esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Assembly Process KG Inference Method Flow  
In this paper, corresponding algorithms are designed for the KG inference tasks of knowledge decision, entity and relation completion respectively. The knowledge decision task is based on logical rules based on entity type. The entity and relation completion task includes steps such as data processing, self-adversarial sample generation, model training and evaluation. The algorithm flow is illustrated in Figure 2.  
This assembly process KG inference algorithm mainly includes the following steps:  
1. KG information and logical rule extraction:  
The information in the quintuple dataset including head entity, tail entity, relation, head entity type and tail entity type respectively are extracted, and the information in the logical rule dataset including head entity type, relation and tail entity type respectively are extracted. Based on the constructed data dictionaries, word vectorization of the information above is carried out.
2. Negative sample generation:  
For different tasks such as entity completion, relation completion and knowledge judgment, a self-adversarial negative sample generation method considering entity type is proposed, and the accuracy of negative sample generation is improved by constructing  
Figure 2: Flowchart of inference algorithm integrating logical rules and embedded learning.  
entity dictionary, relation dictionary, and entity type dictionary. Specifically, negative sample generation for entity completion task is realized based on entity dictionary and entity type dictionary while negative sample generation for relation completion task is realized based on relation dictionary. Negative sample generation for knowledge judgment is realized according to logical rules. This novel self-adversarial negative sampling technique is proposed for efficiently and effectively training the embedded learning model.  
3. Construction of embedded learning model:  
Based on vectorized entity, relation and entity type information, entity and relation modeling and association expression are carried out in vector space according to different representation learning models. Then loss function is constructed according to generated positive and negative samples.  
4. Logical rule embedded:  
Logical rules are vectorized based on the constructed data dictionaries. Then the vectorized logical rules are embedded into the model training process as knowledge constraints so as to guarantee the accuracy of positive and negative samples generation. Also, logical rules are introduced into loss function for knowledge judgment task.  
5. Improved model training and evaluation:  
Improved model is trained and its performance is evaluated for different assembly process KG completion tasks.  
Compared with the original embedded learning model, this paper improves the positive and negative sample generation, model training and logical rules embedded. The additional logical rules embedded module is used for negative sample determination and result determination, without increasing the algorithm complexity or reducing the algorithm's computational efficiency.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques  
The localization algorithms for IoT networks depend on a variety of measurement techniques as discussed in in this section. The important factors that affect the precision of localization estimate include the network topology, node density, number of anchor nodes, synchronizing timing, bandwidth and the geometrical dimensions. Furthermore, sensor nodes in the network can be static or movable, and their locations are determined either by using absolute coordinates or corresponding to anchor nodes. Localization algorithms may use 2-dimensional (2D) or 3-dimensional (3D) coordinates. The measurement techniques for localization of IoT nodes are broadly divided into two categories, viz. range free and range based. These two types of measurement techniques are explained in this section.
## 4 Discussion and Future Research
For these, the geometrical model and the simulation model must be developed; this can be a significant step for future research, as based on the analysis of the data from Budapest (3 shopping malls and 1 shopping area), 135 of 477 stores have business-to-customer deliveries (28%), 116 from 433 stores have incoming deliveries from other stores (27%), and 171 from 464 stores have outgoing deliveries to other stores (37%). If we would like to examine the business-to-customer deliveries, it will be important to investigate the so-called micro-hubs [51], as they can have a significant role in the geometrical model-based city logistics system with business-to-customer deliveries. Figure 17 shows those delivery transactions which must be added to the model in future research: the deliveries between the stores of the shopping malls by red lines and the home deliveries by blue lines; their geometrical model, graph-theory-based description, and the possible role of micro-hubs in the deliveries must be examined in the next steps. As a summary of the results of the paper, in Figure 18, the main steps of the application of the geometrical model-based new approach can be seen. In this research, the development of the graph theory-based model and notation was motivated by the results provided by a previously developed simulation model. In future projects, the first steps will be to describe the geometrical model of the examined real system and to collect data about the CSDLs; based on these, it will be possible to develop a simulation model, and the results of the simulation together with the geometrical model of the real system and the reference geometrical model can provide data for the AHP-based multicriterial ranking which makes it possible to decide about the application of the new approach for given cities. Figure 17: The examined concept with the deliveries between stores and home deliveries.
## 1 Introduction
Specifically, in this control scheme, the ESMO was adopted in the outer loop to observe not only contact force with an environment but also the joint velocities which are proven to be robust against unknown friction force in hydraulic actuators. Meanwhile, an ESO was employed to address matched disturbance in hydraulic systems. Nonetheless, it can be observed that ESMOs have not been applied in EHS control systems in the literature. Motivated by the above discussion, a novel trajectory adjustment mechanism-based active disturbance rejection control (ADRC) aiming to achieve high-accuracy tracking performance for EHSs with hydraulic rotary actuators (HRAs) in the presence of immeasurable velocity and lumped uncertainties caused by parameter deviations, modeling errors, and unknown external disturbances is originally proposed in this article. The main advantages of the proposed control strategy are summarized as follows:  
1. A novel trajectory adjustment mechanism-based active disturbance compensation control framework with prescribed tracking performance is introduced to achieve a high-accuracy tracking performance for an EHS subject to disturbances, model uncertainties, and both kinematic and dynamic constraints. 2. Compared to the conventional ESO [44; 52], with the same observer bandwidth, an ESMO is developed for to estimate the angular velocity more accurately and better react against unknown fast-changing external loads in the mechanical system. In addition, a new disturbance rejection mechanism in which a LESO and an ESMO are combined to obtain a better estimation performance. Accordingly, a higher precision tracking capability is achieved. 3. As a corrective version of an approach in [34], for the first time, the PPF and CF approaches are successfully coordinated in the backstepping framework for EHSs to not only efficiently reduce the computational burden and avoid the \"explosion of complexity\" but also guarantee the prescribed tracking performance. 4. The stability of the closed-loop system is rigorously demonstrated using the Lyapunov theory. The superiority of the suggested method is convincingly validated through comparative numerical simulation results in MATLAB/Simulink environment. The rest of the article is structured as follows. In Section 2, the system modeling and control problem placement are provided. The observers, i.e., an ESMO and a LESO; trajectory adjustment mechanism; and control strategy are developed in Section 3. Comparative numerical simulations are conducted in Section 4. Finally, Section 5 concludes this paper.
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
###### Abstract  
This paper proposes a rail defect location method based on a single mode extraction algorithm (SMEA) of ultrasonic guided waves. Simulation analysis and verification were conducted. The dispersion curves of a CHN60 rail were obtained using the semi-analytical finite element method, and the modal data of the guided waves were determined. According to the inverse transformation of the excitation response algorithm, modal identification under low-frequency and high-frequency excitation was realized, and the vibration displacements at other positions of a rail were successfully predicted. Furthermore, an SMEA for guided waves is proposed, through which the single extraction results of four modes were successfully obtained when the rail was excited along different excitation directions at a frequency of 200 Hz. In addition, the SMEA was applied to defect location detection, and the single reflection mode waveform of the defect was extracted. Based on the group velocity of the mode and its propagation time, the distance between the defect and the excitation point was measured, and the defect location was predicted as a result. Moreover, the SMEA was applied to locate the railhead defect. The detection mode, the frequency, and the excitation method Were selected through the dispersion curves and modal identification results, and a series of signals of the sampling nodes were obtained using the three-dimensional finite element software ANSYS. The distance between the defect and the excitation point was calculated using the SMEA result. When compared with the structure of the simulated model, the errors obtained were all less than 0.5 m, proving the efficacy of this method in precisely locating rail defects, thus providing an innovated solution for rail defect location.  
Keywords:rail; ultrasonic guided wave; semi-analytical finite element; single mode extraction algorithm; defect location +
Footnote †: journal: Applied Sciences
## 4 Results
### Pore Systems
#### 4.3.1 Qualitative Analysis
(**j**) Authigenic quartz microcrystalline, well L81, delta front sandstone, 3899.4 m (CTS, \(-\)). (**k**) Debris dissolution pores, well L81, delta front sandstone, 3899.41 m (CTS, \(+\)). (**l**) Carbonate cement dissolution pores, feldspar intragranular dissolution pores, well T305, delta front sandstone, 4204.7 m (CTS, \(+\)). (**m**) Kaolinite cement, well L81, delta front sandstone, 3851.7 m (SEM). (**n**) Carbonate cement, well T306, delta front sandstone, 4017.2 m (CTS, \(+\)). (**o**) Carbonate cement and carbonate cement dissolution pores, well T306, delta front sandstone, 4006.47 m (CTS, \(-\)). (**p**) Microfractures and some microfractures are filled with calcite, well T306, delta front sandstone, 4015.8 m (CTS, \(+\)). (**q**) Quartz intragranular dissolution pores, residual intergranular pores, well T305, delta front sandstone, 4204.7 m (SEM). (**r**) Pyrite microcrystalline, residual intergranular pores, well T305, delta front sandstone, 4208.5 m (SEM). (**s**) Illite clay interstitial material and intercrystalline microcopes, well L81, delta front sandstone, 3851.7 m (SEM). (**t**) Feldspar intragranular dissolution pores, residual intergranular pores, well L86, delta front sandstone, 4096.6 m (SEM). The tight reservoirs of the delta front of sand Groups 4-6 in Es\({}_{3}\) in the Linnan Subsag are generally filled with carbonate (calcite and dolomite) cementations and clay gap fillers (Figure 5i-t). Kaolinite clay cementation can be observed incidentally (Figure 5m), and secondary enlargement of quartz (Figure 5j) and pyrite (Figure 5r) can be observed under the microscope. The degree of pore development is generally low. The main pore spaces are mineral particle intergranular dissolution pores, residual intergranular pores, feldspar intragranular dissolution pores, and debris dissolution pores (Figure 5i,k,l,t). The secondary pore spaces include the intergranular micropores of clay minerals, microcracks (Figure 5p,s), and a small number of carbonate cementation intergranular dissolution pores (Figure 5l,o). Occasionally, intragranular dissolution pores of quartz particles can be observed (Figure 5q). The overall surface pore rate is between 0.86% and 2.17%, and the average rate is 1.63%. The overall surface pore rate of the turbidite reservoirs is relatively high. The primary intergranular pores, intergranular dissolution pores, and intragranular dissolution pores are relatively developed, whereas the carbonate cementation is relatively weak. Therefore, the degree of pore development in turbidite reservoirs is greater than that in reservoirs at the delta front.
## 3 Results
### Hfq Deletion Results in Bacteria with Faster Growth and More Active Metabolism
WT, \(\Delta\)lifq and pHfq strains were grown in TSB to OD\({}_{600}\) of 0.9 and both the intracellular ATP levels (**c**) and NAD\({}^{+}\)/NADH ratio were determined (**d**). Asterisks represent statistical differences with respect to the WT strain (**_p_ \(<\) 0.001). Data represent the means \(\pm\) standard deviations (\(n=3\)).
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 5 Taxonomy for Energy-Efficient and Energy-Balanced Routing Protocols  
Finding the energy-balanced route to the BS from the source node in order to maintain the network lifetime and functionality for a reasonable duration is the objective of all energy-efficient and energy-balanced routing protocols for WSNs. The techniques and approaches might differ, but the ultimate goal is to balance the energy consumption in the network in order to extend the network lifetime and functionality. As explained, different measures have to be considered in order to achieve this goal. Different research works on routing protocols for WSNs are ongoing to proffer a solution to its limited energy problem. We study and classify some of the state-of-the-art energy-efficient and energy-balanced routing protocols found in literature. The concept, objectives, and challenges of designing an energy-efficient and energy-balanced routing protocol as explained in Section 4.2 form the basis of our classification.  
The studied energy-efficient and energy-balanced routing protocols for WSNs are classified based on the their mode of communication towards the BS into the single-hop communication method and multi-hop communication method. As shown in the subsequent subsections, we realise and emphasise that all these studied energy-efficient and energy-balanced routing protocols for WSNs fall under any of these modes of communication as pictured in Figure 6. We explain the categories included in Figure 6, while also classifying the studied routing protocols under these categories. The studied energy-efficient and energy-balanced routing protocols are also grouped based on the solution types or algorithms, and design decision variables used in the algorithms. In addition, we discuss some of the advantages and disadvantages of these studied energy-efficient and energy-balanced routing protocols with respect to the choice of the input decision variables.
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 3 UAV-Assisted Emergency Monitoring and Response System
### System Protocols
#### 3.3.2 Abnormal Situation Response  
When a smart sensor detects that the temperature has climbed above a set threshold, the server immediately sends a message to the drones and security managers, informing them of the location where the abnormal situation has been detected, as well as the present temperature at that location. Upon receiving that message, the outdoor drone moves to the location under question, where it takes photos of the area and immediately sends them back to the server. Concurrently, inside the building on the floor where the abnormal situation was detected, an indoor drone moves to the location. Since the temperature sensor is located at the entrance way to the room, it cannot determine whether the abnormal temperature has occurred inside the room or outside in the hallway. Therefore, when the drone arrives at the location of the abnormal temperature change, it first photographs the hallway area just outside the room and sends that data to the server. Then, it moves into the room to take photographs there. To enter the room, the drone requires authentication, and it is outfitted with a beacon sensor for this purpose. Each drone has its own unique ID that is stored on the server. Using Bluetooth, the drone sends this ID to the smart sensor, which checks against the data in the server to determine whether or not the drone has permission to enter the room. To accomplish this authentication process, we make use of the AllJoyn security platform, which quickly certifies the permission status of the drone. Once this permission has been certified, the door automatically opens, and the drone moves into the room to take photos there. While all of this is happening, the security manager can monitor the whole process on a smartphone, viewing the photographs taken by the drones, as well as the video provided by the CCTV cameras. In addition, when a suspicious situation such as smoke detection occurs, the existing smoke-alert system is activated. The smart sensor transmits smoke data and the video and photo data of CCTV or UAV to the manager. Using this information, the manager can come to a final decision regarding the status of the situation, whether it is an emergency or not.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
###### Abstract  
To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during intersatellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.  
collaborative scheduling; spatial information network; resource coordination; genetic algorithm; particle swarm optimization algorithm
# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 4 Experiments
### ImageNet Dataset  
The ImageNet dataset stands as one of the most influential and widely utilized visual databases in the field of computer vision. Introduced in 2009 by [PERSON]'s team [36], this large-scale hierarchical image database contains over 15 million labeled images spanning more than 20,000 categories. The dataset is organized using WordNet's hierarchical structure, which ensures semantic relationships between object classes, making it a rich resource for training and evaluating machine learning models. ImageNet's introduction revolutionized the field of machine learning, particularly in deep learning for image recognition tasks, and has since become a benchmark for evaluating performance in image classification, object detection, and segmentation.  
The prominence of ImageNet was further amplified by the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), an annual competition launched in 2010. In this challenge, top research teams competed to achieve state-of-the-art performance across various visual tasks. A landmark breakthrough occurred in 2012 with the introduction of AlexNet [37], a deep convolutional neural network that demonstrated unparalleled performance improvements in image classification. This achievement marked the beginning of the deep learning era, catalyzing the development of increasingly sophisticated architectures such as VGG, ResNet, and GoogLeNet.  
In this study, we utilize a subset of the ImageNet 2012 dataset, comprising 600 images across 12 specific categories, to evaluate our proposed interpretable methods. The selected subset focuses on domains where prior shape and appearance information can be effectively integrated, enabling a robust assessment of our framework's performance.
## 6 Potential Experimental Setup
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Operation** & **Scheme** & **Speed (Gb/s)** & **Methodology** & **ER (dB)** & **QF** & **Performance Context** \\ \hline [22] & Comparator & QD-SOA & 160 & Simulation & 10 & 9.00 & Speed/QF \\ \multirow{-2}{*}{[23]} & Comparator & SOA & 10 & Simulation & 9 & NR & Lower-speed \\  & & & & & & baseline \\ \multirow{-2}{*}{[24]} & \multirow{-2}{*}{Encoder} & SOA & 40 & Experimental & 10 & NR & Experimental \\  & & & & & & & & \\ \multirow{-2}{*}{[25]} & \multirow{-2}{*}{Encoder and comparator} & \multirow{-2}{*}{SOA} & \multirow{-2}{*}{40} & \multirow{-2}{*}{Experimental} & \multirow{-2}{*}{10} & \multirow{-2}{*}{NR} & Hybrid design \\  & & & & & & & reference \\ \multirow{-2}{*}{[26]} & \multirow{-2}{*}{Comparator} & \multirow{-2}{*}{SOA} & \multirow{-2}{*}{40} & \multirow{-2}{*}{Experimental} & \multirow{-2}{*}{10} & \multirow{-2}{*}{NR} & Experimental \\  & & & & & & & comparator \\ \multirow{-2}{*}{This work} & \multirow{-2}{*}{Encoder and comparator} & \multirow{-2}{*}{CR-SOA} & \multirow{-2}{*}{120} & \multirow{-2}{*}{Simulation} & \multirow{-2}{*}{10} & \multirow{-2}{*}{8.33–9.05} & Advanced \\  & & & & & & & \\ \hline \multicolumn{8}{c}{(NR = Not Reported).} \\ \end{tabular}
\end{table} 
Table 3: Performance comparison of encoder/comparator designs using different SOA technologies. XGM effect. The AND operation (AB) is realized in the CR-SOA-MZI3, where the combined signals A, B, and CW generate the output at \(\lambda_{\text{B}}\). The NOR operation (\(\overline{\text{A}}\)\(\overline{\text{B}}\)) is executed in the CR-SOA-MZI4, where the XGM effect enables the logic operation. For the XNOR gate (AB + \(\overline{\text{A}}\)\(\overline{\text{B}}\)), the outputs from the AND and NOR gates are combined. OBPFs with narrow bandwidths are placed at the output of each CR-SOA-MZI to select only the desired logic gate output, eliminating any noise or unwanted signals. The output signals are further amplified using erbium-doped fiber amplifiers (EDFAs) to maintain the desired power levels for signal analysis. To assess the system's performance, an optical spectrum analyzer (OSA) measures the optical spectrum and signal quality, while a digital communications analyzer (DCA) evaluates the output waveforms, providing insights into timing, logic levels, and signal integrity. Metrics such as the QF are used to verify the precision and efficiency of the logic gates. The system operates at data rates as high as 120 Gb/s, making it a promising solution for high-speed, all-optical digital encoders and comparators in optical computing and photonic logic circuits.