esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Human Ground Control Performance in Unmanned Aerial Systems
## 3 The Ground Control Station Prototype  
### Interface Requirement  
Designing the interaction between an operator and a multi-UAS system means setting the communication mechanisms between the human and each drone in the swarm, understanding their specificities and making the human-system relationship effective, efficient and satisfactory for the operator, in addition to being mission-oriented and safe.  
It is, therefore, necessary to consider the mental processes that are activated in the operator during a remote mission control and to understand which dimensional, configurative, cognitive, and functional features the user interface should guarantee to be consistent with its user's expected behavior.  
Considering the case when the user must manage multiple events, within a limited time lapse, it is paramount that the interface keeps the operator's workload below a given threshold while maintaining a high level of Situational Awareness. The design of the interface was already presented in [7], this paper intends to set out a vision on how to design a human-machine interface for a GCS (Ground Control Station) of a fleet of drones performing a cooperative mission of persistent surveillance.  
The following is a list of requirements which may have a direct impact on the interface design choices:  
* The GCS should allow for the inclusion or removal of a UAS to/from the mission, for efficient management of the whole swarm. In [19; 20], the authors showed that a single operator could manage a maximum of five drones, the operator's workload being the limiting factor as regards the maximum number of UAS to be managed by the GCS.
* The GCS should notify any event which may occur during the mission, such as a low battery charge or a problem with video transmission from a video camera. The interface should provide suitable visual clues as a response to those events. For example, a visual/sound alarm could be activated when the battery reaches a critical level.
* The GCS should provide timely information on the status of each UAS in the swarm. The information on the interface must be carefully selected to avoid the operator's overload and draw his/her attention on relevant events. The standard GCS can be used for a maximum number of five drones, and it is recommended to have a list of drones in a control panel from which the operator can view the parameters associated with specific drones.
* A certain level of autonomy should be implemented in the system so that the operator can rely on (semi-)automatic procedures meant to take control over an aircraft featuring an off-nominal behavior and terminate its participation in the mission as soon as possible. Establishing the right balance between decision-making autonomy and human intervention in re-planning or homing actions may have serious effects on mission success.
# MBHAN: Motif-Based Heterogeneous Graph Attention Network
## 4 The Proposed Method
### Node Feature Mapping Mechanism
```
1:Input: A heterogeneous graph: \(\mathcal{G}=(\mathcal{V},\,\mathcal{E})\);
2:Node types: \(\mathcal{T}\);
3:A random walk + Skip-Gram Parameters set: \(\mathcal{c}\);
4:The inherent semantic features contained in some types of nodes: \(h^{sem}\);
5:The collection of motif subgraphs with node-type connectivity patterns based on the heterogeneous graph, \(\mathcal{G}\): \((\mathcal{G}_{\mathbf{m}_{0}},\mathcal{G}_{\mathbf{m}_{1}},\dots,\mathcal{G}_{\mathbf{m}_ {k}})\);
6:The number of attention heads: \(K\);
7:Output: Node representation learning vectors: \(\mathcal{Z}\);
8:Generate node \"structure\" features using the random walk strategy;
9:\(h^{str}\leftarrow\)Random Walk, (\(\mathbf{c}\));
10:for every node type, \(\mathbf{t}\), in \(\mathcal{T}\) do:
11:if\(\mathbf{v}_{i}^{\mathbf{t}}\) does not have semantic features \(h^{sem}\) do;
12:\(h_{i}^{sem}=\mathbf{M}_{t}\cdot h^{str}\);
13:Integration of all node features \(\
eg h\);
14:for every motif sub-graph, \(\mathcal{G}_{\mathbf{m}}\), in \((\mathcal{G}_{\mathbf{m}_{0}},\mathcal{G}_{\mathbf{m}_{1}},\dots,\mathcal{G}_{\mathbf{m}_ {k}})\);
15:for\(\mathbf{k}=1\dots k\) do;
16:for\(\mathbf{v}_{i}\) in \(\mathcal{G}_{\mathbf{m}_{0}}\) do;
17: find, for\(\mathbf{v}_{i}\) the neighboring nodes set, \(\mathcal{N}_{i}^{m}\);
18:for\(\mathbf{j}\in\mathcal{N}_{i}^{m}\) do;
19: Calculate the weight coefficient \(\mathbf{\alpha}_{ij}^{m}\);
20: Calculate the motif subgraph-specific node embedding;
21:\(z_{i}^{m}\leftarrow\sigma\left(\sum_{j\in\mathcal{N}_{i}^{m}}\mathbf{\alpha}_{ij} ^{m}h_{j}\right)\) ;
22: Calculating embeddings learned from all attention mechanisms;
23:\(z_{i}^{m}\leftarrow\)\(\frac{K}{K_{\mathbf{k}}}\)\(1\,\sigma\left(\sum_{j\in\mathcal{N}_{i}^{m}}\mathbf{\alpha}_{ij}^{m}\cdot h_{j}\right)\);
24: Calculate the weight of the motif subgraphs, \(\beta_{\mathbf{m}_{i}}\);
25: Calculate the \(\mathbf{t}\)-type node embedding \(\mathcal{Z}^{t}\leftarrow\)\(\int_{i=1}^{M}1\,\beta_{\mathbf{m}_{i}^{t}}\mathcal{Z}_{\mathbf{m}_{i}}^{t}\);
26:Fuse all types of node embedding, \(\mathcal{Z}\);
27: Calculate the downstream task-specific loss functions;
28:Back-propagate and update the parameters in MBHAN;
29:return\(\mathcal{Z}\). ```  
**Algorithm 1** The Overall MBHAN Process
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 6 Computational Complexity
### Proposed Method  
For the proposed method, it can be known that for a traditional CNN, about 90% of the parameters of the total network are in the FC layers [47]. Hence, the computational complexity can be decided by the FC layers while ignoring the influence of the convolutional layers. It can be understood that the input feature is directly given to the first FC layer. Thus, the first part of the computational complexity is decided by the size of the input feature and the number of the nodes in the first FC layer. Similarly, the second part is decided by the output of the first FC layer and the number of the nodes in the second FC layer. Thus, the complexity of the proposed method is \(O(N_{H}N_{I}+N_{H}N_{C})\), and \(N_{I}\) indicates the dimension of the input feature. Moreover, the proposed method has three different input features. From Table 1, the dimension can be obtained. Thus, the complexities are \(O(N_{H}(2M^{2})+N_{H}N_{C})\) for TFD, \(O(N_{H}(2(N+1)^{4})+N_{H}N_{C})\) for SHD and \(O(N_{H}(2(2N+1)^{4})+N_{H}N_{C})\) for AED. From the medical analysis, it can be seen that the proposed method has lower computational complexity than the DPD-MUSIC.
# Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging
## 2 Materials and Methods  
### Sample Collection and Analysis  
For scanning the unpeeled potato samples, a Specim IQ hyperspectral VS_NIR camera (Specim, Spectral Imaging Ltd., Oulu, Finland) was used with two 750 W tungsten halogen lamps (ARRILITE 750 Plus, ARRI, Munchen, Germany) and a white reference tile. The Specim IQ hyperspectral VS-NIR camera was positioned with two symmetrically placed halogen lamps, and the white reference tile was properly positioned with the camera, as shown in Figure 1. Reflectance geometry is employed by the camera, which works between the range of 400 and 1000 nm, with a spectral resolution of \(-\)7 nm, thereby providing 204 wavebands. Accordingly, each hyperspectral image is a volumetric image cube that is a three-dimensional matrix storing spatial as well as spectral information about the sample.  
A total of 47 samples of commercial different variety potatoes were collected from the local supermarkets around Central Queensland, Australia. The whole unpeeled potatoes in dry basis were used in the study. Before conducting the moisture analysis in the unpeeled potatoes, they were kept in a lab oven at 60 \({}^{\circ}\)C for two days. The ground truth moisture content was obtained by measuring the difference in potato weights before keeping the potatoes inside the oven, and, after, taking the potatoes that were left inside the oven at 60 \({}^{\circ}\)C for two days for drying. By presuming that moisture alone has been driven off after drying, the gravimetric microwave drying method estimates moisture indirectly [37]. The pretreatment was carried out to contain the moisture content in the potatoes. The moisture content of unpeeled potatoes varied between 50 and 80%, as shown in Figure 2.  
Figure 1: Hyperspectral imaging system in the laboratory.
## 2 Materials and Methods
### Fabrication of MWNT/NR Single Composite and MWNT/NR Bi-Layer Composite  
Both the MWNT/NR single composite and the MWNT/NR bi-layer composites with low contents (1 wt.%) and high contents (5 wt.%) were fabricated using the ultrasonication method. Because the dispersion of MWNT is essential in manufacturing the composite, a solution process using an ultrasonicator (Sonics & Materials Inc., Newtown, CT, USA, VC 505) was performed.  
First, 5 g of NR was dissolved in 250 mL of chloroform using a magnetic stirrer (IKA, C- MAG HS7) at 45 \({}^{\circ}\)C, 500 rpm for 24 h. The MWNTs were then mixed with 150 mL of chloroform in another beaker for uniform dispersion, and the first ultrasonic dispersion was performed at 200 W for 30 min. Next, a secondary ultrasonic dispersion was performed for 1 h by adding an NR solution to the CNT dispersion, which was then sufficiently mixed. Then, 2 wt. % DCP was added to the resultant dispersion and mixed for 30 min. Finally, the solvent was evaporated in a vacuum oven at 45 \({}^{\circ}\)C for 16 h. Because NR is a thermosetting polymer, the obtained MWNT/NR film was pre-pressed at 3 MPa for 2 min at 170 \({}^{\circ}\)C in a 0.5 mm mold, and then pressed at 15 MPa for 18 min. This process fabricated 0.5 mm of the MWNT/NR single composite.  
The NR film, previously prepared to fabricate the MWNT/NR bilayer composite, was placed at the upper and lower ends of the single composite and heat-pressed in the same manner as mentioned above. In the case of the bilayer composite, a 1.5 mm mold was used to design all layers with thicknesses of 0.5 mm. This process is illustrated in Figure 1.
## 3 Identification of Boundary Conditions  
### Fishing Vessels  
The analysis considered fishing vessels that are representative of the fishing fleet in different countries and climates. Both the case of oceanic navigation, i.e., long distance shipping with several days at sea, and the Mediterranean case, with shorter travel distance and time at sea, were evaluated. Based on the analysis reported in [25] for fishing vessels that included a refrigeration system on board, three cases were selected, mainly differing in the dimensions and prime movers. In addition, according to what was reported in [20], a fourth case was considered, typical of the Mediterranean fleet, a vessel with 10 m\({}^{3}\) frozen fish storage, with 10 days of operation. The amount of fish that each vessel could hold was calculated using the cubic number (\(C\!U\!N\!O\)) method [26]: the fish hold is equal to \(C\!U\!N\!O\times\!0.14\), where the \(C\!U\!N\!O\) is:  
\[C\!U\!N\!O=Loa\times B\times Dm, \tag{1}\]  
where \(Loa\) is the length overall (m), \(B\) is the beam width amidships at deck level (m), and \(Dm\) is the distance from deck to keel amidships (m).  
The cases investigated are summarised in Table 1. It is clear that the wide variety of the different fishing vessels guarantees the representativeness of the analysis carried out. Nonetheless, the peculiarities of the fishing vessels in different world areas were considered. For instance, as reported in [27], the Italian fleet, which can be considered representative of the overall Mediterranean fleet, is mainly characterised by vessels with a displacement lower than 400 m\({}^{3}\) and therefore, the case D is the most representative one, followed by case A. On the contrary, the fishing fleet in the North Sea, comprising several countries (Norway, Belgium, The Netherlands, UK, Denmark) is extremely variable and includes both fishing vessels below 16 m (cases A and D) and bigger fishing trawlers that stay at sea between 20 and 25 days, since the wide availability of different fish species allows for different landings [28]. Regarding the fisheries specifications in the ocean, it is possible to assume that  
Figure 1: The methodology followed in the analysis.  
close to the coasts, smaller vessels are used, whereas in deep sea big fishing trawlers with refrigeration facilities can be found, as confirmed in [29], where it was shown that there is not a prevalence of the so-called artisanal fishing boats over the industrial trawlers. The variability considered in the analysis is therefore wider than that usually considered in the scientific literature on the topic [20] and it is meant to give a complete overview regarding the technical and environmental benefits connected to the application of sorption systems, also under conditions not investigated up to now.
# The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System
## 3 System Testing
### Spurious and Higher-Harmonic Testing  
When conducting spurious and higher-harmonic testing, a standard cosine signal generated by a signal source is used as the input for the system, and the electrical signal outputted by the system is connected to a spectrum analyzer for analysis. A schematic diagram and the actual setup of the system for testing are depicted in Figure 12.  
Starting with a representative 10 GHz signal, the spurious conditions of the system were tested. The output frequency of the signal source was set to 10 GHz with a power output of \(-\)10 dBm. The resolution bandwidth (RBW) and video bandwidth (VBW) of the spectrum analyzer were set to 10 Hz and 300 Hz, respectively. The spurious conditions around the center frequency were observed, and the results are shown in Figure 13. From  
Figure 11: Parameters of the system-cascaded amplifier.  
Figure 12: Schematic and test setup of the spurious and higher-harmonic test.  
the results, it can be gleaned that there was almost no spurious interference from other frequencies near the center frequency. Subsequently, using a 4 GHz signal as a representative, the higher-harmonic conditions of the system were tested. The output frequency of the signal source was set to 4 GHz with a power output of \(-10\) dBm. The frequency sweep range (span) of the spectrum analyzer was set from 1 to 20 GHz to observe the higher-harmonic conditions. The results are shown in Figure 14.  
The figure shows that the power of the first harmonic in the output signal is 8.98 dBm, while the power of the second harmonic is only \(-26.66\) dBm, which is 35.64 dB lower than the first harmonic, consistent with the conclusion in Section 2. Additionally, it can be noted that there are third-order harmonics present in the system output; these were introduced by the amplifier cascaded after the photodetector. The power of the third harmonic is 46.06 dB lower than that of the first harmonic, almost negligible, and does not affect the normal operation of the system. The test results for higher harmonics at other frequencies are summarized in Table 2.  
Figure 14: Higher harmonics at a 4 GHz input (diamond shapes represent markers).  
Figure 13: Spurious test results (diamond shape represents marker).  
The test results indicate that the signal transmission system does not necessarily require a filtering device. However, if conditions allow for the introduction of a filtering device, such a device can reduce the interference from the existing spurious and higher-order harmonics.
## 3 Probabilistic Forecasting of ALSC
### The Basic Theories of Lhs  
The sampling method is crucial for Monte Carlo simulation (MCS). Traditional random sampling (\(RS\)) requires numerous samples to characterize random variables accurately but reduces computational efficiency. Evidently, a smaller sample size is expected to decrease calculation time while maintaining accuracy. Latin hypercube sampling (\(LHS\)), which is a typical stratified sampling method, can cover more space of random variables than \(RS\) with the same sample size.  
With the same sample size \(N\), \(RS\) and \(LHS\) achieve different joint coverage of two independent random variables. The expected values of joint coverage space for \(RS\)\(E_{RS}\) and \(LHS\)\(E_{LHS}\) are shown as (31) and (32), respectively [36].  
\[E_{RS}=\left(\frac{N-1}{N+1}\right)^{2}\times 100\% \tag{31}\]  
\[E_{LHS}=\left(\frac{N-1}{N}\right)^{2}\times 100\% \tag{32}\]  
When \(N\geq 2\), \(E_{LHS}\) is always larger than \(E_{RS}\). Therefore, LHS always covers more space of random input variables than RS with the same sample size.
## 2 Positioning in Cellular Networks
### Fingerprinting-Based Localization Techniques  
The most-utilized fingerprinting-based positioning method consists of two phases: an offline (training) phase and an online (positioning) phase. In the offline or training phase, a database is created by measuring given signal or antenna attributes at known locations (i.e., known as signatures or fingerprints). Such a signal attribute can be the received signal strength, which can be measured by the UE. In addition to these attributes, the floor number (if indoors), the orientation of the device, the TA (Timing Advance) or RTT (Round-Trip Time) value (or any kind of timing information), the type of mobile unit, etc., may also be stored. This database of locations and the associated fingerprints are called the radio map or fingerprint database. This database enables the creation of one or more models through Machine Learning (ML) techniques. In the online phase, localization is accomplished by using the model to estimate the user's position based on the attributes measured by the mobile unit.  
The most significant advantage of fingerprinting-based solutions is their ability to remain accurate even in highly cluttered multipath environments [18; 30]. It provides an edge over the angle-based and range-based solutions, but it comes with a high cost, too, since the offline phase's fingerprint collection requires extensive human resources. The Achilles heel of the fingerprinting-based solutions is the alteration of the setup. For example, when the number or the locations of the access points in the environment changes, the radio map, i.e., the fingerprint, must be updated accordingly. Although a simple removal of an access point can be modeled with the deletion of the corresponding values from the fingerprints, the installation of one or more new access points forces a new fingerprint collection. Additionally, the different types of mobile devices contain various types of antennas, resulting in an additional error component in the measurements. These problems negatively affect the accuracy of positioning.  
Figure 4: Range difference measurement based positioning with at least three BS.  
Figure 3: Range measurement based positioning with at least three BS.
## 3 Network Background
### Handover Management in Ultra-Dense 5G/6G Cellular Networks  
HO management is a crucial process in wireless mobile networks, ensuring that a user's connection remains uninterrupted while moving between different coverage areas. This process involves dynamically transferring the connection from one BS to another that provides a stronger and more stable signal. Effective handover management is essential for maintaining mobility, reducing RLF, minimizing interruption times, and ultimately enhancing network reliability and user experience. By optimizing HO strategies, wireless networks can significantly improve throughput, decrease connection disruptions, and support seamless connectivity for users in motion [49; 50]. Handover management in ultra-dense 5G and 6G cellular networks refers to the process of maintaining seamless connectivity for mobile users as they move between overlapping small cells in densely deployed network environments. This process ensures continuous service delivery by efficiently transferring active connections from one base station to another, thereby minimizing interruptions and maintaining high-quality communication [6]. In UDN scenarios, where many small cells are deployed to meet the increasing demand for high data rates and low latency, handover management becomes particularly challenging. The proximity of numerous base stations can lead to frequent and unnecessary handovers, increased signaling overhead, and the potential degradation of user experience [8]. Effective handover management strategies in such environments involve optimizing handover parameters, employing predictive algorithms, and using ML techniques to anticipate user movement and network conditions. These approaches aim to reduce handover failures, minimize latency, and enhance overall network performance, thereby ensuring reliable and efficient connectivity in ultra-dense 5G and 6G networks [51].
## 2 Background
### Relationship Extraction  
A relationship can be extracted between any combination of named entities. An RE task is basically a classification of the semantic relationship between entities from textual data. RE between entities in any text is a vital task that facilitates its automated natural language understanding. The abundance and heterogeneity of unstructured data in any domain are hard to be fathomed by humans alone. Hence, the conversion of unstructured text into structured data by annotating its semantics needs to be automated. RE tasks are thus very useful in automating the process of identification of different relations from clinical data. Some important applications of clinical RE include gene-disease, drug-effect, disease-mutation, and disease-symptom relationships. In general, the pair-wise association between entities is considered, but in many cases, more than two entities are also involved. The process of checking whether a relationship exists between entities is a classification problem that can also be extended to multi-class classification or multi-label classification. In [20], a relation is defined as a tuple \(t=(e_{1},e_{2},e_{3},\ldots,e_{n})\), where \(e_{i}\) are the entities with a predefined relationship \(r\) within the document \(D\). Similarly, all of the different relationships in a document can be defined.  
Similar to NER, RE has been applied to many domains, including the healthcare domain. One of the oldest works on RE was published in 1999, which extracted informative patterns and relations from the World Wide Web [21]. In the following year, relationship extraction from the large plain text was conducted, where a system named Snowball introduced novel strategies for pattern generation [22]. Kernel-based methods such as dependency tree kernel-based technique [23], shortest path dependency kernel-based technique [24], and subsequence kernel-based techniques [25] were proposed. The integration of probabilistic models and data mining were also proven to be good techniques for extracting relations and patterns from text [26]. Although there are enumerable RE methods in place, the models and algorithms are very domain- and data-specific. The absence of generalized algorithms to perform RE makes it challenging to define and perform a new RE task; the state-of-the-art models vary between different datasets and from one domain to another. In general, RE is most commonly viewed as a supervised learning technique performing classification [27]. In such cases, a machine learning (ML) algorithm, either traditional ML or deep learning-based methods, is used. RE can also be achieved by using unsupervised learning and rule-based methods. In the following sections, we discuss the various RE tasks and techniques applied to the clinical and biomedical domains.