esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge Graph Recommendation Model Based on Feature Space Fusion
## 3 Our Model
### Model Introduction
#### 3.2.3 Feature Space Fusion Module  
Behavioral features and content features describe the features of users and items from two different aspects of interactive behavior and knowledge graph content. The fusion of the two features is able to enrich the expressions of users and items and enhance the accuracy of recommendation. However, as analyzed in Section 3.1, behavioral features and content features contain different semantic information and belong to different feature spaces. Direct fusion will result in a \"noise\" problem. To solve this problem, the model proposes the feature space fusion method: first, in the behavioral feature space, the behavioral features of users and items are constructed by extracting the behavioral feature from the user-item bipartite graph. Then, in the content feature space, the attention mechanism is applied to extract the content information related to users and items from the knowledge graph, and the content feature vector of users and items is constructed. Finally, through the feature space fusion model, on the one hand, through the projection operation, the behavior features and content features are projected into the preference feature space so that they are in the same feature space. Specifically, the behavior feature vector \(u_{\text{cf}}\),\(v_{\text{cf}}\) and content feature vector \(u_{\text{kg}}\),\(v_{\text{kg}}\) are projected into the same preference feature space through the projection matrix \(M_{\text{cf}}\),\(M_{\text{kg}}\). On the other hand, through feature cross, the behavioral feature and content feature extract important feature subset and filter irrelevant semantic features. Specifically, behavioral features and content features are spliced in the preference feature space, and then feature cross is carried out through multilayer perceptron (MLP) to output the complete representations of user and item feature vectors \(u_{\text{f}}\) and \(v_{\text{f}}\):  
\[u_{\text{cf}}^{\prime} =M_{\text{cf}}u_{\text{cf}} \tag{19}\] \[u_{\text{kg}}^{\prime} =M_{\text{kg}}u_{\text{kg}}\] (20) \[v_{\text{cf}}^{\prime} =M_{\text{cf}}v_{\text{cf}}\] (21) \[v_{\text{kg}}^{\prime} =M_{\text{kg}}v_{\text{kg}}\] (22) \[u_{\text{f}} =\sigma\Big{(}W\Big{(}u_{\text{cf}}^{\prime}||u_{\text{kg}}^{ \prime}\Big{)}+b\Big{)}\] (23) \[v_{\text{f}} =\sigma\Big{(}W\Big{(}v_{\text{cf}}^{\prime}||v_{\text{kg}}^{ \prime}\Big{)}+b\Big{)} \tag{24}\]  
where \(M_{\text{cf}}\) and \(M_{\text{kg}}\) represent the behavioral feature projection matrix and content feature projection matrix, respectively, and \(W\) and \(b\) are the parameters to be trained.
## 4 The Solution Method Based on Modified NSGA-II
### Procedures of Evolutionary Solving  
1. With the planning result determined by each individual, solve the simplified multi-scenario TNEP problem in turn. The optimization results, which include transmission investment cost and curtailed renewable generation, are considered as two fitness values for the corresponding individual.
2. For all individuals, carry out the non-dominated and crowding sorting according to the fitness values.
3. On the basis of the sorting results, tournament selection, single-point crossover and mutation are implemented to generate offspring. In this paper, the mutation manipulation is to add or remove a line for a selected right-of-way with 50/50 probability [5; 6].
4. Likewise, solve the simplified TNEP problem for each offspring, and integrate the offspring and parent population for non-dominated and crowding sorting. Then update the population by removing the individuals with the lowest ranking.  
* If maximum iterations are met, go to step (f). Otherwise, update the parameters of crossover and mutation as follows, then go to step (c): \[P_{CR}=0.8-\left\lceil\frac{it_{c}}{it_{a}}\right\rceil\times 0.1\] (24) \[P_{MU}=0.2+\left\lceil\frac{it_{c}}{it_{a}}\right\rceil\times 0.1\,,\ d_{MU}=6- \left\lceil\frac{it_{c}}{it_{a}}\right\rceil\times 0.1\] (25) where \(P_{CR}\) and \(P_{MU}\) are the parameters of crossover probability and mutation probability respectively; \(it_{c}\) is the current number of iterations; \(it_{a}\) is an artificial parameter, which is set to be 20% of the maximum iteration number; symbol \" \(\left\lceil\right\rceil\)\" denotes the rounded-up value of a quantity; and \(d_{MU}\) is the mutation operator, which represents the number of right-of-ways that should mutate in a mutation manipulation.
* Output the planning results from the Pareto optimal front.  
According to the idea in [23], this paper proposes an adaptive control strategy for the parameters of crossover and mutation in step (e). With Equations (24) and (25), large values of \(P_{CR}\) and \(d_{MU}\) are employed in the early iterations to diversify the search area and to ensure the global optimization. In later iterations, as the global optimal area is gradually determined, a large value of \(P_{MU}\) is employed while \(d_{MU}\) decreases, which means that a large number of individuals will be mutated and few right-of-ways (\(d_{MU}\) will eventually be decreased to 1) will be changed for each individual. Since the mutation manipulation is only to add or remove a line for each selected right-of-way, a large value of \(P_{MU}\) is employed in later iterations to enhance the local search; this will not have an adverse effect on the optimal solution.
## 2 Related Work
Moreover, it adds size-balance cluster formation (SBCF), inter-cluster energy adjustment (ICEA), and energy-aware data forwarding (EDF) schemes. CEEC is also focused on homogeneous WSNs and does not address the complex dynamics of a real-life heterogeneous wireless sensor network-based IoTs. Despite the advancements made by the aforementioned protocols, several outstanding research problems remain unaddressed. Many existing protocols fail to fully account for the diverse heterogeneity of sensor nodes, particularly in balancing energy consumption across both intra-cluster and inter-cluster communications. This limitation leads to premature node failures, especially in large-scale networks with varying node capabilities. Current strategies often overlook the need for an optimal relay-node selection and rotation scheme, which is essential for maintaining network longevity in heterogeneous environments. This oversight can result in uneven energy distribution and increased communication overhead. Moreover, most existing protocols are either tailored to small-scale networks or limited to specific levels of energy heterogeneity, making them less effective in real-world IoT applications that demand flexibility and scalability. In summary, although significant progress has been made in developing clustering and routing protocols for energy-efficient and scalable WSNs and HWSNs, several critical challenges remain. These include the need for more comprehensive management of node heterogeneity, improved strategies for balancing energy consumption, and the development of protocols that can adapt to varying network sizes and levels of heterogeneity. This research addresses these challenges by introducing innovative methods that integrate multi-level heterogeneity into the clustering and routing processes, thereby enhancing network efficiency, scalability, and longevity.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology
### Semi-Automatic Point of Interest Recognition and Tracking  
While the fully automatic pipeline for recognizing and tracking POIs is effective in many situations, there are instances where it may not be sufficient. For example, the automatic pipeline may fail to detect a POI or recognize it too late. Additionally, video operators may want to track an object that is not included in the reference database. To address these limitations, the proposed software provides an interface that allows for a manual selection of objects of interest. This interface displays a live helicopter video stream, allowing operators to interact directly with the footage. By clicking or drawing on any part of the stream, operators can manually initiate the POI recognition and tracking pipeline, as illustrated in Figure 8.  
The manual input from the user is used to replace the automatic detection of salient regions and points. Most of the subsequent steps in the pipeline are reused to maintain con  
Figure 8: Visualization of the manual input (yellow) for the recognition and tracking pipeline by drawing on the live broadcast.  
Figure 7: Example visualization of the tracked inner bounding box using the MedianFlow tracker with (**a**,**b**) being 20 s apart from each other.  
sistency, as depicted in Figure 9. In contrast to the automatic pipeline, the semi-automatic approach reorders the POI recognition and tracking steps. After the user input is processed by the Segment Anything Model to generate a mask of the object, the tracking process is immediately initiated. This allows the broadcast operator to track objects that may not be present in the reference database. Concurrently, the POI recognition methodology is executed on a background thread. If the POI is successfully recognized, the information is passed to the visualization engine. In case of failure, the recognition process is reattempted every second, up to a maximum of 10 tries.  
Note that the segmentation mask refinement step, as detailed in Section 3.2.5, is omitted in the semi-automatic methodology. On the one hand, this decision was made to provide the user with full control over the input prompt of the SAM stage. On the other hand, it is uncertain if the selected object is in the reference database, which makes waiting for the recognition result unnecessary.  
Since the semi-automatic approach reuses POI recognition and tracking components, this leads to performance levels that are comparable with, and potentially exceed, those of the fully automatic pipeline. By omitting the saliency detection component, the computational overhead and potential errors associated with it are reduced.  
By incorporating a semi-automatic pipeline alongside the fully automatic one, the proposed software offers flexibility to various scenarios encountered during live broadcasts.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 3 Main Error Sources and Suppression Methods of Dynamic Measurement
### Dynamic Effect of the Carrier
#### 3.3.2 Horizontal Acceleration  
The stabilized platform provides a horizontal reference for the atom gravimeter to ensure that it measures the acceleration in the vertical direction. However, due to the  
Figure 7: Scene of accelerometer temperature experiment.  
influence of the dynamic environment and the limitations of the technical performance of the platform itself, the platform cannot be absolutely horizontal. There is an inclination angle, and gravity measurements will inevitably be disturbed by the horizontal acceleration of the carrier. Therefore, it is necessary to establish a model and correct it, which is called horizontal acceleration correction or tilt correction.  
The researchers have proposed a variety of correction models, which can be divided into two categories. One is to directly use accelerometer measurements and navigation information to calculate the correction:  
\[\delta a_{\rm H}=(f_{x}^{2}+f_{y}^{2}-a_{\rm E}^{2}-a_{\rm N}^{2})/2g_{\rm m}, \tag{14}\]  
where, \(f_{x}\) and \(f_{y}\) are the lateral and longitudinal horizontal accelerations sensitive to the dual-stabilized platform, \(a_{\rm E}\) and \(a_{\rm N}\) are the eastward and northward horizontal accelerations of the carrier, and \(g_{\rm m}\) is the gravity observations. The other is to determine the tilt angle of the platform before calculating the correction:  
\[\delta a_{\rm H}=g_{\rm m}(\theta_{x}^{2}+\theta_{y}^{2})/2-a_{\rm Ex}\theta_{ x}-a_{\rm Ny}\theta_{y}, \tag{15}\]  
where \(\theta_{x}\) and \(\theta_{y}\) are the horizontal and longitudinal tilt angles of the platform, and \(a_{\rm Ex}\) and \(a_{\rm Ny}\) are the horizontal and longitudinal components of \(a_{\rm E}\) and \(a_{\rm N}\)[59].  
The above are commonly used correction models, in addition, some researchers have improved the model, such as [PERSON] et al., who put forward a new correction model considering the influence of earth disturbance gravity and the horizontal component of Coriolis acceleration. The validity of the model was verified by the measured data [60].  
The systematic errors of the atom gravimeter under static conditions, including light intensity noise, Raman wavefront distortion, and Zeeman frequency shift, will still have an impact on the measurement under dynamic conditions, but the magnitude is relatively small. So, it is not listed here; for details, see references [31, 61, 62] and so on.
# Stepped Spillways and Energy Dissipation: A Non-Uniform Step Length Approach
## 3 Results and Discussion
### Mesh-Independence Test and Model Validation
#### 3.1.1 Free Surface Profiles  
A standard practice for the VOF approach is to determine the numerical water depth at the position where the volume fraction \(\alpha\) = 0.5 [26]. As shown in Figure 4, for different values of discharge (0.008 \(\leq Q\leq\) 0.250 m\({}^{3}\)/s), the simulated water surface profiles over a broad-crested weir length (\(L_{crest}\)) are very similar to the experimental values.
# Perceptual Similarities between Artificial Reverberation Algorithms and Real Reverberation
## 3 Materials and Methods
### Test Materials and Reference Binauural Room Impulse Responses  
The seven reverberation algorithms defined in Section 2.3 and 2.4.1 were used in the listening test, and each algorithm was binauralized according to the method in Section 2.4.2. The test materials consisted of commonly used male speech, female singing, a solo cello piece, and a drumbeat [52], each rendered with the seven different reverberation algorithms with three different reverberation times (0.266 s, 0.95 s, and 2.34 s). [PERSON] et al. used these same materials as part of their experimental material when studying the perceived level of late reverberation in speech and music [53]. The four audio samples were chosen for the following reasons:  
* Male speech is a common and recognised sound source with a familiar timbre.
* Female singing is a familiar musical source with less energy at low frequencies.
* A solo cello piece is used as a low frequency source.
* A drumbeat is used as an example of more transient sounds.  
These four test materials are all one-channel anechoic audio of 10 s length with 44.1 kHz sample rate and 24 bits bit depth.  
The reference conditions are obtained by the four audio samples rendered with measured BRIRs with the three different reverberation times.  
In each trial, there are nine stimuli: seven stimuli convolved with different BRIRs generated by seven different reverberation algorithms, one hidden reference, and one low anchor (dual-mono 3.5 kHz low pass filtered audio), for a total of 108 stimuli and 12 trials in the whole listening test.  
The BRIR with 0.266 s reverberation time, shown in Figure 14a, was measured from Control Room 7 at WDR Broadcast Studios, Germany. It was taken using a KU100 at 3 m from the source [54]. The BRIR with 0.95 s Reverberation time, shown in Figure 14b, was measured from the Printing House Hall at Trinity College Dublin, Ireland. It was obtained using a KU100 at 2 m from the source [55]. The BRIR with 2.34 s Reverberation time, shown in Figure 14c, was measured from the Lady Chapel at St Alban's Cathedral, United  
Figure 13: Structure of the binaural reverberation algorithmKingdom. It was taken using a KU100 at 4.2 m from the source [56]. A comparison of IACC and D/R energy ratio between the measured BRIRs and BRIRs generated from the reverberation algorithms is presented in Table 10. The IACCs of all BRIRs generated by the reverberation algorithm are matched as closely as possible to the IACCs of the measured BRIRs, and all the D/R energy ratios have been matched exactly.
## 4 LTC Architecture
### PoT Gateway
#### 4.2.4 PoT Gateway and Dialogflow Integration  
With slight tweaks to the original interactional behavior between the PoT gateway and the Dialogflow [29] proposed in [8], the PoT gateway is configured such that once a user picks up the phone, the embedded Asterisk server waits for the user utterance. The embedded Asterisk server records the user's utterance as an audio file and saves it to the PoT gateway. The PoT gateway then changes the recorded audio file's sampling rate from 8 kHz (the sampling rate of phone calls) to 16 kHz (suitable for Google Assistant) and sends the file using the low-level API offered by Google to Dialogflow via the Internet and waits for a reply. The user utterance may contain the name of the called employee or a customer's inquiry to be answered. Upon the reception of the text file from Dialogflow, which includes the phone number at the current location of the target person, the system passes the number to the embedded Asterisk server to originate a call to that number. Asterisk interfacing  
Figure 4: Overview of the LTC solution to serve multi-site companies.  
and audio file manipulation are carried out using Python scripts, which are invoked by Asterisk using Application Gateway Interface (AGI) [30].
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 1 Introduction
### Background and Previous Work
#### 1.1.4 Industry 4.0  
Intelligent advances within the IT domain can benefit a Supply Chain network. Industry 4.0 is driven by IT advances like smart factories using smart technologies [21]. In the field of B2B transactions, the concept of Industry 4.0 plays a vital role in the automatic exchange of information between different business entities. Offering full traceability and transparency between suppliers, manufacturers, and customers is essential for streamlined autonomous services within a Supply Chain. The Supply Chain Industry plays a pivotal role in the transportation and traceability of goods. Adopting Industry 4.0 can have many additional benefits, like a 53% chance of order fulfilment opportunities and a 71% of opportunities in procurement [22]. Other benefits include the flexibility and scalability of Supply Chain operating models that extend upon Supply Chain productivity [23]. Operational Research algorithm's using the Supply Chain Information System via the cloud can address problems relating to the routing of transport and the scheduling of deliveries within cities [24]. A lack of standards around IT security can sway a company from adopting the 4.0 concept. A taxonomy of advantages and disadvantages of Industry 4.0 and the Supply Chain network has been researched [25]. Frameworks have been developed to help companies assess if they are ready to adopt Industry 4.0 supply chains [26].
# Infrared Thermography to Evaluate Thermal Comfort under Controlled Ambient Conditions
## 1 Introduction
### Assessing Thermal Comfort via IRT
[18] examined skin and clothing temperatures from different locations that are normally visible in an office context (e.g., the arms, face, and chest), reaching the conclusion that those measurements are correlated with thermal comfort. The possibility of evaluating thermal comfort through IRT opens space for several innovative applications. [PERSON] et al. [19] evaluated the thermal properties of ten tabletop materials as well as user perceptions of those materials after use. [PERSON] et al. [20] carried out a study to improve the thermal comfort of building occupants by dynamically determining the optimum room conditioning mode and HVAC settings. More recently, [PERSON] and [PERSON] [21] developed a new control strategy for HVAC systems that adjusts the thermostat setpoint according to the clothing level and mean facial skin temperature using an image classification model. IRT images were used by [PERSON] et al. [22] to recognize age and gender, and the data were applied as inputs in a new non-intrusive method of predicting personal thermal comfort. [PERSON] et al. [23] compared three approaches to thermal comfort modelling for older care home residents: PMV, adaptive comfort, and long wave IRT. One must stress that the use of IRT in humans requires the standardization of the protocols and a subsequent data analysis. [PERSON] et al. [24] divided the influential factors of IRT in humans into three main categories: (i) environmental, i.e., those associated with the location where the study is performed (room size, ambient temperature, relative humidity, atmospheric pressure, source radiation); (ii) individual, i.e., those associated with the personal characteristics of the person being targeted (intrinsic factors, extrinsic factors); (iii) technical, i.e., those associated with the equipment used (validity, reliability, protocol, camera features, ROI selection, software, statistical analysis). Regarding the environmental factors, most of the studies were carried out in small rooms, under uniform and controlled conditions. In the studies with a focus on IRT of the face, the air temperatures varied from 18 \({}^{\circ}\)C to 29 \({}^{\circ}\)C [12, 14, 15, 16, 18]. Additionally, the relative humidity rates also showed narrow intervals, varying from 33% to 55% [16, 18].
## 2 Related Work
### Q-Learning Based System Model  
In the UWSNs, the sink nodes are on the surface while the source nodes are deployed underwater. The sink nodes can receive data from multiple distributed source nodes [22]. In our QDAR mechanism, when a source node has a packet to send to the sink node, it broadcasts to the sink node to request communication as well as collect information. This mechanism is explained in the next section. With the collected information, the sink node can lay out a virtual topology from the source node to the sink node and decide the routing path thereafter.  
Each packet in the networks can be seen as an agent in the Q-learning technique. The Q-learning state is related to the node which holds the packet. When a packet is at node \(i\), the state of the agent is \(x_{i}\). Packet forwarding from node \(i\) to node \(j\) is action \(a_{i}\). If the transmission is successful, the packet (agent) state shifts from \(x_{i}\) to \(x_{j}\). The routing path is the policy \(\pi\), directing the packet (agent) to take the proper action. Obviously, it is costly for a node to perform all actions by sending packets to all the neighbor nodes and get all these \(Q\) values. Therefore, in our mechanism, the sink assumes a virtual packet and sends it in the virtual topology. By doing this virtual experiment, the sink node can perform QDAR algorithm and determine the routing path because the sink holds the nodes information, such as the delay and the residual energy.
## 2 Fundamental Concepts and Background
### Types of Explanations  
Techniques can be grouped by scope into those providing global explanations of the entire system and those providing local explanations of single predictions. Global explanations facilitate the understanding of the entire model behavior and reasoning leading to expected outcomes. For local explanations, the reasons for a single prediction are provided to justify why the model made a specific decision for that instance [19]. Techniques can be grouped by whether they are model agnostic (i.e., they can be applied to any ML algorithm), or model specific (i.e., they can be only applied to a specific ML algorithm) [19].