esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Comparative Discussion on Various Energy-Efficient Navigation Systems
### Machine Learning-Based Navigation Systems  
MR navigation constraints can be trained using machine learning (ML) approaches, including reinforcement learning (RL) [49]. This type of system generates navigational decisions based on knowledge and experience. ML-based navigation systems can maximize energy efficiency by learning strategies that minimize unnecessary motions and use less energy. But initially, training the models might require a lot of computing time and energy. Thus, the PSO technique is better for energy conservation when compared with other ML-based navigation systems. The quantity and quality of training data, the quantity of required flexibility, the preferred degree of optimization, and the inherent complexity of the surroundings will all impact the decision between PSO-based navigation and ML-based navigation. PSO-based navigation is focused on optimization and is excellent for difficult path-planning problems, whereas ML-based navigation is adaptive and data-driven [50].
## 2 Methods
### Active Maturity Classification
#### 2.2.3 Dynamic Viewpoint Selection and Maturity Classification  
Figure 6 shows the developed dynamic viewpoint selection algorithm for the maturity classification. The decision whether an additional viewpoint is required uses the classification uncertainty resulting from the initial maturity classification based on the first viewpoint (Section 2.2.3.1) and an economic analysis (Section 2.2.3.2). If an additional viewpoint is deemed profitable, the next viewpoint is selected based on a spatial analysis of the variability in color (Section 2.2.3.3). The maturity of the pepper is then reclassified by combining the additional viewpoint with the original single viewpoint (Section 2.2.1) using the same classification model as for the initial maturity classification.  
Although this algorithm can be easily adapted to include more viewpoints, we considered a single additional viewpoint. This is based on prior research that showed no significant improvement in the classification accuracy using more than two viewpoints [31]. Furthermore, adding more viewpoints increases cycle times, and hence this was not considered.
## 3 Results and Discussion  
### Effect of the Roller's Rotational Speed on the Damaged Seed Percentage at Different Feed Rates  
The experimental results obtained from the current study are represented by line charts. As is shown in Figure 4, the damaged seed percentage increased from 0.36% to 0.88%, 0.46% to 0.98%, 0.73% to 1.15% and 0.92% to 1.28% with the increase of the roller's rotational speed to 150, 200, 250 and 300 rpm for 600 kg/h, 700 kg/h, 800 kg/h and 900 kg/h feed rates, respectively. At higher roller rotational speeds, the threshing barsstruck the sunflower heads and seeds more strongly, breaking more seeds, which was why higher speeds resulted in higher damaged seed percentages. The results of the seed damage percentage of this study coincided with those in previous studies [8; 28], while the accumulation of sunflower heads inside the threshing box at high feed rates also led to a rise in the broken seed percentage.
# Smart Low-Cost Control System for Fish Farm Facilities
## 5 Smart Control Algorithm for Aquaculture
### Included Algorithms in the Complete System
#### 5.1.1 Predefined Control Algorithms  
These algorithms are part of the backup system which allows the sensor nodes to communicate with actuator nodes in the case that the connectivity with the DB is lost. These algorithms are based on the activation or deactivation of actuators based on data sensed from the sensors of given time parameters. These predefined control algorithms are not used if the sensor and actuator receive information from the DB. Thus, the commands from the DB can modify and update these predefined control algorithms.  
An example of these predefined control algorithms in sea cages includes the feeding algorithm, which includes the data from the falling feed sensor to reduce the feed velocity when uneaten feed is detected to reduce the waste feed. Another example is the activation of the feeder at a given time if no information from the DB is received and fed with the same amount of feed as the previous shot. A last example is postponing feeders' activation if wind velocity or water current is above an established value or if DO is below a threshold.  
An example in RAS is the possibility of turning off the tank's water pump in case water quality exceeds a given threshold for the different sensed parameters. Other examples are the activation of aeration if DO drops below a certain value and the activation and deactivation of light according to established photoperiod.  
Figure 14: Algorithms of control system for fish farms. Five types of algorithms are detailed in this paper. The predefined control algorithms are used as a specific fault tolerance algorithm to allow the system to operate even without connectivity with DB. Energy-efficient algorithms try to reduce the energy consumption in the nodes. Fault tolerance algorithms aim to reduce the system’s possible failures based on different backup mechanisms. The data management algorithm is applied only in the database and intends to process and compute the information. Finally, the smart control algorithm is the artificial intelligence engine and the database operating with the data and deciding the most efficient decision for the fish farm.
## 2 Methods
### Monte Carlo Estimation Model
The \(RSRP\) is determined per resource element (bandwidth) and is therefore multiplied by a factor of 12 for each resource block bandwidth, as this data originates from LTE. Note: while in LTE, each resource block can only have 12 subcarriers with 15 kHz bandwidths per resource block, there are more options in 5G new radio (NR). \(M_{RB}\) is the number of resource blocks occupied in the downlink. The authors in [23] report an average per-user downlink data rate of approximately 20 Mbps in an LTE cell with a 100% load, a 15-MHz bandwidth and an \(RSRP\) of \(-100\) dBm at the cell edge and a \(2\times 2\) multiple-input multiple-output (MIMO). Based on this information, we observe that an LTE cell with 75 resource blocks in the downlink can achieve a per-user downlink capacity of 20 Mbps with a cell edge \(RSRP\) of \(<\)\(-120\) dBm and no MIMO present. The factor \(\Delta_{\text{eff_{\text{ACSG}}}}\) is the increase in spectral efficiency between 5G and LTE. Based on [24], \(\Delta_{\text{eff_{\text{ACSG}}}}\) is assumed to be 3, but this factor of 3 can be disputed, especially in initial implementations of 5G networks [25]. However, in this study design, with an emphasis on relative statements, the same factor applies to mobile stations and base stations and is therefore not relevant to evaluate differential effects of network structures and data usage. \(\Delta_{\text{data}_{\text{AGC}}}\), the increase in data capacity required for transitioning from LTE to 5G, is expected to be at least 5 with respect to the reference LTE cell with 20 Mbps at a cell edge \(RSRP\) of \(<\)\(-\)120, i.e., 100 Mbps. \(UL_{\text{rf}}\) is the uplink radiation factor accounting for propagation losses in the user equipment (UE) and the user's body (6 dB). \(A_{\text{e}}\) is the effective aperture of the UE antenna that measures the \(RSRP\). We apply the \(A_{\text{e}}\) of a dipole antenna at 2 GHz.
## 5 Security Analysis
### Session Key Security  
Similar to privileged internal attacks, the RCRS key Xs is known only by the RCRS itself. In our proposed TCVK scheme, the session key SK is related to the random number generated by each legitimate participant in the pre-checkups phase and subsequent verification phase. Owing to thecharacteristics of the hash function, it is not feasible to calculate the session key SK in polynomial time to obtain a random value. Therefore, our proposed scheme provides session key security.
# Towards a Reference Architecture for Cargo Ports
## 2 Background
### Cargo Ports
#### 2.2.2 Container Terminal Overview  
Container terminals provide many services, i.e., container loading/unloading to and from vessels and feeder ships for import or export purposes, internal container movement from ships to stacking areas and vice versa, stacking containers in dedicated areas distributed in the terminal area, container inspection for customs requirements, refrigerated containers handling and storage, etc. All the above processes require several shared and reusable resources and equipment to fulfill the tasks involved in handling and transporting containers, such as dock cranes, yard cranes, transport vehicles, yard stacking deposits, automatic stacking cranes or automatic storage/retrieval systems, railway tracks, and human operators. All processes and operations are usually planned, scheduled, monitored, and controlled by a central supervisor and make use of information technologies and IoT to allow fast shipping operations, optimize the usage of facilities, and reduce lag times [33].  
A typical modern container terminal installation is represented in Figure 2 using UML package notation. It includes [34]:  
1. An area for port physical security and access control; this incorporates the physical entrance to the terminal, video surveillance, gates, and the infrastructure necessary to check the credentials of the maritime transportation workers requiring unescorted access to the secure areas of the port. Credentials are typically biometric identification cards, also known as TWICs (Transport Worker Identification Cards);
2. The cargo handling equipment at shipside and landslide, which includes the container terminal cranes, transport vehicles, and similar conveyances;* An area for intermodal transportation of the containers, such as commercial long-haul trucks, railways, or even other ships;
* A terminal operating center, incorporates the financial, communications, customs, IT, security, and other back office functions.
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 3 Problem Description
### System Model
Meanwhile, the duration of a delay in addressing a session request from a given user is measured starting from the time the request is forwarded to the RT processing queue or the NRT processing queue to the time his/her data is completely transmitted. Furthermore, the basic power consumption required by the BS to activate CC-\(i\) is denoted with \(p_{i}\), while \(p^{(z)}(s)\) is used to denote the total power consumption of all the active CCs in configuration \(z\) during round \(s\). Moreover, \(T_{i}\) denotes the bandwidth (in Hz) of CC-\(i\), while the total system bandwidth is denoted by \(T=\sum_{i=1}^{c}T_{i}\). As such, \(p^{(z)}(s)\) can also be expressed as \(p^{(z)}(s)=\sum_{i=1}^{c}p_{i}v_{i}^{(z)}(s)\) while \(\sum_{k\in\Re_{w}(s)\cup\Re_{s}(s)}b_{k}(s)\leq T^{(z)}(s)\), where \(T^{(z)}(s)=\sum_{i=1}^{c}T_{i}v_{i}^{(z)}(s)\) and \(b_{k}(sallocated to the active user-\(k\) during round \(s\). It is thus implied that, given a specific configuration \(z\), the total fraction of bandwidths occupied by active users during each round can be distributed arbitrarily over any sub-channels of the activated CCs.
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 6 Conclusions  
This work develops a lithium\(-\)ion battery scheduling architecture based on deep learning and reinforcement learning, demonstrating the prospect of using a data\(-\)based capacity estimation environment model and DRL algorithm to solve the power battery scheduling problem. The SOC estimation model is trained using a deep learning algorithm as the reinforcement learning agent environment. We combine principal component analysis and double DQN for training the agent. Several publicly available power battery cycle aging datasets are used for validation. The experimental results show that the SOC estima  
Figure 12: Cycle life on LEP and NCA, NCM, NCA+NCM dataset.  
tion model trained by the neural network interacts well with the agent. Compared with PSO and GA algorithms, the proposed method has a slight advantage in the battery cycle life index and has strong adaptability, which does not need to develop specific schemes for batteries with specific chemical compositions. Compared with DDQN and Q\(-\)learning algorithms, this method outperforms other methods regarding convergence time and cumulative rewards. However, we cannot guarantee that the DDQN algorithm, based on principal component analysis is optimal. In future work, more accurate power battery models can be used for simulations, adding specific temperature\(-\)influencing factors and considering the varying application requirements in realistic scenarios.  
Conceptualization, H.X. and J.C.; methodology, J.C.; software, S.R.; validation, S.R., A.Z. and J.C.; formal analysis, H.X.; data curation, J.C.; writing\(-\)original draft preparation, J.C.; writing\(-\)review and editing, A.Z.; visualization, S.R.; supervision, H.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program \"Key Technology for Collaboration and Interoperability of Distribution Grid Business Resources (2021 YFB2401300)\".  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions
### Concluding Remarks  
In model construction and optimization, we successfully built a multi-neural network integration model and optimized its output weights using the Whale Optimization Algorithm. Through hyperparameter tuning, we determined the optimal model structure and adopted techniques such as the tanh activation function, Adam optimizer, early stopping, and L2 regularization to prevent overfitting and ensure efficient convergence. The model demonstrates excellent performance in predicting field strength in high-speed rail carriages. Future work can explore more efficient hyperparameter selection strategies based on the existing model framework.  
For data processing and dataset construction, we integrated dual-path modeling, on-site channel measurements, and ray-tracing (RT) simulations to create a high-precision training dataset. Channel measurements in a CR400 BF train compartment provided real data, while RT simulations generated a dataset of 4320 samples, forming a solid foundation for model training. Future work can incorporate data from diverse environments to enhance model adaptability.  
In model evaluation and comparison, we employed multiple metrics (RMSE, MAE, MAPE, and R2) to assess model performance. Results show our model outperforms traditional and comparative models, confirming its effectiveness. Additionally, our model demonstrates strong robustness under the specified seat conditions, further validating its stability and adaptability in practical applications. The evaluation approach in this study provides a valuable reference for future model refinements.
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 2 Materials and Methods
### Requirements of International Regulations for Preventing Collisions at Sea (COLREGS)  
The CA behaviors regulated by COLREGS for two power-driven vessels may be largely characterized as one of three encounters: overtaking (Rule 13), head-on (Rule 14), and crossing (Rule 15) [23], as shown in Figure 6. Rule 13 provides the numerical criteria that \"a vessel shall be deemed to be overtaking when coming up with another vessel from a direction of more than 22.5 degrees abaft her beam\". The other two rules have no clear quantification for the arising condition of the corresponding encounters. The overtaking and crossing encounters each identify one vessel as being required to \"give-way\" (Rule 16) and the other to \"stand-on\" (Rule 17). The head-on situation requires both vessels to alter course to starboard so that each shall pass on the port side of the other (Rule 14). Rule 16 also requires each give-way vessel to take early and substantial action (course and/or speed change) to keep well clear. A solid arrow indicates the present heading and speed of each vessel, and a dashed arrow indicates the possible direction in which the vessel may take CA action. Speed change is unusual in the practice of navigation except in some emergency situations; therefore, in this paper, course change is assumed as the only strategy adopted for avoiding a collision, which is suitable for the maneuvers of the own ship and all TSs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description and Symbols** & **Value** & **Unit** \\ \hline Length overall (LOA) & 171.8 & m \\ Length between perpendiculars (LBP) & 160.93 & m \\ Maximum beam (B) & 23.17 & m \\ Design draft (T) & 8.23 & m \\ Loaded displacement (D) & 18,541 & T \\ Design speed (U) & 15 & knots \\ Max. rudder angle (\(\delta\)) & 35 & degree \\ Max. rudder rate (\(\hat{\delta}_{max}\)) & 5 & degree/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ship particulars of Mariner class vessels.  
Figure 5: Coordinate system of ship maneuvering motion.
## 2 Materials and Methods
### Defect Detection Using Convolutional Neural Network (CNN)
#### 2.3.2 Description of the CNN  
In this work, the VGG-16 convolutional neural network (CNN) architecture, developed by the Visual Geometry Group (VGG) at Oxford University in 2014 [29; 30], was employed  
Figure 6: C-scan generation of 2 classes of datasets. (**a**) Sample No.1 \"defective\" area; (**b**) Sample No.1 \"non-defective\" area; (**c**) Sample No.2 \"defective\" area; (**d**) Sample No.2 \"non-defective\" area. colors in the image represent the amplitude of the reflected ultrasonic signals.  
to classify defective and non-defective areas at the interface of adhesively bonded materials, using C-scan images generated at subsequent time instances of the ultrasonic signals. The VGG-16 CNN architecture was selected for its simplicity and effectiveness in image classification tasks. The network is characterized by its use of \(3\times 3\) convolutional filters, which not only simplify the network design but also enhance its capacity to capture fine spatial features in images while maintaining computational efficiency. This is particularly crucial in the analysis of ultrasonic images, where defects may introduce subtle variations that require precise detection. Although more advanced architectures such as ResNet and Inception were considered for their innovative features--like skip connections and varying filter sizes within each layer--the sequential structure of VGG-16 was found to be sufficiently robust for this specific task. Additionally, VGG-16 strikes an optimal balance between accuracy and computational complexity, making it particularly well-suited for applications with limited training data, such as in non-destructive testing.
## 3 Preliminaries and System Model
### Preliminaries  
The relaying strategy can be either amplify and forward (AF) or decode and forward (DF). While a DF relay decodes, re-modulates, and retransmits the received signal, an AF simply amplifies and retransmits the signal without decoding. Compared to an AF relay, the complexity of a DF is significantly higher due to its full processing capability. In our model, we used the DF relaying strategy because it will result in better SNR at the destination with less power requirements at the relays, thus, the interference temperature control level at the primary can be achieved efficiently.  
Figure 4 shows various techniques of identifying interference to the primary network, which are grouped under two main types: the geo-location technique and spectrum sensing [28]. As mentioned earlier, IEEE 802.22 uses both geo-location and spectrum sensing for spectral awareness. Figure 5 shows how geo-location-based interference management can be used to minimize the interference to the primary network.
# Coverage Extension for the UK Smart Meter Implementation Programme Using Mesh Connectivity
###### Abstract  
Smart meters (SM) with wireless capabilities are one of the most meaningful applications of the Internet of Things. Standards like Zigbee have found a niche in transmitting data on energy usage to the user and the supplier wirelessly via these meters and communication hubs. There are still certain difficulties, notably in delivering wireless connectivity to meters situated in difficult-to-reach locations such as basements or deep indoors. To solve this issue, this paper investigates the usage of mesh networks at 868 MHz, particularly to increase coverage, and proposes an additional mounted antenna to significantly increase outside coverage while providing the necessary coverage extension for hard-to-reach indoor locations. Extensive measurements were made in Newbury in both suburban and open environments for validation and delivery of a simple statistical model for the 868 MHz band in United Kingdom conurbations. Results presented in this paper estimate that mesh networks at 868 MHz can accommodate large areas constituting several SMs with the proposed coverage extension method. With our findings and proposed methods on mesh connectivity, only 1% of UK premises will require mesh radios to achieve the desired coverage.  
mesh; 868 MHz; 2.4 GHz; smart meter deployment; coverage extension Article Coverage Extension for the UK Smart Meter Implementation Programme Using Mesh Connectivity [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], M.A. Coverage Extension for the UK Smart Meter Implementation Programme Using Mesh Connectivity. _Telcom_**2022**, 3, 610-618. [[https://doi.org/10.3390/telecom3040034](https://doi.org/10.3390/telecom3040034)]([https://doi.org/10.3390/telecom3040034](https://doi.org/10.3390/telecom3040034))
## 2 System Model
### Wht-Nomma in Small-Cells
#### 2.3.2 WHT-NOMA Receiver with Nonideal SIC  
In this case also, just like the nonideal SIC part for the conventional NOMA receiver in Section 2.1, the SCU performs SIC in the presence of the channel effects produced by the transmission channel, i.e., the Rayleigh fading channel. In this case, for WHT-NOMA, the signal information of SEUs at the UE-\(m\) served by the \(b\)th SBS is given using (24) as:  
\[\hat{s}_{\ b,k}^{\ H}=h_{b,k}\times\left(\sum_{k=m+1}^{N}s_{b,k}^{H}\sqrt{P_{b,k }}\right)+\underbrace{n}_{noise}+\underbrace{I}_{ICI} \tag{28}\]  
where, \(n\), \(I\), and \(h_{b,k}\) represent the AWGN, ICI, and the Rayleigh fading channel, respectively. Then, in the WHT-NOMA case, the signal information of UE-\(m\), served by \(b\)th SBS, is extracted using (24) and (28):  
\[s_{b,m}=\Bigg{|}\left(\frac{y_{b,m}-\hat{s}_{\ b,k}^{\ H}}{\sqrt{P_{b,k}}} \right)\times H_{i}\Bigg{|}. \tag{29}\]  
For the two-UE-pair case in WHT-NOMA, the signal information of UE-2 at UE-1, after including the effects of the Rayleigh fading channel, is given using (28) as:  
\[\hat{s}_{\ b,2}^{\ H}=h_{b,2}\times s_{b,2}^{H}\sqrt{P_{b,2}}+\underbrace{n}_ {noise}+\underbrace{I}_{ICI}. \tag{30}\]  
The UE-1's received signal, after SIC, can be extracted using (25) and (30) given as:  
\[s_{b,1}=\Bigg{|}\left(\frac{y_{b,1}-\hat{s}_{\ b,2}^{\ H}}{\sqrt{P_{b,1}}} \right)\times H_{i}\Bigg{|}, \tag{31}\]  
where, as aforementioned, \(H_{i}\) is the WHT matrix of the same order \(i\), as used for the SBS transmitter, and \(\lfloor.\rfloor\) represents the demodulation and detection of the received signal.  
After the description of the WHT-NOMA receiver for both ideal and nonideal SIC cases, the description of the IRC model is next presented.