esa-sceva/satcom-chunk-collection huggingface dataset:
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 3 Review on Optimization Methods under Continuous Phase Shift
### MM Method  
Both the SDR method and the penalty method require a complexity of at least \(\mathcal{O}(M^{3})\). To reduce the computational complexity, the MM method can be employed to tackle the unit-modulus constraint. The key idea lies in constructing a sequence of surrogate functions that serve as upper bounds of the cost function with respect to the unknown variable \(\mathbf{e}\). Figure 4a visualizes how a linear surrogate function \(g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r)})\) upper bounds a convex quadratic function \(f\Big{(}\mathbf{x}^{(n)},\mathbf{e}\Big{)}\) on the unit circle at the \(r^{th}\) iteration.  
Specifically, given the solution for \(\mathbf{e}\) at the \(r^{th}\) iteration as \(\mathbf{e}^{(r)}\) (the red point in Figure 4), the constructed linear surrogate function needs to satisfy: (a) \(g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r)})\geq f\Big{(}\mathbf{x}^{(n)}, \mathbf{e}\Big{)}\) on the unit circle manifold; (b) \(g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r)})=f\Big{(}\mathbf{x}^{(n)}, \mathbf{e}\Big{)}\) at \(\mathbf{e}^{(r)}\); and (c) \(\
abla_{\mathbf{e}}f\Big{(}\mathbf{x}^{(n)},\mathbf{e}\Big{)}=\
abla_{\mathbf{ e}}g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r)})\) at point \(\mathbf{e}^{(r)}\). In practice, the second-order Taylor expansion and Jensen's inequality are commonly used to find \(g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r)})\)[13].  
With the established upper bound \(g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r)})\), problem (5) under C1 can be iteratively solved with the subproblem at the \((r+1)^{th}\) iteration being  
\[\underset{\mathbf{e}}{\text{min}}\ \ g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r )}),\ \ \ \text{s.t.}\ \ |e_{m}|^{2}=1,\ \ \ \forall m. \tag{8}\]  
Since \(g(\mathbf{x}^{(n)},\mathbf{e}|\mathbf{e}^{(r)})\) is a linear surrogate function, it has a closed-form minimizer \(\mathbf{q}_{\mathbf{e}^{(r)}}\). Then, we can project \(\mathbf{q}_{\mathbf{e}^{(r)}}\) onto the unit circle manifold to obtain \(\mathbf{e}^{(r+1)}\). The next iteration involves finding \(\mathbf{q}_{\mathbf{e}^{(r+1)}}\) based on \(\mathbf{e}^{(r+1)}\), and the process repeats. Therefore, problem (5) can be iteratively solved, and the final converged point is a local optimal point of problem (5) [13]. The computational complexity of the MM method is dominated by the determination of surrogate functions, which gives a complexity order of \(\mathcal{O}(M^{2})\).
###### Abstract  
Local differential privacy (LDP) protects user information from potential threats by randomizing data on individual devices before transmission to untrusted collectors. This method enables collectors to derive user statistics by analyzing randomized data, thereby presenting a promising avenue for privacy-preserving data collection. In the context of key-value data, in which discrete and continuous values coexist, PrivKV has been introduced as an LDP protocol to ensure secure collection. However, this framework is susceptible to poisoning attacks. To address this vulnerability, we propose an expectation maximization (EM)-based algorithm combined with a cryptographic protocol to facilitate secure random sampling. Our LDP protocol, known as emPrivKV, exhibits two key advantages: it improves the accuracy of statistical information estimation from randomized data, and enhances resilience against the manipulation of statistics, that is, poisoning attacks. These attacks involve malicious users manipulating the analysis results without detection. This study presents the empirical results of applying the emPrivKV protocol to both synthetic and open datasets, highlighting a notable improvement in the precision of statistical value estimation and robustness against poisoning attacks. As a result, emPrivKV improved the frequency and the mean gains by 17.1% and 25.9%, respectively, compared to PrivKV, with the number of fake users being 0.1 of the genuine users. Our findings contribute to the ongoing discourse on refining LDP protocols for key-value data in scenarios involving privacy-sensitive information.  
LDP; key-value data; expectation maximization Article Robust Estimation Method against Poisoning Key-Value Data with Local Differential Privacy.
# Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT
## 2 Communications: NB-IoT
We extend the use of IoT technology toward prosumers, providing an alternative solution for future distribution system scenarios in which a high renewable penetration would lead to higher peaks of generation as a consequence of potential reverse power flows at the medium voltage/low voltage (MV/LV) distribution transformers [48]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Schemes** & **Technologies** & **Data Transfer Rate** & **Typical Coverage Range** & **Latency** \\ \hline \multirow{3}{*}{HAN} & Ethernet, PLC, Zigbee, WLAN, Z-Wave & 10–100 kbps & up to 500 m & 10 ms–1 s \\ \hline \multirow{2}{*}{BAN/IAN} & Ethernet, PLC, Wimax, WLAN & 100 kbps–1 Mbps & up to 1 km & 10 ms–2 s \\ \hline \multirow{3}{*}{NAN} & Ethernet, PLC, DSL, Fiber–Optics, WiMax, NB–IoT, LoRa & 100 kbps–10 Mbps & 0.1–10 km & 10–50 ms \\ \hline \multirow{3}{*}{WAN} & PLC, Ethernet, Fiber–Optics, LoRa, WiMax & 10 Mbps–1 Gbps & 10–100 km & 10 μs–20 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network schemes and communication technologies properties.
## 3 UAV Deployment and Trajectory
### A Static UAV  
Single or multiple UAVs can be deployed as a BS or a RS in UAV wireless networks. In these use cases, static UAVs are efficient to reduce the energy consumption and to provide the seamless wireless services on a given area or to user groups. Multiple UAV networks have the advantage over single UAV networks. If single UAV networks experience a link interruption, the whole networks become unavailable. However, multiple UAV networks are resilient to the link disruption because they can reconstruct the networks and find other routes to deliver information to the destination. In addition, multiple UAV networks based on mesh networks are capable of self-reorganization to improve the reliability of the whole networks [7]. If they do not fly over a wide area, multiple UAVs are assumed to be static even though re-positioning or self-reorganization during the operation is performed. In this section, we review the literature on the deployment of UAVs for both single and multiple UAV networks.
## 3 The Proposed CTHE Method  
### The Overall Method  
To overcome the shortcomings of the existing triples extraction methods, we proposed an open domain Chinese triples extraction method. This method is a hierarchical framework, which combines five modules to achieve the triples extraction task with open relations.  
* Named Entity Recognition Module: this module aims to extract all named entities from the input original sentence, and combine some simple rules to form all the entity pairs that may have a relation.
* Schema Match Module: this module aims to use a specific schema library to filter entity pairs for a specific pattern.
* BERT-based Att-Bi-GRU Specific Relation Prediction Module: this module aims to apply the BERT-based Att-Bi-GRU model to finish the prediction of all the specific relations.
* Relation Check and Confidence Discrimination Module: this module aims to check unknown relation and automatically determine the confidence of the predicted result by the BERT-based Att-Bi-GRU model.
* Open Relation Prediction Module: this module aims to achieve entity relation prediction under non-specific, unknown classification, and low-confidence specific relation based on traditional sentence semantic dependency parse.  
By combining named entity recognition, supervised learning methods with relation classification, and unsupervised learning methods with relation extraction, we built an integrated open domain Chinese triples hierarchical extraction framework. The overall framework of our model is shown in Figure 1. It is worth noting that all modules in the entire system are not independent, and they have a strict logical flow between input and output.  
Figure 1: The framework of open domain Chinese triples hierarchical extraction method.
## 3 Results and Discussion
### Characteristics of Liquid Digestate and Slurry When Used for Ma-B Flocs Growth
#### 3.2.2 Phytoremediation of Liquid Digestate and Slurry: COD and Ammonium Removal
Figure 2 shows that the lowest removal efficiencies were observed on the initial samples of DL\({}_{0}\) (14.8 \(\pm\) 2.9%) and SL\({}_{0}\) (20.0 \(\pm\) 1.3%), which is believed to be due to their high COD content (1184 mg\(\cdot\)L\({}^{-1}\) and 1337 mg\(\cdot\)L\({}^{-1}\), respectively (Table 3)) resulting in toxicity to Ma-B flocs. On the other hand, high COD removal efficiencies were observed for SL\({}_{10\%}\) and SL\({}_{30\%}\) diluted slurry cases: Approximately 83.1 \(\pm\) 6.0% and 82.3 \(\pm\) 1.0%, respectively, and are higher than the control efficiency with synthetic wastewater (approximately 75.7 \(\pm\) 2.0%). The highest value for the digestate trials was, at best, approximately 37.7 \(\pm\) 4.0% for D\({}_{1,30\%}\) and is very low compared to the control yield with synthetic wastewater (approximately 75.7 \(\pm\) 2.0%). For Ma-B flocs, highly diluted liquid slurry appears to represent a suitable growth medium, although COD concentrations are lower than those of the reference synthetic wastewater (SWW). The better results obtained with liquid slurry effluent can be explained as follows: The slurry collected from the farm had a less dense liquid consistency than the digestate. This aspect could affect the removal performance because the nutrients would be easier to assimilate by the Ma-B flocs, and therefore the release of CO\({}_{2}\) (by heterotrophic bacteria), which is essential for the growth of microalgae,  
Figure 1: Ammonium removal rates (mean value \(\pm\) SD) evaluated after 2 and 14 days of Ma-B flocs culture for each diluted substrate (**a**) DL: Liquid Digestate; (**b**) SL: Liquid Slurry. WWS is a synthetic wastewater control. is increased [25,26]. On the other hand, the less intense brown coloration of the slurry favors better photosynthetic growth of microalgae. In the literature, filtration and/or centrifugation steps, in addition to dilution, are often recommended to reduce the color intensity of this kind of effluent [5,17]. This could be judicious in the case of our digestate.
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
###### Abstract  
Structured light 3D shape metrology has become a very important technique and one of the hot research topics in 3D face recognition. However, it is still very challenging to use the digital light projector (DLP) in a 3D scanner and achieve high-speed, low-cost, small-size, and infrared-illuminated measurements. Instead of using a DLP, this paper proposes to use a galvanometer scanner to project phase-shifted fringes with a projection speed of infrared fringes up to 500 fps. Moreover, the measurement accuracy of multi-frequency (hierarchical) and multi-wavelength (heterodyne) temporal phase unwrapping approaches implemented in this system is analyzed. The measurement accuracy of the two methods is better than 0.2 mm. Comparisons are made between this method and the classical DLP approach. This method can achieve a similar accuracy and repeatability compared to the classical DLP method when a face mask is measured. The experiments on real human face indicate that this proposed method can improve the field of 3D scanning applications at a lower cost.  
optical 3D measurement; infrared fringe projection; galvanometer scanner; 3D face recognition; low-cost projector +
Footnote †: journal: Applied Physics Letters
###### Abstract  
To solve the problem of fatigue failure in fillet welds of traction rod brackets on locomotive bogies of a given model, the cause for failure and the improvement method were studied. The results show that when there is maximum clearance at weld roots, maximum incomplete fusion of sidewalls, maximum incomplete fusion at weld roots, and maximum pores allowable in the ISO 5817 standard, the stress amplitude separately increases by 70-97%, 53-55%, 40-46%, and 19-34%. Despite this, when various types of defects of the maximum size are present in the weld alone, the static and fatigue strengths of fillet welds with a throat depth of 6 mm on the traction rod bracket can still meet the requirements in the UIC615-4 standard. In practical fillet welds, defects including clearance at weld roots, incomplete fusion, and pores are very likely to occur at the same time, which may induce fatigue failure in fillet welds of traction rod brackets within the original design life. According to the size of the frame and the traction rod brackets, a strengthening scheme for increasing the throat depth of fillet welds of traction rod brackets to 8 mm was designed. Calculation results of the strengthening scheme show that for new structures subjected to overall post-veld stress-relief thermal treatments, the maximum stress amplitude decreases by 5-29% when increasing the throat depth of fillet welds from 6 to 8 mm. For structures in service with the throat depth of fillet welds increased from 6 to 8 mm through repair welding, peak residual stress at the weld root after repair welding can reach 383 MPa. Because overall stress-relief thermal treatments cannot be performed on repair-welded structures, the fatigue strength of repair-welded fillet welds cannot meet the requirements of UIC615-4; therefore, local stress-relief treatments have to be performed in the welded zone. The results are of guiding significance for the treatment of locomotives in service and performance improvement of new locomotives and suggest that the current standard is relatively conservative.  
bogies; fillet welds; traction rod brackets; weld defects; repair welding; fatigue strength Article  
Fillet Welds of Traction Rod Brackets on a Locomotive Bogie Based on International Union of Railways Standards and  
Improvement Measures Adopted  
_Appl. Sci._**2023**, 13, 5494. [[https://doi.org/10.3390/app13095494](https://doi.org/10.3390/app13095494)]([https://doi.org/10.3390/app13095494](https://doi.org/10.3390/app13095494))
# The Use of Machine Learning Methods in Road Safety Research in Poland
## 4 Mathematical Modeling of Road Safety in Poland
### RF Classification with Cross-Validation  
Another model is Random Forest with cross-validation. First, the _mtry_ parameter, which defines the number of randomly selected predictors considered at each node split in the random forest trees, was selected. Various _mtry_ values were tested, and the results are shown in Table 7.  
The highest accuracy and Kappa values were achieved for the parameter _mtry_ = 44, and this value was subsequently used in the model. The confusion matrix for Random Forest with cross-validation is presented in Table 8.  
For this model, as for KNN, the value of [PERSON]'s test statistic was very low (\(\chi^{2}\) < 2.2 \(\times\) 10\({}^{-16}\)). Random Forest also makes classification errors in an asymmetric manner. The number of cases in which the model incorrectly classified an example as one of the classes differs significantly from the number of cases in which the model incorrectly classified an example as another class. The accuracy of this model is 63.02%, indicating moderate performance, similar to the previous model, KNN. Similarly, the model performs well for the \"no injuries\" and \"minor injuries\" classes but struggles significantly with the less frequent \"severe injuries\" and \"deaths\" classes.  
The results for each class are as follows:  
1. \"No injuries\": * Sensitivity: 84.95%--the model effectively identifies samples belonging to this class; * Specificity: 79.02%--the model less frequently misclassifies other classes as \"no injuries\"; * Balanced accuracy: 81.98%--demonstrates strong performance for this class.
2. \"Minor injuries\": * Sensitivity: 59.03%--moderate ability to detect this class; * Specificity: 75.03%--the model performs better at excluding other classes; * Balanced accuracy: 67.03%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**mtry** & **Accuracy** & **Kappa** \\ \hline
2 & 0.6185 & 0.3470 \\
44 & 0.6335 & 0.4195 \\
86 & 0.6229 & 0.4062 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results for different _mtry_ values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual Class** & **No Injuries** & **Minor Injuries** & **Severe Injuries** & **Deaths** \\ \hline No injuries & 2991 & 650 & 209 & 71 \\ Minor injuries & 421 & 1661 & 714 & 148 \\ Severe injuries & 88 & 444 & 319 & 77 \\ Deaths & 21 & 59 & 39 & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrix for the Random Forest model with cross-validation for _mtry_ = 44.  
3. \"Severe injuries\": * Sensitivity: 24.90%--the model struggles to correctly identify this class; * Specificity: 90.87%--the majority of other classes are not misclassified as \"severe injuries\"; * Balanced accuracy: 57.88%.
4. \"Deaths\": * Sensitivity: 12.17%--very low sensitivity indicates that the model hardly detects this class; * Specificity: 98.44%--the model rarely misclassifies other classes as \"deaths\"; * Balanced accuracy: 55.30%.
# IoT Services for Monitoring Food Supply Chains
## 2 Literature Review
### Advanced IoT Architectures and Smart Sensor Technologies
NB-IoT (Narrowband IoT) is a cellular-based technology that offers deep coverage and low power consumption, operating in licensed spectrum and providing speeds of up to 250 kbps with a range that extends to underground or remote areas, making it ideal for innovative city applications,utilities, and agricultural monitoring. Each technology offers a trade-off between range, speed, and power consumption to meet specific use-case requirements. The diversity of sensors available is contingent upon the underlying network architecture and connectivity protocols, each exhibiting distinct characteristics suitable for various applications. An initial categorisation of these sensors includes the following: a. Passive and Active, b. Analogue and Digital, as well as Scalar and Vector types. It is evident that each category possesses unique attributes; however, collectively, they encompass a wide array of static and dynamic characteristics. Active sensors function as detecting devices that necessitate an external power source; conversely, passive sensors solely detect and respond to specific inputs from the physical environment. Both active and passive sensing technologies play crucial roles in remote sensing applications, enabling observations and measurements at distances or scales that exceed human visual capacity. Furthermore, sensors are particularly valuable in extreme conditions and inaccessible areas where human presence may be impractical or impossible.
## 2 Materials and Methods
### Numerical Modelling
\begin{split} Ez_{(i,j)}^{n+1}=Ez_{(i,j)}^{n}\left[\frac{4e_{(i,j)}^{n}-\Delta t\Big{(}e_{(i,j)}^{n+1}+e_{(i,j)}^{n}\Big{)}}{4e_{(i,j)}^{n+1 }+\Delta t\Big{(}e_{(i,j)}^{n+1}+e_{(i,j)}^{n}\Big{)}}\right]+\left\{\frac{ \Delta t}{e_{(i,j)}^{n+1}+\frac{\Delta t}{4}\Big{(}e_{(i,j)}^{n+1}+e_{(i,j)}^ {n}\Big{)}}\right.\\ \left.\left[\frac{1}{\kappa(i,j)}\frac{Hy_{(i+1/2,j)}^{n-1/2}-Hy_ {(i-1/2,j)}^{n-1/2}}{\Delta x}+\Psi x_{(i,j)}^{n+1}(H_{y})-\frac{1}{\kappa(i, j)}\frac{Hx_{(i,j+1/2)}^{n-1/2}-Hx_{(i,j-1/2)}^{n-1/2}}{\Delta y}+\right.\\ \left.\Psi y_{(i,j)}^{n+1}(H_{x})-\frac{Jz_{(i,j)}^{n+1}+Jz_{(i,j )}^{n}}{2}\right]\right\}\,.\end{split}  \tag{37}\]  
To guide the search of the optimum CPML parameters for this study in order to avoid spurious waves within the domain, we start with the values of the parameters given in [53], which proved to work for electromagnetic propagation in different frequency domains. Such parameter values are shown in Table 1.  
To validate the numerical implementation, the electromagnetic energy flux is evaluated at each time step by calculating the magnitude of the Poynting vector at each mesh centre point as  
\[S_{(i,j)}^{n+1}=\sqrt{\left(Ez_{(i,j)}^{n}Hy_{(i,j)}^{n+1/2}\right)^{2}+\left( Ez_{(i,j)}^{n}Hx_{(i,j)}^{n+1/2}\right)^{2}}\ ; \tag{38}\]  
consequently, the electromagnetic energy flux at time \(n+1\) in the entire domain is given by \[S^{n+1}=\sum_{i,j}S^{n+1}_{(i,j)}. \tag{39}\]
# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
## 2 Related Works
### Discussion  
Overall, the above-mentioned studies did not evaluate the entire network conditions along the flights. In the studies that adopted heterogeneous communications, many assumed pre-existing mission conditions, defining offline which interface will be used for a given purpose, reducing the network's adaptability to unexpected events.  
To construct a method to overcome such issues, maximizing packet delivery and improving the quality of transmissions, which are dynamically necessary requirements to establish wireless networks composed of high-mobility nodes [11; 30], some stacks of protocols present changes in their medium access classes (MAC). These stacks include reduction in the CTS, RTS, and ACK frame generation interval and decreases the size of the packet header, thus reducing the response time in message delivery. An example of this is the stack of the IEEE 802.11p protocol, which serves for communication between vehicles, and provides the alternative of allowing the transmission of messages within a network range based on the geo-positioning, so that the nodes do not need previous authentication in the network [31; 32]. Other alternatives to maintain the reliability of these wireless networks include complementary mechanisms to a communication protocol, which function by managing the assignments of the frequency channels [33], and efficient algorithm systems that optimize the quality of the message exchange and the repositioning schemes of the nodes [34] in order to improve their connection intensity [7; 35].  
Maintaining stable wireless connections between high-mobility mobile devices, such as UAV networks, is a research field facing several challenges due to the mobility, flexibility, and variant topologies that constitute these networks known as FANETs [3]. Some of these static features include, in most cases, limited flight time (as in natural disasters), resistance to interference (for example, channel overlap), and limits imposed by technology on the extent of signal coverage [2; 5].  
Some emergent technology solutions, such as the use of SBAS communication [24], software-defined networks for UAV-to-UAV communications [36], and the use of IoT protocols [37] have produced promising results in these communication scenarios. In terms of algorithms, biological algorithms [38] and dynamic relay nodes selections [39] have provided signal gains and continuous connectivity in search and rescue missions. In this context of emergent solutions, the use of heterogeneous networks solutions can also be useful for addressing the problem of maintaining connectivity between these types of nodes, seeking quality and reliability in the delivery of messages [14; 40].  
The IM presented in this paper can make decisions about which communication interface should be used based on dynamically sensed network metrics. It is not a centralized network architecture, but a decentralized network with an ad hoc topology, so that message exchanges are performed in UAV-to-UAV (U2U) mode.