esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Integration of DL-Based NOMA with Emerging Technologies
### IRS-Assisted NOMA  
Due to their superior spectrum-, energy-, and cost-efficiency [149; 150], intelligent reflecting surfaces (IRS) have been identified as a potentially game-changing technology for sixth-generation (6G) wireless communications [151]. Essentially, IRS is a cheap antenna array with a high number of tunable reflecting pieces [152]. IRS has the capacity to regulate the wireless communication propagation environment. In contrast, a number of tried-and-true methods, such as massive multiple-input multiple-output (massive-MIMO) and cooperative communications, solely focus on how the transceiver can adapt to the channel environment [153]. When the line-of-sight (LOS) between the BS and the users is obstructed by obstacles like buildings or mountains, IRS can be used to forge other pathways to maintain reliable connections and quality of service (QoS). In [152], the authors integrate NOMA with the IRS because they like its benefits, namely its high spectrum efficiency, and because research shows that it performs better than standard orthogonal multiple access [154]. Unmanned aerial vehicles (UAV), in contrast, are a promising 6G approach that can be employed to increase IRS adaptability. Previous research [155] optimized beamforming, phase shift, and UAV location for a 2-user IRS-UAVNOMA downlink system and found the best-closed form for beamforming.  
Nevertheless, the time-varying multiuser situation is more like wireless communication networks. Therefore, it is challenging to tackle these non-convex joint optimization problems using traditional optimization techniques like convex optimization. In contrast, artificial intelligence (AI) techniques like deep learning (DL) and deep reinforcement learning (DRL) have been effectively used to solve a number of wireless communication challenges [156]. Wireless communication systems can learn by interacting with their surroundings using DRL-based methodologies instead of deep learning (DL), which requires many training labels. DRL is better because it is hard to get training labels in real-time wireless communication systems. Also, deep deterministic policy gradient (DDPG) is better than other DRL techniques like deep Q networks (DQN) for situations with a high-dimensional continuous action space.  
In an article [151], the authors look at how DRL-based approaches may be used for the IRS-UAV-NOMA downlink system for multiple users, as shown in Figure 10. To maximize the efficiency of the BS's power distribution, the IRS's phase shifts, and the UAV's horizontal position all at once, the DDPG algorithm is included in the DRL framework. The improved performance of the suggested algorithm is shown by computer simulations, which show both how fast it is and how well it can handle different inputs. A comparison of studies reported on IRS-assisted NOMA is presented in Table 5.  
Figure 10: UAV-based IRS-assisted NOMA system.
# A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests
## 2 System Overview
During each time slot, a single transmitter insonifies the area under the array with a short pulse, and all sparse receive elements are used to sample the scattered wavefield. This sparse MIMO array significantly reduces the number of required transducers and cost but at the expense of less array gain and increased sidelobe level. Therefore, specialized signal processing and image reconstruction algorithms are needed to extract maximal information from the data and to optimize the sparse array performance.
## 2 Overall Analysis Approach and Implemented Methods
### Thermodynamic Analyses  
The tank thermodynamic analysis was carried out according to the procedure presented in [24]. The thermodynamic analysis objective is to determine weight, volume, and pressure of the LH\({}_{2}\) contained in the tank, at each point of the mission. This computation is not as simple as for the traditional fuel, since the density of the saturated H\({}_{2}\) is variable with the tank state, and because of the eventuality of venting in case of overpressure. In addition, the analysis needs to check that the H\({}_{2}\) pressure never falls under the minimum pressure to avoid contamination from atmospheric oxygen ([25; 33]).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Safety Factors**} & \multicolumn{2}{c}{**Test Case \#1**} & \multicolumn{2}{c}{**Test Case \#2**} \\ \cline{2-5}  & **ASME ([38;39])** & **FEM** & **ASME ([38;39])** & **FEM** \\ \hline Tube—longitudinal tensile stress \([-]\) & 1.99 & 2.03 & 3.18 & 3.18 \\ \hline Tube—circumferential tensile stress \([-]\) & 1.00 & 1.00 & 1.69 & 1.67 \\ \hline Endcap—tensile stress \([-]\) & 1.00 & 0.98 & 2.66 & 2.55 \\ \hline \multirow{2}{*}{Tube Buckling} & Buckling stress \([-]\) & 1.81 & 1.82 & 1.32 & 1.35 \\ \cline{2-5}  & 1 st Eigenvalue \([-]\) & - & 2.55 & - & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Safety factors calculated for the structural test cases.  
Figure 13: Visualisation of buckling analysis results for test cases #1 (**a**) and #2 (**b**) under ultimate downward inertial loads (\(n_{r}=4.5\)).
## 3 Derivation of the Theoretical Formula of the Surrounding Rock Loose Zone
### Theoretical Derivation of Work-Energy Relationship Method
#### 3.2.1 Based on Simple Work-Energy Relationship Theory  
The blast heat works on the surrounding rock to overcome the resistance per unit area of the surrounding rock; that is, to overcome the tensile strength (\(E_{P}\)) of the surrounding rock, resulting in radial cracks and water wave circumferential cracks in the surrounding rock. Outside the roadway contour, within the range of the peripheral hole spacing (\(a\)) and blast hole charge length (\(L\)), the surrounding rock with a certain thickness (\(H\)) is broken, cracked, and loosened. Therefore, the mechanical work of the explosion heat on the surrounding rock can be calculated as follows [43]:  
\[W=ahLE_{p} \tag{36}\]  
The heat (\(Q\)) generated by the explosive loaded in the perimeter blast holes after the instantaneous explosion is:  
\[Q=keNqL \tag{37}\]  
Mechanical energy can generate heat by performing work, and heat can also perform work. Heat is a form of energy. The ratio of work (\(W\)) to its equivalent heat (\(Q\)) is the thermal work equivalent (\(J\)). That is, \(J=W/Q\). \(N\) is the charge per meter of the blast hole, \(q\) is the heat released when each kilogram of explosive explodes, and \(k\) is the correction coefficient, which increases with the increase in the surrounding rock grade.  
The corrected thickness of the blasting-expansion loose zone \(L_{b2}\) is:  
\[L_{b2}=h=\frac{kNqL}{aL_{p}} \tag{38}\]  
Combined with the thickness of the stress loose zone derived based on the Fenner solution, the formula of the final loose zone thickness under the consideration of the influence coefficient is as follows:  
\[L_{b}=mL_{b1}+nL_{b2} \tag{39}\]
## 4 Experimental Environment and Results
### Field Study Results  
This study validated the practicality of the seamless technology framework and the effectiveness of the \"Pillow Fight\" system through two phases of systematic field research. The study employed mixed methods, systematically evaluating the outcomes through quantitative tools (SUS usability scale) and qualitative methods (observations and interviews).
## 4 Results and Discussion
### The Effect of Air Leaking into the Primary Fluid on the Ejector Performance
#### 4.1.3 Distribution of the Species Component with Air Leaking into the Primary Fluid  
The species component distribution of the ejector at different air mass fractions is depicted in Figure 10. The red region represents the primary fluid, composed of steam and air, and the blue region is the secondary fluid of pure steam. The two green strips between the blue and red regions form the mixing zone of the two fluids. The air mass fraction of the mixing zone increased along the axis of the ejector. A shock train pattern of the primary fluid emerged along the ejector axis and subsequently decomposed until it disappeared into the surrounding fluid in the diffuser. Eventually, a fully mixed zone with the same air mass fraction was formed at the rear of the diffuser. We found the commonality of these component distribution diagrams: the air mass fraction of the mixing zone gradually increased along the ejector axis. A comparison of Figures 9 and 10 revealed that air significantly reduces the velocity of the mixing zone compared to no air in the diffuser. This further confirms that air leaking into the primary fluid decreases the momentum of the mixing zone to resist the back pressure. Therefore, air leaking into the primary fluid impairs the ability of the ejector to resist the backpressure.
# Effects of Shading on the Growth and Carbon Storage of _Enhalus acoroides_
## 3 Results
### Sediment Carbon  
The sediment carbon content was higher in the control than in the shading treatment (Figure 4). There was no significant difference in the sediment carbon content at different depths in the control (\(p>0.05\)). When the light was reduced, the sediment carbon content at 0-5 cm in the high shading treatment was significantly lower than that in the control at the same depth (34.
## 3 Stationary Localization Algorithms
### Distributed Localization Algorithms
#### 3.2.5 Underwater Sensor Positioning (USP)  
Underwater Sensor Positioning (USP) scheme is proposed in [29, 30, 31] for sparse 3D UWSNs, as shown in Figure 6. At least three anchor nodes are included in the network. To simplify the process of endowing anchor nodes with their positions, they are placed at the surface as GPS-enabled buoys. In USP scheme, unknown nodes get their depth information by pressure sensors. The depth information is used to transform the 3D underwater positioning problem into its 2D counterpart via the projection technique. For example, consider an unknown node \(U\) that needs to compute its position within a 3D oceanic deployment area. It is assumed that node \(U\) is within communication range of three anchor nodes \(A\), \(B\) and \(C\) located at known positions \((x_{A},y_{A},z_{A})\), \((x_{B},y_{B},z_{B})\), \((x_{C},y_{C},z_{C})\), respectively. Given \(U\)'s depth information \(z_{U}\), node \(A\) is projected as node \(A^{\prime}\) located at position \((x_{A},y_{A},z_{U})\). Nodes \(B\) and \(C\) are projected as nodes \(B^{\prime}\) and \(C^{\prime}\) located at position \((x_{B},y_{B},z_{U})\) and \((x_{C},y_{C},z_{U})\). After three anchor nodes \(A^{\prime}\), \(B^{\prime}\) and \(C^{\prime}\) have been projected, elegant localization methods such as simple bilateration can be employed to localize node \(U\).  
USP scheme first uses depth information to localize unknown nodes in sparse 3D UWSNs and has low storage and computation requirements. USP scheme seems simple by introducing pressure sensors. However, installing pressure sensors increases energy consumption per unknown node, hence decreasing the life time of the UWSNs. Moreover, USP scheme has low localization success. In addition, each unknown node consumes much energy to map the available anchor nodes on the horizontal plane it resides on. Furthermore, unknown nodes in deep water cannot be localized because they are without the anchor node's communication range.  
Figure 5: The Underwater Positioning Scheme.  
Figure 6: The Underwater Sensor Positioning.
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 4 Results and Discussion  
### Experimental Setup  
The MIT battery dataset contains three batches of batteries, namely the \"2017-05-12\" batch, the \"2017-06-30\" batch, and the \"2018-04-12\" batch. According to the experimental scheme proposed by [PERSON] et al. [PERSON] et al. (2018), the dataset is divided into a training set and two test sets. The training set is used for model training and parameter selection, including 20 batteries from the first batch and 21 batteries from the second batch; the test set is used to evaluate model performance, where the main test set contains 21 batteries from the first batch and 22 batteries from the second batch; and the secondary test set includes 40 batteries from the third batch. The first two batches of batteries are from the same year, constituting the training set and the main test set, and the third batch is from the batteries of the second year, serving as the secondary test set. In the experiment, the prediction results of the main test set are used to verify the accuracy of the model, while the results of the secondary test set better demonstrate the generalization ability of the model.  
Based on the transformer-LSTM fusion model, we conducted three experiments on SOH prediction. In the first experiment, the degradation feature data from the same battery was divided into training set, validation set, and test set in a ratio of 4:1:5. The model was trained on the first half of the dataset to predict the SOH of the second half. The second experiment utilized the training set consisting of data from 41 batteries and selected data from one battery as the validation set. The model then performed SOH prediction on two different test sets, one consisting of 43 battery data and the other consisting of 40 battery data. The third experiment served as a comparative test to evaluate the performance of various models--LSTM, CNN-LSTM, and transformer-LSTM fusion--in SOH prediction. These experiments comprehensively assessed the predictive performance of the proposed model under diverse conditions. The experiments were conducted using Python 3.8 as the programming language and implemented with toolkits such as PyTorch 2.3.1, Scikit-learn 1.3.2, and Pandas 2.0.3.
## 3 Materials and Methods
### Groundwater Irrigation Quality Indices  
Since agricultural practice is an essential activity in the northern Beichuan River Basin, it is necessary to consider a series of groundwater irrigation quality indices to evaluate the groundwater suitability for irrigation usage. The indices involved in this study are separately electrical conductivity (EC in \(\upmu\)S/cm), sodium adsorption ratio (_SAR_), sodium percent (%Na), residual sodium carbonate (_RSC_), Kelly's ratio (_KR_), permeability index (_PI_), and potential salinity (_PS_). The calculation methods for these parameters are provided below [25]:  
\[SAR=\frac{Na^{+}}{\sqrt{(Ca^{2+}+Mg^{2+})/2}} \tag{13}\]  
\[\%Na=\frac{Na^{+}+K^{+}}{Ca^{2+}+Mg^{2+}+Na^{+}+K^{+}}\times 100 \tag{14}\]  
\[RSC=(HCO_{3}^{-}+CO_{3}^{2-})-(Ca^{2+}+Mg^{2+}) \tag{15}\]  
\[KR=\frac{Na^{+}}{Ca^{2+}+Mg^{2+}} \tag{16}\]  
\[PI=\frac{Na^{+}+\sqrt{HCO_{3}^{-}}}{Ca^{2+}+Mg^{2+}+Na^{+}}\times 100 \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Exposure Parameter** & **Infants** & **Children** & **Females** & **Males** \\ \hline IR & 0.65 & 1.50 & 2.66 & 3.72 \\ EF & 365 & 365 & 365 & 365 \\ ED & 0.5 & 6 & 30 & 30 \\ BW & 6.94 & 25.9 & 64 & 73 \\ AT & 365 \(\times\) ED & 365 \(\times\) ED & 365 \(\times\) ED & 365 \(\times\) ED \\ K\({}_{\text{P}}\) & 0.001 & 0.001 & 0.001 & 0.001 \\ SA & 3640 & 9186 & 17,166 & 18,482 \\ T & 0.4 & 0.4 & 0.4 & 0.4 \\ EV & 1 & 1 & 1 & 1 \\ CF & 0.002 & 0.002 & 0.002 & 0.002 \\ RfD\({}_{\text{oral}}\)-NO\({}_{3}\)\({}^{-}\) & 1.6 & 1.6 & 1.6 & 1.6 \\ RfD\({}_{\text{dermal}}\)-NO\({}_{3}\)\({}^{-}\) & 0.8 & 0.8 & 0.8 & 0.8 \\ RfD\({}_{\text{oral}}\)-F\({}^{-}\) & 0.06 & 0.06 & 0.06 & 0.06 \\ RfD\({}_{\text{dermal}}\)-F\({}^{-}\) & 0.03 & 0.03 & 0.03 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exposure parameters used in the HHRA model for different populations in the study area.  
\[PS=Cl^{-}+\frac{1}{2}SO_{4}^{2-} \tag{18}\]  
where the concentrations of all ions above are in units of meq/L. The evaluation standards of groundwater irrigation quality in view of the calculated indices are exhibited in the subsequent sections in detail.  
Besides, the combination of certain irrigation indices provides a comprehensive interpretation of the groundwater irrigation quality [9; 16], for example, the permeability index diagram using the PI versus total ion concentration (meq/L), the Wilcox diagram consisting of EC and %Na, and the USSL diagram constructed by SAR and salinity hazard.
# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 2 Literature Review  
### Sensor Dependent Detection Methods  
Roadway traffic safety management and its automation have received increasing attention. An integral aspect of traffic safety management is accident detection, as vehicle collisions on highways typically result in the obstruction of a lane, inducing an increase in traffic volume in remaining lanes, leading to the cumulative risk of subsequent accidents [12]. Factors influencing traffic accident risk range from environmental factors to temporal-spatial patterns. Previous studies illustrate the magnitude of influence of certain factors on overall accident rates and have devised data models to predict risk within a specified segment based on available data. Some conventional proactive accident detection approaches are centred around sensor-recorded data or environmental factors such as precipitation, traffic volume, and road geometry [13; 14; 15]. Others have identified non-physical patterns regarding accident rates, such as the spatio-temporal correlation of traffic accident occurrences and traffic risk's non-uniform distribution over time, and established predictive models based on traffic accident data [16].  
Despite serving as a predictive model from environmental data, such models do not define real-time cumulative risk (from relevant aspects) to be referenced. Specifically, environmental factors such as road design and spatio-temporal patterns are invariant in traffic flow and thus fail to significantly reflect the state of traffic during which risk inferences are made [16]. In addition, sensor-collected data are integral to some of the proposed models (radar, inductive loops, etc.).  
Despite extensive research on sensor-dependent detection methods, the implementation of such methods has encountered numerous obstacles. For instance, the expense required for certain hardware installations often renders such approaches unfeasible on a large scale [17]. This deficiency is aggravated by the prevalence of vehicle-based approaches that require data collected from sensors installed on each vehicle to achieve an optimal effect [18].
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 4 Experiments and Results
### Ablation Experiments
#### 4.2.3 Training Rate  
In this section, we investigate the impact of the amount of data in the training set on the DIR-Net decoding algorithm. There are a total of \(2^{K}\) codewords when the information bit length is \(K\). In theory, when \(2^{K}\) codewords are involved in training, the decoding network obtains the best decoding performance. When the value of \(K\) is small, it is feasible to use full codeword training. However, as the length of the codewords increases, the number of corresponding codewords increases exponentially. Thus, it is impossible for all codewords to participate in training. During the training process, we only selected part of the data as the training set. Specifically, we calculated the total amount of data participating in training as follows:  
\[n_{d}=r_{d}\times 2^{K}, \tag{16}\]  
where \(r_{d}\) is the data utilization rate with a value range of \((0,1]\), representing the ratio of the number of codewords participating in training to the total number of codewords. In our experiment, the number of information bits of our codeword \(K\) was 16, and the encoded codeword bit \(N\) was 32. In contrast, we set \(r_{d}\) to 0.4, 0.6, 0.8, and 1.0 to train the network. During the test phase, a batch of codewords was extracted randomly into the network for decoding. The SNR/BER curve of the obtained decoder is shown in Figure 10.  
Figure 9: The comparison of BER performance with different AFC numbers.  
As can be seen from Figure 10, even without using all codewords for training, the trained decoding algorithm could still decode codewords that had not been seen before. This indicates that the decoding algorithm learned the rules of codewords rather than simply memorizing the correspondence of codewords. On the other hand, as the value of \(r_{d}\) increased, the performance of the decoding network also improved. This is because the decoding algorithm recognized a more diverse combination of codewords and noise in the decoding process. The generalization and robustness of the denoiser and decoder were improved.
## 3 Results and Discussion
### Determination of the Frost-Heave Crack Propagation Law
#### 3.1.3 Mechanism of Frost-Heave Crack Propagation  
The experimental results indicate that the effect of the frost-heave force produced by the water-ice phase transition in cracks should not be ignored. These results were combined with the experimental results of the frost-heave crack propagation process. The general mechanism of frost-heave damage to the slab-track with water cracking can be summarized as follows: a micro-frost-heave force is produced at the crack tip at the initial freezing stage. This force is balanced with temperature stress for a period of time. With decreasing temperature, an ice plug is formed at the crack opening. The frost-heave water pressure abruptly increases in the crack, breaking the balance between the frost-heave force and temperature stress. When the frost-heave force reaches the crack initiation threshold of concrete, the generalized strain abruptly increases around the crack tip. The crack propagates and then the crack volume increases, resulting in the release of the frost-heave water pressure in cracks. With the further decrease of temperature, the water-ice phase transition is completed. Therefore, the main cause of frost-heave damage to the slab-track with cracked water is the frost-heave force caused by the water-ice phase transition in cracks.  
Figure 8: Surface strain distribution during crack frost-heave propagation. (**a**) The process of temperature equilibrium, stage 1; (**b**) micro-crack formation, stage 2; (**c**) obvious crack, stage t3; (**d**) freezing force release, stage 4; (**e**) frost have completed, stage 5.
## 3 Pre-deployed Keys and Cryptographic Notations
 &
\begin{tabular}{c} none \\ none * \(T\): a trusted node between the initiator node \(I\) and the responder node \(R\) (\(T\) shares a session key with \(I\) and \(R\)),
* \(n_{A}\): a nonce generated by node \(A\),
* \(\{x\}_{k}\): the encryption of message \(x\) with the symmetric or asymmetric key \(k\),
* \(pk(A)\): the public key of node \(A\),
* \(sk(A)\): the secret (private) key of node \(A\),
* \(K(I,S)\): the session key between \(I\) and \(S\),
* \(K_{DH}(N,S)\): the shared symmetric key between \(N\) and \(S\) using the Diffie-Hellman key exchange without interaction described above. In the next section, we recall two protocols that allow a node to join a multi-hop WSN in a secure way and establish secure links with the sink and a neighbour node. A new node is able to establish a secure link with the nearest node that is already part of the network. Then in the following sections, we propose six other protocols that allow any node to establish secure links with any another node in its multi-hop neighbourhood. Two protocols use the sink for key establishment and four protocols establish new keys without resort to the sink. In all figures that describe our protocol, we denote a direct communication by an arrow between two nodes, and denote a communication passing by several possible intermediate nodes by a dotted arrow. We also explicate the size of each exchanged message in Bytes.