wells and horizontal wells. They have a high sand control accuracy. However, the prevention and control accuracy when applied to the formation with fine sand and gravel diameter formations is not high. The process technology is not yet mature, and the cost is relatively high. The ability of the expansion screens to apply to complex wellbore is not good, and the fine control of the expansion process is difficult, and their ability to adapt to complex working conditions is poor.  
Therefore, for the argillaceous fine silt gas hydrate reservoirs, how can we achieve a better sand control effect? Based on a comprehensive analysis of the performance characteristics of various types of sand control screens, the current screens do not have strong pertin in sand control for hydrate argillaceous reservoirs. The overall coordination between the sand plugging ability and the fluid flowing capacity of the screens still needs to be improved. For example, some screens may over-control sand, resulting in an increase in the flow resistance in the wellbore, which is not conducive to the release of production capacity. Meanwhile, there are still deficiencies in aspects such as the integration of sand control and water control. Therefore, it is necessary to carry out a targeted evaluation and analysis, and to innovate and develop new types of screen structures with the optimized appropriate screen materials.
# Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios
## 3 Related Work  
A review of the literature shows several related scenarios that have been previously studied. In [8], results are shown for a simulation performed with LoRaWAN technology and class B devices. The devices were distributed within a radius of 6100 m. In this work, two simulations were performed, one with 1% and the other with 10% channel limitation, to study the downlink performance, but this work did not consider the impact of enabling ADR because DR was kept static during simulations.  
In [9], the mechanism of ADR was analyzed using simulations. The LoRaWANSim simulator was used to carry out a study of the ADR mechanism. The chosen duty cycle was 1% with a radius of 670 m. The results show the network behavior for a different number of nodes. This work did not analyze class B device operation.  
In [10], an indoor industrial scenario with LoRaWAN technology with class A devices was analyzed. In this case, the simulations were performed with the ns-3 simulator. Simulations were performed with a number of nodes ranging from 10 to 1000 within a radius of 200 m. The simulations were done with a constant SF, comparing those of SF7 and SF12, comparing the probability of success. Moreover, battery consumption was also compared with other technologies such as IEEE 802.15.4.  
The authors of [11] analyzed the possibility of optimizing performance by modifying the SF used, but instead of using the ADR mechanism, the gradient projection method was chosen. Simulations were performed for a number of nodes from 0 to 10,000 with the use of SF7, SF8, and SF9. In this case, the length of the packet was a consideration in optimizing performance.  
A new simulator, LoRa-MAB, was analyzed in [12]. The performance of LoRaWAN was evaluated with 100 nodes, with the possibility to select one of the six SFs available from 7 to 12. Nodes were distributed within a radius of 4.5 km. The duty cycle limitation was 1%. In this case, the algorithm used improved the performance of LoRaWAN in the values of successful packages and energy consumption. Conversely, the connection times were too long.  
In [13], the FADR algorithm with the LoRaSim simulator was presented. The simulations were performed for a number of nodes from 100 to 4000 within a radius of 100 to 3200 m. Said mechanism controlled the transmission power but could not completely eliminate all collisions.  
In the different articles described, some works do not consider ADR or class B operation at all, while other studies try to propose a solution for the adaptive data rate mechanism which does not follow the LoRaWAN standards. In this present work, an implementation of the ADR mechanism is carried out following the standard for a realistic industrial indoor setting.