esa-sceva/satcom-chunk-collection huggingface dataset:
# A Middleware Solution for Wireless IoT Applications in Sparse Smart Cities
## 6 Quorum-Based Distributed Actuation and Resiliency Policy
Sensor nodes spontaneously participating in the leader-election algorithm are willing to behave as controllers, but only for a brief period of time, to limit power consumption or reduce costs to dispatch the packet to the Internet:
* in case a new controller node becomes available, the sensor node elected as time-limited controller becomes again a sensor node only;
* in case an actual controller is not available for a long time period, the current time-limited controller node stops behaving as controller, to push other Internet-enabled sensor nodes to start the leader-election algorithm again;
* regular sensor nodes (not able to behave as time-limited controller nodes) keep looking for a new controller for a while, that is, to wait for the election of a limited-timed controller node. If a new controller is not discovered, they switch from the \"remotely controlled\" to the \"resilience\" state. Whenever in the latter state, they exploit as configuration parameters resilience values (provided via previously received command packets). In fact, since in this case sensor nodes cannot receive command packets from any controller nodes, they cannot discriminate if the active parameter is the same currently set in the cloud application or not. The availability of a resilience parameter issued via controller nodes allows, also in case of network partitioning, to switch to a standard behavior, such as allowing a proper tradeoff between metering frequency and power consumption, also in case of sensor nodes detached from the network. Figure 4 presents Sensor and Controller components, implementing the quorum-based distributed actuation and resiliency policy solution. The Sensor component offers the following API (Figure 4, left):  
* joinApp(appName, listener), allowing to register a listener interested in:
* receiving commands related to a given application;
* being notified of controller unavailability to activate the resilience policy;
* leaveApp(appName), allowing to leave an application and unregister the listener. The Controller component offers the following API (Figure 4, right):
* registerApp(appName), to add a local application;
* removeApp(appName), to remove a local application;
* sendCommand(appName, command, timeToWait, threshold), to send a command to sensor nodes of a given application: it sends a pre-command, waits for here-I-am messages for timeToWait, and finally sends the actual command message only if at least \"threshold\" sensor nodes have replied with here-I-am messages. Pre-command, here-I-am and command messages are dispatched taking advantage of the Multi-hop Networking Overlay Layer, thus transparently achieving multi-hop and opportunistic networking capabilities. As better detailed in Section 8, PresenceSensor and PresenceController components take advantage of the Sensor and Controller ones respectively to transparently register, look for, and join controller nodes, send and receive commands, and notify resilience policy activations in an event-driven fashion.
# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 2 Background
### Nature-Inspired Algorithms for Machine Learning Ensemble Optimization  
The authors of [60] considered the application of ensemble and nature-inspired machine learning algorithms for driver behavior prediction. The three classifiers applied were the Support Vector Machine (SVM) algorithm, the K-Nearest Neighbors (K-NN) algorithm, and the Naive Bayes (NB) algorithm. Initially, the performance of the classifiers was improved using techniques such as voting ensemble, bagging, and boosting. Then, four nature-inspired algorithms were applied to improve the results. These four algorithms were as follows: the PSO algorithm, the GWO, the WOA, and the Ant Lion Optimizer(ALO) [61]. The results showed that the best approach was the GWO-voting approach, which had an accuracy of 97.50%.  
The approach presented in [62] analyzed driver performance considering a learning technique based on weighted ensembles and nature-inspired algorithms. The applied ensemble technique was the Extreme Learning Machine (ELM) algorithm, and the combined classifiers were the Generalized Linear Model (GLM), the K-NN algorithm, and the Linear Discriminant Analysis (LDA) algorithm. The applied nature-inspired algorithms were the GA, the Grasshopper Optimization Algorithm (GOA) [63], and the binary BA. Compared to the other models proposed in that approach, the model that was based on the hybrid nature-inspired ELM algorithm led to the highest performance metrics.  
In [64], the authors applied an ensemble method based on the SVM, the K-NN, and the PSO to improve the accuracy of intrusion detection. The obtained results suggested that the novel approach was better than the Weighted Majority Algorithm (WMA) [65] in terms of accuracy. The approach presented in [66] applied a WOA-based ensemble using the SVM and the K-NN for the classification of the diabetes based on data collected from medical centers from Iran. The WOA ensemble classifier improved the best preceding classifier by about 5%.  
The authors of [67] proposed a method based on an improved version of the PSO combined with the Adaptive Boosting (AdaBoost) [68] ensemble algorithm for the classification of imbalanced data. The experimental results showed that the proposed method was effective in the processing of the data characterized by a high imbalance rate. Another approach, the one considered in [69], proposed an ensemble system based on the modified AdaBoost with area under the curve (M-AdaBoost-A) classifier, which was optimized using strategies such as the PSO. The proposed approach returned performant results both for 802.11 wireless and for traditional enterprise intrusion detection.
# Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods
## Appendix A Solvers for the TR Subproblem
### Computing with an \(L\)-BFGS Matrix
#### Spectral Decomposition of \(B_{k}\)  
By the thin QR factorization of the matrix \(\Psi_{k}\), \(\Psi_{k}=Q_{k}R_{k}\), or the Cholesky factorization of the matrix \(\Psi_{k}^{T}\Psi_{k}\), \(\Psi_{k}^{T}\Psi_{k}=R^{T}R\), and then spectral decomposition of the small matrix \(R_{k}M_{k}R_{k}^{T}\) as \(R_{k}M_{k}R_{k}^{T}=U_{k}\hat{\Lambda}U_{k}^{T}\), we have  
\[B_{k}=B_{0}+Q_{k}R_{k}M_{k}R_{k}^{T}Q_{k}^{T}=\gamma_{k}I+Q_{k}U_{k}\hat{ \Lambda}U_{k}^{T}Q_{k}^{T},\]  
where \(U_{k}\) and \(\hat{\Lambda}\), respectively, are orthogonal and diagonal matrices. Now, let \(P_{\parallel}\triangleq Q_{k}U_{k}\) (or let \(P_{\parallel}\triangleq(\Psi_{k}R_{k}^{-1}U_{k})^{T}\)) and \(P_{\perp}\triangleq(Q_{k}U_{k})^{\perp}\), where \(\perp\) is an orthogonal complement (perpendicular). By Theorem 2.1.1 in [49], we obtain \(P^{T}P=PP^{T}=I\), where  
\[P\triangleq\begin{bmatrix}P_{\parallel}&P_{\perp}\end{bmatrix}\in\mathbb{R}^{n \times n}. \tag{14}\]  
Therefore, the spectral decomposition of \(B_{k}\) is obtained as  
\[B_{k}=P\Lambda P^{T},\qquad\Lambda\triangleq\begin{bmatrix}\Lambda_{1}&0\\ 0&\Lambda_{2}\end{bmatrix}=\begin{bmatrix}\hat{\Lambda}+\gamma_{k}I&0\\ 0&\gamma_{k}I\end{bmatrix}, \tag{15}\]  
where \(\Lambda=\text{diag}(\hat{\lambda}_{1},\ldots,\hat{\lambda}_{n})=\text{diag}( \hat{\lambda}_{1}+\gamma_{k},\ldots,\hat{\lambda}_{k}+\gamma_{k},\gamma_{k}, \ldots,\gamma_{k})\in\mathbb{R}^{n\times n}\) with \(\Lambda_{1}\in\mathbb{R}^{2l\times 2l}\) and \(\Lambda_{2}\in\mathbb{R}^{(n-2l)\times(n-2l)}\) when \(k>2l\). We note that \(\Lambda_{1}\in\mathbb{R}^{k\times k}\) and \(\Lambda_{2}\in\mathbb{R}^{(n-k)\times(n-k)}\) when \(k\leq 2l\). We also assume the eigenvalues in \(\Lambda_{1}\) are ordered in increasing values. Notice that \(\Lambda_{1}\) includes, at most, \(2l\) elements with limited memory parameter \(l\).
## 3 Results and Discussion
### Theoretical Insights into the Catalytic Suzuki-Miyaura Cross-Coupling Mechanism  
In order to further inspect the mechanism, the effective ligand dtbpf (entries 13-15, Table 3), compounds **3** and **10** (substrates **3** and **10**, Figure 5), were selected for computation using density functional theory (optimized structures are shown in Section S27, Supplementary Materials). As shown in Figure 5, the computational results indicated that, after the formation of the Pd\({}^{(0)}\) precatalyst, the reaction successively underwent oxidative addition, transmetalation, and reductive elimination. In particular, herein, there were three OH groups in the coordinated boronic acid (Int-3, Figure 5): two of them came from the coordinated boronic acid (compound **10**, Scheme 1), while the remaining one was derived from the alkaline reaction medium (Section 2.11), indicating that the P-Pd bond was hydrolyzed by OH\({}^{-}\) in the alkaline reaction medium (Int-3, Figure 5), which propelled a further reaction (Figure 5). The final reductive elimination towards compound **11** (**substrate 11**, Figure 5) was the rate-determining step (\(\Delta\)G\({}^{\ddagger}\) = 10.1 kcal/mol). The overall reaction was strongly exothermic (ca. \(-\)70.5 kcal/mol). For comparison, an alternative ligand dppf (entries 10-12, Table 3) was also tested in the computations. It turned out that the barrier (17.3 kcal/mol) in the rate-determining step with dppf ligand was slightly higher than that with the dtbpf ligand.  
As shown in Figure 6, the selected optimized structures showed that the bond angles of C-Pd-C in **Int-5** were 78\({}^{\circ}\) and 82\({}^{\circ}\) for dtbpf and dppf ligands in the reductive elimination, demonstrating that it suffered more constraint from the steric repulsion of two _tert_-butyl groups than that of two phenyl groups. In this sense, more constraint from the ligand led to a shorter distance and lower barrier in the reductive elimination. Note that, on the contrary, the strong repulsion would increase the barrier of oxidative addition (1.7 vs. 6.5 kcal/mol, **TS1**, Figure 5), although this step was somewhat rapid and could not strongly disturb the overall mechanism.  
Figure 5: Relative Gibbs free energy (kcal/mol) profiles in Suzuki–Miyaura cross-coupling of compounds **3** with **10** to yield compound **11** using Pa catalyst featuring dftbpf (balck solid line) or dppf (red dashed line) ligands.
## 3 System Description
### CSI WiFi Sensing Model  
WiFi CSI-based sensing is predicated on the propagation of packets through sensing subjects, which the transmission model can express as [29]:  
\[Y(f,t)=H(f,t)\times X(f,t)+N(f,t) \tag{1}\]  
where for a specific time instant \(t\) and frequency \(f\), \(Y(f,t)\) is the received wireless signal, \(X(f,t)\) is the transmitted signal, \(H(f,t)\) represents CSI, and \(N(f,t)\) is noise. In modern protocols such as IEEE802.11n and others that followed, the OFDM technique is used to modulate data onto multiple frequency bins called subcarriers. Each subcarrier is a frequency-selective channel, which gives us the CSI readings. Every CSI reading has a unique sensitivity to the channel due to signal propagation in the channel. CSI is acquired as a complex value channel gain at time \(t\). This value provides insights into the attenuation and delay that the channel introduces on the subcarriers operating at various frequencies. It is presumed that the physical medium encountered by these electromagnetic waves at different frequencies will affect them in distinct ways. Consequently, analysing their responses allows us to infer the characteristics of the signal's path.  
In Figure 1, we illustrate the generalised WiFi sensing architecture. The passive sensing and estimation of CSI are based on open-source software such as Nexmon [30], which allows Network Interface Cards (NICs) to extract CSI from received packets. The Tx propagates WiFi packets through the sensing medium, which contains hands performing gestures such as waving, moving, and touching. The act of performing a particular movement causes a change in the wireless signal as mentioned at the beginning of Section 2, consequently altering the amplitude of the ping packets acquired by the Rx. The sensitivity of this device is remarkably high, making it susceptible to even the slightest alterations in its surrounding environment [31]. Consequently, any minor changes in the surroundings, along with the burst noise caused by hardware [32], introduce unwanted disturbances to the channel. The firmware in the Rx includes the Nexmon CSI extraction tool [30], which calculates CSI for received packets and then processes them before being fed into an ML algorithm to predict physical gestures. The definition of the CSI calculation can be illustrated using the following equation [14]:  
\[H(f,t)=\sum_{n=1}^{N}a_{n}(t)e^{-j/2\pi f_{n}(t)} \tag{2}\]  
In this equation, \(a_{i}(t)\) is the amplitude attenuation factor, and \(\tau_{i}(t)\) is the propagation delay. The movement and location changes of test objects or people impact the CSI amplitude attenuation and phase change.
# Intelligent Diagnosis of Compound Faults of Gearboxes Based on Periodical Group Sparse Model
###### Abstract  
A gearbox compound fault intelligent diagnosis method based on the period group sparse model is proposed for the problem that the fault features are coupled with each other and the fault components are superimposed on each other and difficult to be separated in the gearbox compound fault signal. Firstly, a binary sequence is constructed to embed the fault pulse period as a priori knowledge into the group sparse model to decouple and separate the composite fault signal while maintaining the amplitude and sparsity of the extracted features. Secondly, the wavelet packet energy features of the decoupled signals are extracted to improve the data quality while enhancing the characterization ability of the dictionary in the classification model. Finally, the wavelet packet energy features are imported into the sparse dictionary classification model, and the fault diagnosis is completed by outputting the fault categories using the self-driven characteristics of the data. The results show that the fault identification accuracy using the proposed method is 97%. In addition, the experimental validation under different states and working conditions with different rotational speeds allows the superiority and effectiveness of the algorithm in this paper to be tested and has the feasibility of a practical application in engineering.  
compound fault; gearbox; sparse representation; feature classification +
Footnote †: journal: Applied Sciences
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 4 Analysis of the Improved PBFT Consensus Algorithm
### Improvements to Multi-Leader Consensus Algorithms
Having multiple leaders helps lower the complexity of the consensus process while also improving system throughput. These advantages become particularly evident in large-scale networks. However, employing multiple leaders also presents several challenges, including the complexity of leader synchronization and view switching, as well as increased resource consumption. This approach is only suitable for high-throughput, low-latency closed networks (e.g., cloud computing centers). In resource-constrained or high packet loss wireless networks (e.g., 6G edge computing), its performance may deteriorate significantly.
# Domain-Specific Languages for Algorithmic Graph Processing: A Systematic Literature Review
## 4 Discussion
### DSLs for Industrial Large-Scale Graph Analytics: Opportunities and Open Challenges
#### 4.2.1 Potential of the DSL Approach  
Productivity  
Concisely expressing a graph algorithm in a non-graph-specific language can be a tedious task, a fortiori for novice or unaccustomed programmers. DSLs for algorithmic graph processing help manage this complexity through tailored semantics that allow programmers to encapsulate their graph algorithm logic while abstracting over implementation concerns. DSL implementations of graph algorithms are free of the boilerplate code that would otherwise be required for an API-based approach [21]. The reluctance to learn yet another language can be countered with fewer language constructs and a generally smaller grammar than what one might expect from a general-purpose language. Moreover, many DSL developers take this factor into account and strive for a familiar syntax that either mirrors an existing, commonly familiar language (e.g., Green-Marl, Falcon, GraphIt...) or is a subset of one (e.g., PGX Algorithm, PyGB).  
Proven Benefit Gains  
The DSL approach enables substantial performance gains, as DSL compilers leverage their precise knowledge of domain semantics to encode specific optimizations and produce high-performance programs. Several DSLs for algorithmic graph processing report superior performance to non-DSL-based state-of-the-art solutions.  
Tools for Cost-Efficient DSL Implementation  
Emerging DSLs for algorithmic graph processing can reuse existing DSL processors to save up on implementation efforts or extend existing DSL compilers with support for new frontends or new target platforms. Recent advancements in language workbenches and DSL tooling have lowered development costs and improved the DSL experience for users, as well as developers of the language. This addresses the issue of a lack of the DSL tool and support raised in existing literature [86]. Multiple case studies in industry and beyond report on a positive experience using these tools for domain-specific software language engineering [87]. DSL users can also benefit from state-of-the-art IDE tooling at minimal cost [88], which somewhat helps close the gap between DSLs and more widely used general-purpose languages.
## 3 Case Study
### Discussion and Analysis
The number of sensors in the lower-left part of the dam gradually increases, resulting in a more even distribution of sensors across the entire dam. After considering the superimposed effects of contraction joints and cracks, the location of the sensor placement can effectively balance the effects of both, and the sensor placement is more uniform overall, but the sensors in the middle and upper parts of dam body are more intensive, especially near the 105 m and 111.5 m elevations, and the number of sensors increases. Consideringonly dam material zoning, there is minimal change in sensor position compared to the seamless condition. When only considering elastic modulus zoning degradation of the dam body, as the degradation period increases, there is a noticeable downward shift in sensor positions. This trend is subtle in the first 20 years but becomes more pronounced beyond 30 years. The number of sensors increases in the upper-right part, lower-left part, and the overall lower-middle part of the dam. When considering the combined impact of the cracks, contraction joints, and elastic modulus zoning degradation of a dam body, the spatial distribution of sensors becomes more even. With an increase in the degradation period, the positions of sensors gradually move from the upper-middle part of the dam to the lower-middle part. After 30 years of degradation and beyond, the downward shift of sensors becomes more pronounced. In summary, based on changes in sensor spatial placement, cracks, contraction joints, and elastic modulus zoning degradation of a dam body all have significant impacts on sensor positions. (5) The OSP for the concrete arch dam studied in this paper takes into account that the dam has been in operation for 45 years and has cracks, contraction joints, and degradation in the elastic modulus zoning of the dam body. The final placement of the arch dam differs from that during the seamless period, as reflected by an increase in the number of sensors in the upper-right and lower-left sections of the dam, as well as a denser arrangement of sensors in the middle to lower sections of the dam body. This paper outlines the monitoring scheme for the current condition of the dam after 45 years of operation, determining that a total of 36 sensors need to be placed in the dam, with the final sensor layout shown in Figure 21r. In summary, it is essential to consider the effects of cracks, contraction joints, and elastic modulus zoning degradation of a dam body in the OSP problem of arch dams. Additionally, utilizing advanced monitoring technologies, such as the stretchable fiber strain sensors studied by [PERSON] et al. [64], can enhance the capabilities of monitoring cracks in concrete dams. These sensors are capable of detecting the propagation of existing cracks and the emergence of new cracks in real time, thereby significantly improving the accuracy and timeliness of damage assessment.
# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
## 4 Discussion  
During the development and realization of the robot, we once faced a difficult situation when the robot was walking on grass and gravel roads. When walking on certain rugged roads, the entire robot shakes like an inverted pendulum due to the vibration caused by the omnidirectional wheel. When the shaking period meets a certain frequency, the robot may even fall. Although this problem is caused by the robot's center of gravity and omnidirectional wheel specifications, as a solution, we reduced the robot's moving speed and increased the number of batteries at the bottom of the robot to adjust the center of gravity. Consequently, the endurance of the robot increased; it could patrol for 1 to 2 hours  
Figure 35: The robot patrolled in the nighttime and encountered a stranger passing by.  
Figure 36: The robot patrolled in the night and identified the person passing by as a stranger.  
each time and reduce the demand for rapid response time for obstacle avoidance. In the area of image processing and face recognition, we used the repeated recognition method to improve the accuracy, with the advantage that even a single recognition error cannot affect the overall recognition rate, so it can overcome the impact of the poor classification ability due to the light effect and save several training resources and time spent on the entire neural network. However, the disadvantage is the increase in the time spent on face recognition. We made a few adjustments to sample pictures of the known targets in dynamic to perform classification. The image calculation could be completed in 1 second. For the result of a robot replacing a human in patrol, we send back the message of the detected person to the remote-control center. This has three advantages. First, when the robot's recognition result is a stranger or an intruder, the personnel of the control center can reconfirm and decide whether a security guard is needed to the place of the incident. The second is data transmission and mailing through the 4G network. The coordinates and images of people can be known in detail. Naturally, it can also be set to automatically send messages to community guards or police stations when an incident occurs. Finally, during the patrol process, the image captured by the robot camera is synchronized back to the control center. If the personnel of the control center see an abnormality on the screen that the robot has not found, they can react immediately. Indeed, if the control center can have sufficient data transmission and computing performance, it is entirely possible to set up a robot formation to cover the patrolling and supporting tasks in the desired area.