esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Pipeline Extraction  
The pipeline extraction method refers primarily to the extraction of entity recognition, followed by relationship extraction. The early pipeline extraction learning methods included CNN and RNN models. The properties of CNN diversity convolutional kernels are beneficial for identifying the structural features of a target [12]. The architecture of CNN models is relatively simple, mainly including a convolutional layer in the front and a fully connected layer behind, and they can be trained relatively quickly. RNNs are neural networks used to process sequence data. RNNs have both internal feedback connections and feedforward connections between the processing units and can use their internal memory to process the sequence information of arbitrary timing, with the ability to learn the combined vector representations of various phrases and sentences of arbitrary length. Compared to general neural networks, they can handle sequence-changing data. However, RNNs also have some limitations, such as gradient disappearance and explosion, as well as long network training cycles; therefore, traditional RNNs struggle to handle long-term dependence in practice. With the continuous development of deep learning, relationship extraction methods based on CNNs and RNNs have been studied, and many variants have been produced. These include dual convolutional neural network (dual CNN), convolutional attention network (CAN), long short-term memory (LSTM), and bi-directional long short-term memory network (Bi-LSTM) models. Additionally, there are variants of graph neural networks (GNNs), graph convolutional networks (GCNs), and graph attention networks (GATs), such as graph convolutional neural network-based feature combinations (FC-GCNs) and graph convolutional network-based attention (AGCN), which are also being gradually used for entity relationship extraction. In addition, researchers are also committed to developing the optimal combination of different models and extraction experiments using existing knowledge embeddings to continuously improve the model effect.
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
###### Abstract  
Low-power ambient backscatter communication (AmBC) relying on radio-frequency (RF) energy harvesting is an energy-efficient solution for batteryless Internet of Things (IoT). However, ambient backscatter signals are severely faded by dyadic backscatter channel (DBC), limiting connectivity in conventional orthogonal time-division-based AmBC (TD-AmBC). In order to support massive connectivity in AmBC, we propose sparse-coded AmBC (SC-AmBC) based on non-orthogonal signaling. Sparse code utilizes inherent sparsity of AmBC where power supplies of RF tags rely on ambient RF energy harvesting. Consequently, sparse-coded backscatter modulation algorithm (SC-BMA) can enable non-orthogonal multiple access (NOMA) as well as \(M\)-ary modulation for concurrent backscatter transmissions, providing additional diversity gain. These sparse codewords from multiple tags can be efficiently detected at access point (AP) using iterative message passing algorithm (MPA). To overcome DBC along with intersymbol interference (ISI), we propose dyadic channel estimation algorithm (D-CEA) and dyadic MPA (D-MPA) exploiting weighted-sum of the ISI for information exchange in the factor graph. Simulation results validate the potential of the SC-AmBC in terms of connectivity, detection performance and sum throughput.  
RF energy harvesting; ambient backscatter communication (AmBC); sparse code multiple access (SCMA); dyadic backscatter channel (DBC); iterative decoder +
Footnote †: journal:  
_Article_
## 3 Proposed Uplink NOMA
### Comparison with Conventional Scheme  
With \(x>0\), \(Q(x)\) can be expressed as:  
\[\begin{array}{l}Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}\exp\Big{(}-\frac {1}{2}u^{2}\Big{)}du\\ =\frac{1}{2}-\frac{1}{\sqrt{2\pi}}\int_{0}^{x}\exp\Big{(}-\frac{1}{2}u^{2} \Big{)}du\end{array} \tag{26}\]  
and thus the second derivative of \(Q(x)\) can be written as follows:  
\[Q^{\prime\prime}(x)=\frac{1}{\sqrt{2\pi}}x\exp\left(-\frac{1}{2}x^{2}\right)> 0(x>0) \tag{27}\]  
Since \(Q(x)\) is a convex function for \(x>0\) from Equation (27), Equation (16) can be written as:  
\[\begin{array}{l}p_{conventional}(\Delta\theta_{m})\\ =\frac{1}{2}Q\Big{(}\sqrt{\gamma_{perfect}}(\cos(\Delta\theta_{m})-\sin(\Delta \theta_{m}))\Big{)}+\frac{1}{2}Q\Big{(}\sqrt{\gamma_{perfect}}(\cos(\Delta \theta_{m})+\sin(\Delta\theta_{m}))\Big{)}\\ \geq Q\Big{(}\sqrt{\gamma_{perfect}}\cos(\Delta\theta_{m})\Big{)}=p_{proposed}^{ (\eta=1)}(\Delta\theta_{m})\end{array} \tag{28}\]  
by the Jensen Inequality [22]. When the channel estimation phase error \(\Delta\theta_{m}\) is \(0\) or \(\pi/2\), \(p_{proposed}^{(\eta=1)}(\Delta\theta_{m})\) is the same as \(p_{conventional}(\Delta\theta_{m})\). But for \(0<\Delta\theta_{m}<\pi/2\), the proposed scheme can achieve less BER than the conventional scheme, meaning that the proposed scheme is more robust to channel estimation errors. When multiple packets are superimposed onto the single resource, there can be severe channel estimation errors due to the interference among the devices. In this case, the proposed NOMA scheme can reduce the BER before channel decoding especially when random-like spreading patterns are used and thus \(\eta\) is \(1\) or close to \(1\). On the other hand, if the complex-conjugate operations significantly change the characteristics of spreading patterns and \(\eta\) becomes considerably smaller than 1, then there are two adversary effects and it is not easy to predict which is better.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 4 Advantages and Limitations
### Limitations  
Despite the encouraging results, our study is not without its limitations. These limitations provide valuable insights for the future direction of research in the domain of predictive water-quality modeling.  
1. Dependence on Quality of Data: Machine learning models are as good as the data they are trained on. In our study, the data spanning two decades was subjected to various preprocessing steps to handle inconsistencies, errors, or gaps. However, even the min-utest oversight in this process could affect the model's performance. While we were meticulous in handling missing values and outliers, potential anomalies or measurement errors inherent in the data could still influence the model results. Future work could involve more sophisticated data-cleaning techniques and the possible incorporation of error-correction methods to further improve model performance [12; 36; 85].
2. Model Complexity: The Random Forest model, although it yielded the best results in our study, is known for its complexity. This complexity can make the model computationally intensive and difficult to interpret, particularly in terms of understanding the relationships between different features. Future research could explore methods of simplifying the model without significant loss of accuracy, or look into implementing more computationally efficient machine learning algorithms that maintain robust predictive power [51; 75; 86].
3. Limited Generalizability: The model has been trained and tested on data from Sloneczne Lake, a specific urban lake. Consequently, its applicability to other water bodies, especially those with significantly different ecological, geological, or anthropogenic conditions, may be limited. Future studies should focus on testing and refining the model using data from various other urban lakes to improve its generalizability. This could also provide more comprehensive insights into universal and location-specific factors affecting urban water quality [87; 88; 82].
4. Need for Constant Updating: The water quality patterns can change over time due to a plethora of factors such as climate change, urbanization, changes in land use, and technological advancements in pollution control. Consequently, the model should be updated regularly with new data to maintain its predictive accuracy. This poses an ongoing challenge and highlights the need for continuous data collection and monitoring efforts [89; 90; 91].  
This honest evaluation of the study's strengths and weaknesses not only enhances its credibility but also provides a foundation for future research aimed at addressing the identified limitations.
# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 5 Discussion
### Detection Models
#### 5.1.2 Trends and Future Research  
The following are potential future trends in related research:  
* Low-cost Solution for Comparative Resilience to LiDAR An increasing number of studies are exploring multi-camera 3D detection combined with RADAR as an affordable alternative to LiDAR. RADAR provides robust performance under varying lighting conditions and is effective for depth measurement,while cameras deliver high resolution and rich semantic information. Additionally, RADAR demonstrates strong weather robustness, velocity estimation, and long-range detection, offering several advantages over LiDAR [68]. Although RADAR has limitations, such as sparsity, inaccurate measurements, and ambiguity, researchers have proposed RADAR noise filtering modules to enhance detection accuracy [150,151]. Notably, CRAFT [68] achieves a 41.1% mAP on the nuScenes test dataset, with 46.2% AP for pedestrians and 31.0% AP for bicycles. However, none of the existing methods have been specifically tailored for VRU detection. As a result, roadside VRU detection using camera and RADAR fusion remains largely unexplored, representing a promising area for future research.
* Unified Generalist Model Recently, some researchers have attempted to develop unified detection models that integrate multiple modalities and domains to enhance robustness across various settings. For example, MMPedestron [129] fused data from five modalities across domains, ranging from traffic surveillance to robotics. As a result, it outperforms existing state-of-the-art models on individual datasets. In this context, variations in roadside data, such as tilt angle, mounting height, and resolution, may not pose limitations for a unified model trained with diverse data.
* Tracking by Detection Recently, a new trend has been combining the detection and tracking module as an end-to-end method to solve the detection and tracking at the same time, which solves VRU occlusion issues while saving the training process and avoiding the complex data association process.
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 5 Performance Evaluation
### RTT Fairness and Access Link Scenario
Conversely, BBRv2 and BBRv3 offered improved queue stability, and periodic buffer emptying demonstrated the synchronization of flows during the PROBE RTT phases, thus accurately estimating the minimum RTT values of their respective paths. BBRv3 with a buffer size of 2*BDP maintained a stable queue at approximately one-fourth of its nominal size following the initial STARTUP phase, which is the expected and desired behavior. However, for a buffer size of 10*BDP, after the STARTUP and DRAIN phases, the remaining queue was too long. Subsequent PROBE UP phases aimed at probing for more bandwidth resulted in the buffer remaining persistently full, thereby impairing the effectiveness of the PROBE RTT phase and the overall algorithm operation. Figure 11: The queue length for all BBR versions in access Link scenario, buffer size = 10*BDP. Figure 10: The queue length for all BBR versions in access link scenario; buffer size = 2*BDP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Buffer Size** & **0.5*BDP** & **BDP** & **2*BDP** & **10*BDP** \\ \hline
**RTT [ms] 1 | II** & 0.70 & 0.62 & 0.81 & 0.86 \\ \hline
8 & 18 & 20 & 24 & 10 \\
32 & 10 & 10 & 11 & 13 \\
58 & 2 & 6 & 11 & 18 \\
78 & 23 & 2 & 9 & 12 \\
102 & 2 & 3 & 10 & 12 \\
128 & 3 & 21 & 8 & 10 \\
154 & 12 & 23 & 8 & 5 \\
178 & 11 & 3 & 9 & 3 \\
204 & 12 & 5 & 5 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Throughput of BBRv3 flows [Mbps] and overall Jain’s index (JI) for access link scenario. BBRv3's performance in deep buffers should be further investigated. It is conceivable that the issue stems from a simplified simulation model; however, in [18], we can observe that in very deep buffers, the fairness provided by BBRv3 dropped, which may confirm the problem of BBRv3 depleting the deep queue. Summary: In scenarios featuring multiple paths with varying RTT times, BBRv3 maintained a stable and low queue when the available buffer size was equal to 2*BDP. Unfortunately, the protocol fell short in ensuring equitable bandwidth distribution among active flows, thus leading to a fairness lower than that of the initial iteration.
## 3 Results
The higher likelihood of normal PAL at baseline (baseline PAQA score above 2.71) was evidenced for urban adolescents (Model 0: OR: 1.41, 95%CI: 1.23-1.87), adolescents who were taller (Model 0: OR = 1.54, 95% CI: 1.21-1.87; Model 1: OR: 1.65, 95% CI: 1.11-1.98), heavier (Model 0: OR = 1.34, 95% CI: 1.01-1.45), who had better aerobic endurance (Model 0: OR = 1.67, 95% CI: 1.44-1.95; Model 1: OR: 1.61, 95% CI: 1.20-2.01), anaerobic endurance (Model 0: OR = 0.61, 95% CI: 0.41-0.89; Model 1: OR: 0.71, 95% CI: 0.56-0.89), static strength (Model 0: OR = 2.01, 95% CI: 1.45-2.57; Model 1: OR: 1.97, 95% CI: 1.31-1.2.41), and dynamic strength (Model 0: OR = 1.87, 95% CI: 1.11-2.44; Model 1: OR: 1.88, 95% CI: 1.10-2.50) (Figure 3). Dynamic strength (measured by sit-ups; OR: 1.44, 95% CI: 1.22-1.68), aerobic endurance (measured by multi-level-test; OR: 1.64, 95% CI: 1.10-2.24), and anaerobic endurance measured by the 400-m run test (OR: 0.79, 95% CI: 0.60-0.980) were positively correlated with a normal-PAL at follow-up in the logistic regression model non-controlled for the urban/rural environment. However, when the urban/rural environment was included in the logistic regression calculation as a covariate (Model 1), no significant correlation between predictors and PAL was evidenced (Figure 4). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total** & **Rural** & **Urban** \\  & **(_n_ = 823_)** & **(_n_ = 381_)** & **(_n_ = 442_)** \\ \hline Physical activity difference between & & & \\ baseline and follow-up & & & \\ Body height & 0.04 & 0.13 * & 0.20 * \\ Body mass & 0.05 & 0.13 * & 0.23 * \\ Body mass index & 0.10 * & 0.09 & 0.18 * \\ Broad jump & 0.10 * & 0.17 * & 0.07 \\ Sit-ups & 0.05 & 0.08 & 0.03 \\ Sit-and-reach & \(-\)0.05 & \(-\)0.11 * & 0.00 \\ Bent-arm-hang & 0.16 * & 0.11 * & 0.12 * \\ Run-400 m & \(-\)0.04 & \(-\)0.10 * & \(-\)0.04 \\ Multi-level-test & 0.28 * & 0.25 * & 0.20 * \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 3: Predictors of normal physical activity levels at baseline (model 0 – crude logistic regression, model 1 – logistic regression controlled for urban/rural environment as confounding factor).
## 4 Discussion  
As the electromagnetic properties of Mie-resonance based-metamaterials can be tailored by the shape and size of the dielectric cubes, the EMW absorbing concrete can be designed to work at different frequencies. The simulated S11 and S21 in dB of the designed structure with \"a\" ranging from 2.4 mm to 2.6 mm and the dielectric constant \(\varepsilon\) of the dielectric cubes ranging from 146 to 150 is shown in Figure 5. Further, the calculated absorptivity is shown in Figure 6.  
Figure 4: Simulated results of the distribution and energy density of electric field and magnetic field at 9.82 GHz: (**a**) E-field distribution; (**b**) E-field energy density; (**c**) M-field distribution; and (**d**) M-field energy density.  
Figure 5: (**a**) simulated S11 and S21 spectra when a = 2.40–2.60 mm, \(\varepsilon=150\); and (**b**) simulated S11 and S21 spectra when a = 2.50 mm, \(\varepsilon=146\)–150.  
Figure 6: (**a**) simulated absorptivity spectra when a = 2.40–2.60 mm, \(\varepsilon=150\); and (**b**) simulated absorptivity spectra when a = 2.50 mm, \(\varepsilon=146\)–150.  
The results indicate that the working frequency shifts with the dimensions of the structure and the permittivity of the dielectric cubes. The reflectivity reaches \(-15\) dB, the transmission is low in the study frequency range, and the absorptivity is higher than \(90\%\). According to these results, metamaterials based EMW absorbing concrete can be designed to work at different frequencies to work in different environments.  
In addition, the recent literature on metamaterial absorbers and EMW absorbing concrete was surveyed. Further, four representative papers were selected to compare with our work, which are shown in Table 2. The results in Table 2 indicate that the proposed metamaterial-based EMW absorbing concrete has high absorptivity and a simple structure.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 2 Related Work
### Object-Tracking Algorithms  
Object-tracking algorithms play an important role in warehouses. Object-tracking technology offers a valuable way for warehouse managers to ascertain the location and status of items in the warehouses more conveniently, improving operational efficiency. Object-tracking technology could be implemented in various ways. For instance, [PERSON] [13] combined the Industrial Internet of Things (IIoT) and Digital Twin (DT) for the unsupervised management of cold-chain logistics warehouses, ensuring occupational safety  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **[EMAIL_ADDRESS]/percent** & **Delay Time/ms** \\ \hline YOLOv3-320 & 51.5 & 22 \\ YOLOv5s & 55.4 & 2 \\ DSSD513 & 53.3 & 156 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the three detection algorithms on the COCO.  
Figure 2: YOLO algorithm process.  
and health (OSH). These technologies provide higher efficiency and a foundation for the development of unmanned warehouses. However, a convenient and cost-effective means of object tracking is needed that also strikes a balance with practical applications. Therefore, object-tracking technology based on image object detection has gained attention [14]. DeepSORT is widely used for tracking people [15] and ordinary items [16], as a classic technology for target tracking. Previous studies have effectively harnessed the DeepSORT algorithm combined with other technologies to solve indoor object-tracking problems. For instance, [PERSON] [17] designed a lightweight indoor object-tracking method by introducing DeepSORT in scenarios with partially overlapping fields of view (FOVs). Therefore, this article harnesses the potential of DeepSORT to design an object-tracking method for use within logistics warehouses.
## 4 Results and Analysis
### Verification of the Numerical Simulation Approach
#### 4.2.1 The Distribution and Evolution Characteristics of Acoustic Source  
Figure 6 presents the acoustic source distribution characteristic. Basically, the acoustic source is less than 0. The regions near the outlet of impeller, inlet of volute and volute tongue have lower acoustic source than any others, and the minimum acoustic source is located near the tongue. In addition, the concentration, break, extend, migration and reaggregation evolution phenomena of the minimum acoustic source region exist during the fluid motion. Specifically, when the blade is located at the tongue (Time step = 10), the blade-volute tongue interaction intensity is the strongest, the minimum acoustic source region concentrates on the tongue. Subsequently, the minimum acoustic source region initially concentrated on the tongue breaks, then extends and migrates downstream slightly with the blade away from the tongue (Time step = 20). As the blade continues to move, another blade moves close to the tongue (Time step = 30), the minimum acoustic source region aggregates again near the tongue.  
Figure 6: _Cont._  
According to Powell vortex sound equation, the acoustic source consists of two different components, which represents respectively the stretching of the vortex caused by change of velocity (Source-1) and the non-uniformity of fluid kinetic energy (Source-2) [32]. The standard deviation (STD) is calculated to explore the fluctuation intensity of the acoustic source and the proportion of different source components, and it is defined as.  
\[STD=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(S_{i}-\overline{S}\right)^{2}}{n}} \tag{7}\]  
\[\overline{S}=\frac{1}{n}{\sum\limits_{i=
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 5 Power Optimization  
Power optimization involves assigning the optimal power to the users by the access points. The symbol \(\rho_{max}\) represents the maximum transmit power of the access point. The user distance or channel conditions are used by the AP to determine the power allocation to different users. The downlink power coefficients \(\rho_{CU}\) are defined as \([\rho_{CU_{1}},\rho_{CU_{2}}\cdots\rho_{CU_{n}}]^{T}\). The power allocated to the \(n\)th cellular user by all the serving APs is represented by \(\rho_{CU_{n}}\). The SIR estimated in the downlink is dependent on \(\rho_{CU}\) and can be represented as follows, based on various power allocation [27] methods:  
* Equal power allocation: The equal power allocation scheme assigns the same power to all the users served by an access point. Each AP can serve a maximum of \(l_{x}\) users, and hence, the power for each user is assigned as follows: \[\rho_{CU_{n}}=\frac{\rho_{max}}{l_{x}}\] (15)
* Fractional power allocation: The fraction of power is allocated to users within a serving AP and is proportional to the channel gains. \[\rho_{CU_{n}}=\rho_{max}\frac{(\sum_{aeA_{n}}\beta_{ua})^{\mu}}{max_{ic_{i}Cu }(\sum_{aeA_{i}}\beta_{ia})^{\mu}}\] (16) where the channel gains are \(\beta_{ua}\); a 3 GPP urban microcell large-scale fading model [37] is given as \[\beta_{ua}[\text{dB}]=-30.5-36.7 log_{10}\frac{d_{ua}}{1\text{ m}}+F_{ua}\] (17) Here, \(F_{ua}\) represents shadow fading, and \(d_{ua}\) is the distance between UE \(u\) and access point \(a\). \(\mu\) is a variable that influences the power allocation behavior.
* GA-based power allocation: The goal of the design is to reduce the total power used by the users while still meeting their required SIR. This design provides a flexible relaying strategy that can satisfy each user's needs and achieve the desired quality of service. The problem of minimizing the total power at the UEs while meeting their desired SIR targets is stated as \[min\sum_{i=1}^{n}\rho_{CU_{i}}\] (18) The power at each user equipment with optimized weighting can be represented as \[\rho_{CU_{i}}=w_{i}\rho_{i}\] (19)The goal is to find the best values of the weights, \(w_{i}\), that minimize the total power in a network with different types of users, by using the power amplification factor, \(\rho_{i}\), at each UE, subject to \(SIR_{CU_{i}}>SIR_{T}\), where \(SIR_{T}\) is the \(SIR\) threshold at the UE.