esa-sceva/satcom-chunk-collection huggingface dataset:
To address the shortcomings of the original MKR work, we propose MDAR, a multi-task learning method for DeepFM with an attention mechanism (DeepAFM) and a relation-fused multi-head graph attention network (RMGAT). There are three novelties when compared with the original MKR. Firstly, in the RS modules, MDAR utilizes the Deep-AFM to capture both low-level and high-level features of users and items. The attention mechanism in DeepAFM assigns higher weights to features that are more relevant to the target, helping the recommendation system accurately identify users' historical behavior preferences by extracting important low-order linear feature interactions. Secondly, in the KGE modules, MDAR applies the multi-head graph attention network with relation fusion to capture important neighborhood features. By considering the significance of different relationships, MDAR can effectively extract deep multi-relation neighborhood features in the knowledge graph, enhancing the recommendation process by completing the knowledge graph information. Thirdly, we introduce deep neural networks (DNNs) into the cross-compression unit to extract the deep semantic information between the item in the RS module and the entity in the KGE module to solve the problem of missing original data. With the above improvements, each part of MDAR has been redesigned compared with the original MKR, and the novelties and contributions can be summarized as follows:  
* We develop a new framework, MDAR, a multi-task learning approach for the DeepFM with an attention mechanism and a multi-head graph attention network with relation fusion-enhanced recommendation. MDAR has three improvements over the original MKR;
* We construct a DeepAFM in the RS module, which mainly assigns different assigned weights to different cross features through the attention mechanism to mine the user's historical behavior preferences more accurately and to make up the feature contribution consistency problem in the original recommender system;
* We design an RMGAT in the KGE module, where a graph attention network with relation fusion is based on multi-head attention mechanisms, to learn the representation of different relationships for different entities to adequately obtain the entities' aggregate features in KG, which solves the problem of information complementation in the original knowledge graph;
* We introduce a deep neural network (DNN) into the cross-compression unit to extract high-dimensional features from the items in the recommendation system (RS) module and the entities in the knowledge graph embedding (KGE) module. This enables us to share information between the modules and enhance the overall generalization ability of our MDAR and solve the problem of data loss in the data compression process in the original MKR. * We conduct experiments on four public datasets, and the results demonstrate that MDAR outperforms state-of-the-art baselines in terms of click-through rate (CTR) prediction and top-K recommendation. Furthermore, MDAR maintains comparable performance even in scenarios with sparse data.
# Energy Recovery via Thermal Gasification from Waste Insulation Electrical Cables (WIEC)
## 2 Materials and Methods  
Generally, cables consist of a metallic interior (copper or aluminum), a semiconductor layer, and an insulating coating composed of polyvinyl chloride (PVC) or polyethylene (PE) thermoplastic material. Aiming at harnessing the noble metals inside the wires, the wiring is recycled, and the material left over from this recycling is a mixture of PVC and PE and noble metals in small quantities. Polymeric residues may be a suitable co-reagent, together with lignocellulosic biomass for thermochemical processes, namely, co-pyrolysis and co-gasification, since the vast majority of polymeric residues, such as polyethylene (PE), polystyrene (PS), polypropylene (PP), and polyethylene glycol terephthalate (PET) are rich in hydrogen and poor in oxygen. Pending an energy recovery solution, polymeric waste is disposed of in landfills, causing serious environmental problems. For this reason, co-gasification of lignocellulosic biomass and polymeric residues can be a promising method to alleviate environmental pollution and provide renewable energy.  
For the tests, pine forest biomass (PFB) and waste insulation for electrical cables (WIEC) were used in different blending ratios PFB/WIEC. The gasification experiments were carried out in a gasifier comprised of a downdraft reactor, a 3.0 L 4-cylinder internal combustion engine coupled with a 20 kW electric power generator, and an electronic control unit. Several controlled gasification tests were carried out, that is, without the engine and the generator running, to identify the effect of the blending ratio. Four tests were carried out with pine forest biomass and cables in different blending ratios, i.e., 100:0, 90:10, 80:20, and 70:30.  
Three co-gasification tests were carried out with loads of 5, 10, and 15 kW, with the gasifier generator assuming the effect of temperature, equivalence ratio, synthesis gas quality, and amounts of chars and tar production for the feedstock blending ratio of 80:20.
# Experimental Study on Chromaticity Control in Visible Light Communication Systems
## 3 Research on RGB LEDs Dimming
### Discussion  
The visible light communication system using RGB LEDs as the light source in this article can conveniently use color division multiplexing technology to improve the transmission performance of the entire system. However, the research on RGB multi-color visible light communication systems was conducted in the laboratory and has not been used in daily life. Moreover, the three-channel system designed in this article has not reached the limit of the number of visible light band channels. In the future, the transmission antenna can be reasonably designed to increase the communication bandwidth while mixing multi-color LEDs with white light, making the information transmission distance longer and achieving lighting standards in indoor applications, thus achieving the goal of universal use in daily life.
The overview includes the operational and privacy challenges of the distributed load due to its complex nature and the existing state of its integration in different markets via DR aggregators. Second, a critical analysis of the existing DR aggregator and prosumer framework literature in terms of five important gaps is carried out: flexibility, competition, uncertainty, privacy, and scalability (FCUPS framework). These evaluation criteria support the safe and large-scale integration of distributed loads into future US electricity markets and facilitate the 2030 and 2050 goals. Third, future work directions are provided for researchers to develop FCUPS-compatible frameworks that allow for the safe and large-scale integration of distributed loads via DR aggregators in future US electricity markets. The rest of this paper is organized according to Figure 1. Section 2 analyzes the potential for energy savings and grid reliability improvement from distributed loads in the US and provides an overview of energy-efficient technologies, with a focus on DR aggregators. In Section 3, available frameworks for the DR aggregator's operation are investigated in terms of flexibility, reliability, uncertainty, scalability, and the consideration of prosumer's privacy, and missing framework capabilities are identified. This section summarizes open research questions for researchers and market policymakers that will transform the US electricity markets and help achieve the 2030 and 2050 goals. Transformation of Future US Electricity Markets by Distributed Load and Demand-Response Aggregators
# Comparative Tissue Responses of Marine Mollusks on Seasonal Changes in the Northern Adriatic Sea
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Green Ormer**} & \multicolumn{3}{c}{**Mediterranean Scallop**} \\ \cline{2-7}  & **Fall** & **Winter** & **Spring/Summer** & **Fall** & **Winter** & **Spring/Summer** \\ \hline Mg (mg/g) & 13.89 \(\pm\) 15.82 & 3.63 \(\pm\) 0.68 & 4.35 \(\pm\) 1.15 & 18.00 \(\pm\) 23.15 & 18.42 \(\pm\) 13.98 & 13.58 \(\pm\) 21.03 \\ Ca (mg/g) & 2.44 \(\pm\) 2.84 & 0.61 \(\pm\) 0.14 & 0.76 \(\pm\) 0.19 & 2.93 \(\pm\) 3.54 & 3.14 \(\pm\) 3.06 & 2.39 \(\pm\) 3.11 \\ GLU (mg/g) & 0.62 \(\pm\) 0.48 & 0.26 \(\pm\) 0.25 & 0.50 \(\pm\) 0.33 & 1.00 \(\pm\) 1.06 & 0.80 \(\pm\) 0.65 & 0.64 \(\pm\) 0.77 \\ TRIG (mg/g) & 48.45 \(\pm\) 75.49 & 14.62 \(\pm\) 5.20 & 11.34 \(\pm\) 3.49 & 43.43 \(\pm\) 67.83 & 31.80 \(\pm\) 32.98 & 22.54 \(\pm\) 32.33 \\ CHOL (mg/g) & 44.94 \(\pm\) 0.95 & 27.44 \(\pm\) 9.35 & 18.13 \(\pm\) 6.54 & 31.78 \(\pm\) 34.06 & 43.07 \(\pm\) 37.41 & 19.30 \(\pm\) 24.24 \\ SOD (U/g) & 135.33 \(\pm\) 159.47 & 121.38 \(\pm\) 108.55 & 88.31 \(\pm\) 51.35 & 300.01 \(\pm\) 236.93 & 208.60 \(\pm\) 240.49 & 542.20 \(\pm\) 520.96 \\ GPx (U/g) & 49.01 \(\pm\) 17.68 & 48.38 \(\pm\) 11.50 & 41.16 \(\pm\) 5.80 & 60.69 \(\pm\) 16.05 & 53.33 \(\pm\) 10.57 & 93.10 \(\pm\) 52.38 \\ TAS (mmol/g) & 0.29 \(\pm\) 0.53 & 0.08 \(\pm\) 0.01 & 0.76 \(\pm\) 0.52 & 1.07 \(\pm\) 1.39 & 2.36 \(\pm\) 2.70 & 0.52 \(\pm\) 1.56 \\ TBARS (ng/g) & 4.22 \(\pm\) 3.79 & 1.05 \(\pm\) 0.15 & 2.69 \(\pm\) 1.75 & 5.56 \(\pm\) 6.67 & 4.99 \(\pm\) 3.84 & 5.33 \(\pm\) 8.66 \\ \hline \end{tabular}
\end{table} 
Table 3: Seasonal tissue biochemical and antioxidant defense properties for green ormer (_Haliotiis tuberculata_ L) and Mediterranean scallop (_Pecten jacobeus_ L). Figure 1: Differences in tissue biochemical and antioxidant defense properties between _Pectent jacobeus_ and _Haliotis tuberculata_ within each season (**a**) spring/summer (**b**) fall (**c**) winter. The symbols * indicates a significant difference: * \(p\) < 0.05, **\(p\) < 0.01, ***\(p\) < 0.001. Figure 3: Photomicrographs of sections of the _Haliotiis tuberculata_ gonads and digestive gland. Seasonal variations of the ovarian developmental stage: (**A**) early active stage (spring/summer); (**C**) partial spawn stage (fall); (**E**) late active stage (winter); previtellogenic oocyte (arrow); vitellogenic oocyte (dashed arrow); mature oocyte (arrowhead); male gonads with spermatozoa (star). Changes in tubuloacinar terminations of the digestive gland: (**B**) spring/summer; (**D**) fall; (**F**) winter; lipid droplet (arrow). Figure 2: Photomicrographs of the _Pecten jacobeus_ gonads and digestive gland. Seasonal variations of the ovarian developmental stage: (**A**) late active stage (spring/summer); (**C**) early active stage (fall); (**E**) partial spawn stage (winter); previtellogenic oocyte (arrow); vitellogenic oocyte (dashed arrow); mature oocyte (arrowhead); male gonads with spermatozoa (star). Changes in tubuloacinar terminations of the digestive gland: (**B**) spring/summer; (**D**) fall; (**F**) winter; lipid droplet (arrow).
## 2 System Model
### User Distribution Model within the Beam Coverage Area  
Due to the high orbital position, the LEO satellite has a beam coverage area that is much larger than that of ground-based stations. The characteristics of the distribution of ground user terminals are different in different regions. When the subsatellite point changes, the distribution characteristics of the ground terminals in the corresponding coverage area also change. Also, the number of users in each sub-beam is also constantly changing.  
Therefore, the actual number of users within a single sub-beam cell is jointly determined by the location of the user terminal, the time slot division, and the satellite motion state, as shown in Figure 3. Meanwhile, the sub-beam coverage areas partially overlap with each other, and the short message transmission duration is in the millisecond range. When dividing the area of users belonging to the overlapping area of sub-beam coverage, it can be divided according to whether their transmission message time will exceed the current beam coverage area. Furthermore, the total number of users in the beam cell within a period of time can be determined through OTDMA. The number is certain and can provide a basis for frequency allocation.  
The LEO satellite moves much faster than the ground mobile sensor terminal, so the low-orbit satellite can be considered stationary. Both fixed and moving terminals move relative to the satellite. In the case of multiple short messages sent by a single ground terminal, it can be considered as multiple users with the same initial position sending a single short message in different time slots.  
So, we establish the following two models for the distribution of ground users. And three different radius are defined. \(R_{0}\) is the sub-beam coverage radius and \(R_{i}\) is the central area radius of frequency band resource allocation, as shown in Figure 2. \(r_{i}\) is the central area of user distribution, as shown in Figure 4.  
* Scenario 1: Sensor terminals located in remote areas or on the distant seas can be approximately equivalent to users evenly distributed in the same beam, with different numbers of users in different beams, \(\rho_{i}=\rho_{i}^{\prime}\), as shown in Figure 4I.  
Figure 3: User division in sub-beam cell.  
* Scenario 2: Users are randomly and densely distributed within the beam. For example, a large number of ground sensor terminals are deployed in densely populated areas such as cities and ports [34]. The distribution of user terminals is divided equally into users concentrated inside or outside a region with a radius of \(r_{i}\), \(p_{i}>q_{i}\) or \(p_{i}<q_{i}\), \(i=[1,\,n]\) and \(0<r_{i}<R_{0}\), as shown in Figure 4(II-a,II-b).  
The detailed notations and definitions are summarized in Table 1.
## 4 Simulation Results of Fully Mechanized Mining with a Large Mining Height and the Analysis
### Mechanism of Spalling on Coal Walls of Working Face  
The caving of the roof during the advancement of the 8 m fully mechanized mining face in Huoxi Coalfield conforms to the \"O-X\" breaking law. Since the main roof of Huoxi Coalfield is a thick hard limestone roof with a long breaking step, it breaks in the goat area after the ultimate bending strength is reached. The migration of overburden is characterized by the changes in the \"cantilever-instability-hinge\". Figure 12 shows the roof load and the constraints when roof-coal seam-hydraulic support is balanced.  
Figure 12: The force endured by the coal seam under different distributions of roof pressure.  
In the early stage of excavation, due to the stable strength of the thick hard roof and the hydraulic support, the mining site and the original rock structure form a fixed support on two sides to obtain the balance, and the front end of coal seam is not damaged since the peak roof pressure is behind the support; in fully mechanized mining with a large mining height, as the height of the caving zone increases, the range of activity of overburden formation is larger, which results in greater roof pressure and a forward shift in the peak stress of roof. Because the thick hard limestone roof in Huoxi Coalfield can still bear the pressure with the hydraulic support under roof pressure, a cantilever is formed during excavation. When the excavation length of working face reaches the critical value, the mine pressure is obvious, and cracks develop under the roof pressure. The roof is damaged to form a masonry beam structure, and the coal body at the front end is damaged.  
After analysis of the principle of minimum potential energy of coal seam, it is concluded that when the coal wall is not constrained, the failure zone is the damage by tensile shear and splitting. If the cracks continue to develop, they will fall in the direction of the slop face, resulting in spalling. Due to the pressure of roof caving in mining with full mining height, a long rock cantilever forms on the roof, which easily causes deep and large spalling.
## 2 Materials and Methods
### Multispectral Image Analysis
#### 2.4.1 NDVI (Normalized Difference Vegetation Index)  
NDVI can differentiate green leaves with high reflectivity of green wavelengths from withered or dead plant leaves [29]. This vegetation index is widely used in agricultural and environmental applications, such as plant growth and crop cultivation. NDVI is primarily used to monitor plant growth and can be modified by combining bands of the visible (RED) and near-infrared (NIR) regions in multispectral imaging. The chlorophyll in plant leaves is observed to characterize the vegetation. The NDVI formula is shown in Equation (1).  
\[NDVI=\frac{NIR-Red}{NIR+Red} \tag{1}\]  
NDVI, which takes a value between \(-1\) and \(1\), is used to quantify plants' photosynthetic ability. Typically, a value between \(-1\) and \(0\) indicates that plant regeneration is not possible or that there is no vegetation. Vegetation is determined to be alive and present only if the NDVI value is greater than zero.
# A Review on SIW and Its Applications to Microwave Components
## 14 Future Challenges of SIW  
Current research in SIW technology is mostly in the microwave bands (that is, 300 MHz to 30 GHz). Although a few researchers have reported SIW devices that operate in the millimetre-wave bands, up to 60 GHz [90], research beyond 60 GHz in this field is still a huge challenge. Hence, future research in substrate-integrated waveguide devices will have to focus on the deployment of millimetre-wave devices within the frequency range of 60 to 300 GHz. Even when such devices are successfully designed/implemented, fabricating them in the workshop will also pose another huge challenges due to their enormous compact size. The authors of this paper believe that the best way to overcome these future challenges, and deploy SIW millimetre-wave devices of up to 300 GHz operating frequency, would be by collaborative research involving researchers from various fields. The system-in-package research concept may just be where the breakthrough may come from, sooner rather than later.
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 2 FD-LTDA-MAC Protocol
\begin{cases}\tau_{rp}+2\tau_{g},&n<N_{sn} \\ \tau_{g},&n=N_{sn}\end{cases} , \tag{7}\]  
and  
\[ \begin{split}\forall n,k\in\{1..N_{sn}\},k>n,\\ T_{m}[n,k]=2(\tau_{p}[n+1]+\tau_{g})+\tau_{rp}+\tau_{dp}+T_{tx}[n+1,k]. \end{split}  \tag{8}\]  
The FD-LTDA-MAC protocol is described in Algorithm 1. The network instance is firstly created with appropriate \(\tau_{g}\) and time step, \(\tau_{step}\). Then, the initial collision-free schedule is calculated using a large value of transmit delay, \(T_{large}\). The algorithm then checks for full-duplex transmissions by looking for overlap in time of transmit times, \(\tau_{tx}\), and interference time, \(\tau_{I}\), among the nodes transmitting their own data packets. It then schedules full-duplex transmission for nodes transmitting own packet(s) using (5). The above process is repeated for relay transmissions, but (6) is used for forwarding the data packets by relay nodes. The features of the FD-LTDA MAC protocol are highlighted as follows:  
* It uses linear constraints to calculate transmit delays for forwarding packets to reflect full-duplex capabilities;
* To allow the greedy scheduling algorithm utilise the full-duplex based initial starting point that excludes data transmission time and the corresponding propagation delay components from the transmit delay time, as allowed by full-duplex communication;
* Include full-duplex support in the algorithm to evaluate schedules that are derived for full-duplex transmissions. ```
1:Create \(\mathcal{N}\) using initial network discovery
2:Set the desired guard interval and time step \(\tau_{g}\) and time step \(\tau_{step}\)
3:Initialise collision-free schedule using: \(\forall n,k\in\{1..N_{sn}\}\), \(k\geq\)\(n\), \(T_{tx}[n,k]=(N_{sn}n+k)T_{large}\)
4:for\(i\in\{1..N_{sn}\}\)do
5:for\(n\in{1..(N_{sn}-i+1)}\)do
6: Calculate the packet index \(k=n+i-1\)
7: Calculate \(\tau_{tx}\) and \(\tau_{I}\)
8:if\(\tau_{tx}\leq\tau_{I}\)then
9: Calculate \(T_{m}^{FD}[n,k]\) using (5) if \(n=k\), or (6) if \(n\
eq\)\(k\)
10: Initialise Tx delay: \(T_{tx}^{FD}[n,k]=T_{m}^{FD}[n,k]\)
11:else
12: Calculate \(T_{m}[n,k]\) using (7) if \(n=k\), or (8) if \(n\
eq\)\(k\)
13: Initialise Tx delay: \(T_{tx}[n,k]=T_{m}[n,k]\)
14:endif
15:while\(\eta_{col}(\mathcal{N},\textbf{T}_{tx},\tau_{g})>0\)do
16: Increment Tx delay: \(T_{tx}[n,k]\leftarrow\)\(T_{tx}[n,k]+\tau_{step}\)
17:endwhile
18:endfor
19:endfor
```  
**Algorithm 1** FD-LTDA-MAC scheduling based on greedy optimisation algorithm
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 6 Experimental Setup
### Evaluation Criteria
#### 6.2.3 Vehicle Traceability  
Traceability is another privacy metric that measures the tracking percentage of vehicles during a trip. It is inverse to the level of location privacy. It is the probability of an adversary determining the location spots of the vehicles. Let \(T_{v}\) be the tracing probability of an attacker itself, a measure of anonymity set. Traceability can be defined as given below [42]:  
\[T_{v}=[1-Pr(|ASS|)]. \tag{26}\]  
\(Pr\) is the probability of vehicle anonymisation during the pseudonym change process. The value of \(T_{v}\) equal to 1 means that the adversary successfully tracked the target vehicle by linking its various pseudonym during various location spots. With increasing anonymisation, the traceability strength of an adversary reduces over time.
## 5 Results and Discussions
### Proposed LQR-FI Pitch Angle Controller under Fault Flight Condition  
The proposed controller can successfully compensate for the 50% and 80% loss of actuator effectiveness faults as shown in Figure 5a,b. Furthermore, Table 1 shows the obtained design parameters of vectors Q\({}_{new}\), R, \(\lambda\), K\({}_{l}\) and performance index IAE for the proposed controller compared with a conventional LQR controller in the case of 50% and 80% loss of the actuator gain's fault.