esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 4 Utility Optimization  
### Problem Formulation  
For the considered system, the energy efficiency proportional fairness maximization problem can be mathematically formulated as:  
\[\begin{array}{ll}\max_{A_{k}\rho_{k}}&U\left(A_{k},\theta_{k}\right)\\ s.t.&C_{1}:\theta_{k}\geq 0,\forall k,\\ &C_{2}:\sum_{k=1}^{K}A_{k}=1,\\ &C_{3}:A_{k}>0,\forall k,\end{array} \tag{16}\]  
where \(C_{1}\) are non-negative constraints of receive threshold variables for any tier, \(C_{2}\) ensures all active nodes associate with network, \(C_{3}\) is the BSs in any tier at least serving for a active node.  
This problem does not have a closed-form solution and it is not convex in general. Then, we will relax the objective function in the special case and solve the problem. Considering that the \(\log\left(x\right)\) is a concave function, we can obtain  
\[\mathbb{E}\left[\log\left(W/N\right)\right]\leq\log\left(\mathbb{E}\left(W/N \right)\right)=\log\left(\frac{W\lambda_{k}}{A_{k}\lambda_{u}}\right). \tag{17}\]  
The left side of (17) is due to Jensen inequality and the right side is from (14). Meanwhile, due to function \(\log\left(\frac{C_{k}}{P_{t,k}+P_{t,k}-\eta\left(1-C_{k}\right)P_{EH,k}}\right)\) is concave function for \(C_{k}\), we can obtain that  
\[\begin{array}{ll}&\mathbb{E}\left[\log\left(\frac{C_{k}}{P_{t,k}+P_{t,k}- \eta\left(1-C_{k}\right)P_{EH,k}}\right)\right]\\ &\leq\log\left[\mathbb{E}\left(C_{k}\right)\right]-\log\left(P_{t,k}+P_{s,k}- \eta\left(1-\mathbb{E}\left(C_{k}\right)\right)P_{EH,k}\right).\end{array} \tag{18}\]  
Substituted (17) and (18) into (6), we can obtain that  
\[\begin{array}{ll}&U\left(A_{k},\theta_{k}\right)\leq U_{r}\left(A_{k}, \theta_{k}\right)\\ &=\sum_{k=1}^{K}A_{k}\log\left(\frac{W\lambda_{k}}{A_{k}\lambda_{u}} \right)+\sum_{k=1}^{K}A_{k}\log\left(\log\left(1+\theta_{k}\right)\right)+ \sum_{k=1}^{K}A_{k}\log\left[\mathbb{E}\left(C_{k}\right)\right]\\ &-\sum_{k=1}^{K}A_{k}\log\left(P_{t,k}+P_{s,k}-\eta\left(1-\mathbb{E}\left(C_ {k}\right)\right)P_{EH,k}\right).\end{array} \tag{19}\]This upper-bounded energy efficiency proportional fairness utility function is formulated as  
\[\begin{array}{ll}\max_{A_{k}\theta_{k}}&U_{r}\left(A_{k},\theta_{k}\right)\\ s.t.&C_{1}:\theta_{k}\geq 0,\forall k,\\ &C_{2}:\sum_{k=1}^{K}A_{k}=1,\\ &C_{3}:A_{k}>0,\forall k.\end{array} \tag{20}\]
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 4 Over-the-Air Demonstrations
### Ping-Pong  
In addition to the WISCANet control software, we also publish several example applications in our open source distribution to help researchers develop their own custom techniques. One such application is named \"ping-pong\" which is a simple communications application that passes a message between an arbitrary number of radios [30]. This pre-built network infrastructure allows users to test PHY and MAC layer techniques without having to design a network themselves, thereby reducing the barrier to entry and allowing users to focus on research rather than the tedium of networking and configuration. To demonstrate the utility of this pre-built application, we use the ping-pong application to  
Figure 11: Experimental results for the 1-4-1 distributed mesh relay network. The y-axis represents the effective increase in SNR at the final receiver as a result of the distributed relay and multi-stage beamforming compared to the equivalent single input, single output (SISO) direct link. Channel state information is not available until after the first transmit and receive cycle, so the first measurement is limited to incoherent beamforming gain.  
Figure 10: Distributed mesh relay network with 4 distributed radios in each mesh \(A\) and \(B\). These mesh relays execute a distributed, multi-stage beamforming algorithm to relay a message between the transmitter and receiver.  
enable dynamic PHY and MAC layer techniques in real-time, over-the-air demonstrations. In Section 4.3, we leverage ping-pong to deploy an adaptive PHY layer technique that updates the waveform design in response to changing channel conditions. In Section 4.4, we leverage ping-pong to deploy a MAC layer technique that implements simple spectrum sensing and dynamic spectrum access.
## 6 Discussion
Furthermore, since the concept of network coding increases the amount of consumed energy, it is imperative to investigate whether or not all nodes in the network are required to perform network coding, or if a specific set can perform the network coding, without significant loss in overall performance. Lastly, network coding also has a propensity to increase end-to-end latency. Thus, different techniques for network coding (e.g., configuring the number of buffered packets) should be investigated, to possibly limit latency. As the design and requirements for a more reliable HCWSN become more complex and diversified, the effort towards ensuring the secure transmission of private and delicate medical data must also persist. CodeBlue and MASN have implemented security schemes to provide security and privacy of data transmission. Specifically, CodeBlue has implemented Elliptic Curve Cryptography (ECC) on its MICA2 modes only using integer arithmetic [13]. Unfortunately, the time to generate an encryption key takes 35 s, which is deemed unsatisfactory [42]. MASN has implemented a trustworthy medical transmission scheme to promote confidentiality and integrity of patient data through the use of crypto-keys. However, the security design is only limited to one-hop ECG data transmission. [PERSON] _et al._ have stated that the integration of security protocols into MEDiSN remains a top priority. While no recent literature appears in which the transmission of data in MEDiSN has been fortified with a relevant security scheme, [PERSON] _et al._[43] proposed a pervasive, secure access scheme for a hierarchical-based architecture that closely matches that of MEDiSN. The performance results in [43] show that, using a symmetrical key cryptosystem, the transmission of private data can be kept secure while only slightly degrading overall system performance. The following future work must be pursued to enhance the security of CodeBlue, MASN, and MEDiSN: For CodeBlue, a form of cryptography improving the complexity of integer arithmetic should be explored. In the case of MASN, the single hop security design for ECG data transmission must be extended to a multi-hop scheme. Finally, the adoption of the security scheme proposed in [43] into MEDiSN should be pursued as future work.
## 2 Theory
### Split Bregman Algorithm  
The Split Bregman algorithm is from the class of Alternating Direction Method of Multipliers (ADMM) that split a constrained problem into a sequence of simpler unconstrained sub-problems [19; 28]. It differs from continuation-based methods by keeping the values of any Lagrange multipliers fixed between iterations and modifies the data instead. This has the benefits of increased numerical stability and a lower dependence on the initial choice of Lagrange multiplier values. If we wish to use the Split Bregman algorithm to solve a problem of the form:  
\[\begin{array}{ll}\min_{u,z}&E(u,z)\\ \text{such that}&H(u,z)\end{array} \tag{2}\]  
where \(E(u,z)\) is a convex, non-differentiable function and \(H(u,z)\) can be assumed to be of the form \(Au=z\), we first convert it to an unconstrained problem:  
\[\min_{u,z}E(u,z)+\lambda H(u,z) \tag{3}\]  
where conventional continuation-based methods would increase \(\lambda\rightarrow\infty\) as \(H(u,z)\to 0\) to find a solution; however, we apply the Bregman distance relaxation to Equation (3) and split it into sub-problems instead. The Bregman distance for the function \(E(u)\) at the point \(u^{k}\) is:  
\[D_{E}^{p}(u,u^{k})=E(u)-E(u^{k})-\langle p_{u}^{k},u-u^{k}\rangle \tag{4}\]where the Bregman parameter \(p_{u}^{k}\) is the sub-gradient of \(E(u)\) at \(u^{k}\). The Bregman distance and iteration scheme are then applied to Equation (3) by defining the \(u\), \(z\), and Bregman parameter updates at each iteration over \(k\):  
\[\begin{array}{rl}(u^{k+1},z^{k+1})&=\min_{u,z}D_{E}^{p}(u,u^{k},z,z^{k})+ \lambda H(u,z)\\ p_{u}^{k+1}&=p_{u}^{k}-\lambda\
abla_{u}H(u^{k+1},z^{k+1})\\ p_{z}^{k+1}&=p_{z}^{k}-\lambda\
abla_{z}H(u^{k+1},z^{k+1})\end{array} \tag{5}\]  
\(\lambda\) is never increased and under fairly weak assumptions on \(E(u,z)\) and \(H(u,z)\), \(\
abla_{u}H\) and \(\
abla_{z}H\to 0\) as \(k\rightarrow\infty\) so that \(p^{k+1}\to p^{k}\) and the Bregman parameters converge [19]. However, because \(H(u,z)\) is of the form \(Au=z\), Equation (5) can be simplified to the equivalent problem [29]:  
\[\begin{array}{rl}(u^{k+1},z^{k+1})&=\min_{u,z}E(u,z)+\lambda\|z-Au-b^{k}\|_ {2}^{2}\\ b^{k+1}&=b^{k}+\left(Au^{k+1}-z^{k+1}\right)\end{array} \tag{6}\]  
where \(b^{k+1}\) is a Bregman parameter that ensures \(Au\to z\) as \(b^{k+1}\to b^{k}\) converges without increasing \(\lambda\) and sacrificing stability. Because \(E(u,z)\) is convex and non-differentiable, Equation (6) is split into its \(u\) and \(z\) sub-problems, which are solved separately at each iteration, decoupling \(u\) from \(z\)[28]:  
\[\begin{array}{rl}u^{k+1}&=\min_{u}E(u,z^{k})+\lambda\|z^{k}-Au-b^{k}\|_{2}^{ 2}\\ z^{k+1}&=\min_{z}E(u^{k}+1,z)+\lambda\|z-Au^{k+1}-b^{k}\|_{2}^{2}\\ b^{k+1}&=b^{k}+\left(Au^{k+1}-z^{k+1}\right)\end{array} \tag{7}\]
## 4 Materials and Methods
### Analytical Methods
#### 4.6.6 Volatile Profile  
The profile of volatile compounds was analyzed by headspace, solid phase micro-extraction gas chromatography-mass spectrometry (SPME GC-MS), as described in [3] with slight modifications. In brief, for the SPME sampling, 2 mL of wine sample, 7.5 mL water, 1 g NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 500 \(\upmu\)L of 1,4-dioxane (1000 mg/L; as IS) were mixed in a glass vial, sealed, and heated for 5 min in a water bath at 40 \({}^{\circ}\)C. The SPME fiber (DVB/CAR/PDMS, 2 cm; Sigma-Aldrich, Darmstadt, Germany) was then exposed to the headspace for 30 min. The GC-MS analysis took place on a GC-MS-QP2010 Ultra (Shimadzu Inc., Kyoto, Japan) instrument at the following conditions: exposure of SPME fiber in the injection port (240 \({}^{\circ}\)C, 5 min, split ratio 1/10); GC separation with He as carrier gas (36 cm/s) in a DB-Wax capillary column (30 m, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness, Agilent Technologies Inc., Santa Clara, CA, USA), with temperature rising from 40 \({}^{\circ}\)C (5 min), to 180 \({}^{\circ}\)C (by 5 \({}^{\circ}\)C/min), to 240 \({}^{\circ}\)C (by 30 \({}^{\circ}\)C/min; held 5 min); MS analysis by electron ionization (70 eV, 40-300 \(m/z\) mass scan range, source and interface set at 200 and 240 \({}^{\circ}\)C, respectively). Identification and semi-quantification (normalized peak areas %) were performed with the GC-MS Solution (ver. 4.30; Shimadzu), AMDIS (ver. 2.72; NIST), and NIST MS Search (ver. 2.2; NIST) software, and were based on comparison of: (i) retention indices (RI) of C8-C24 n-alkanes, authentic compounds and those available in NIST14 library (NIST, USA), (ii) MS data with those of reference compounds and those obtained from NIST14. The reliability of identification (RID) was considered at levels: A, agreement of RI and MS spectra with those of an authentic compound; B, agreement of RI (\(\Delta\)RI \(<\) 20), and MS similarity match \(>\) 900; and C, at least \(\Delta\)RI \(<\) 20 or MS similarity match \(>\) 800.
# Double Dead-Time Signal Injection Strategy for Stator Resistance Estimation of Induction Machines
## 2 Signal Injection Principle and Considerations
These voltage drops in the inverter are represented in Figure 2. The voltage drop due to the dead-time is dependent on the dead-time value, the DC current offset level, the bus voltage level and the stator current. The dead-time is introduced in the commutation of the IGBTs to avoid two switches in the same arm conducting at the same time, and thus avoiding a shortcut in the DC bus. This phenomenon produces a voltage drop in the injected offset; as previously mentioned, it is dependent on the duty cycle, the current offset level and the bus voltage. The voltage drop in the semiconductors depends on the current DC value, the temperature of the semiconductors and the stators current level. Finally, the series resistance drop  
Figure 1: Closed loop vector control with injection algorithm. Figure 2: Injected voltage drop representation. depends in the current DC offset and the temperature of the cable. As a constant current offset is injected, the dependency to this parameter can be eliminated. Analyzing the parameter dependency in the voltage drop due to the dead time, it can be observed that the parameters \"dead-time value\" and \"bus voltage\" can be made constant. In this way, their compensation is easy and the dependency of these variables can be eliminated. Therefore, the dead-time voltage drop depends only in the stator current value. Unfortunately, this parameter can change in amplitude, frequency and phase. A small change in any of these variables can modify the voltage drop due to the dead time. From the other side, analyzing the variables that affect the voltage drop in the semiconductors, the temperature is usually measured and can be compensated for easily. Thus, it will only depend on the stator current. Fortunately, the change in this parameter is less sensitive in the change in the voltage drop in semiconductors than in the dead-time voltage drop, making its compensation easier. Finally, the voltage drop in the cable can be easily compensated for using a simple thermal model. Thus, the major difficulty comes in the compensation of the dead-time voltage drop. In the present publication, a new dead-time effect elimination method is exposed. It has been eliminated using a double dead-time injection. The voltage drop in the semiconductors can be compensated for easily with a look-up table in function of the stator current level. Thus, the voltage in the machine terminals can be estimated easily compensating the voltage drop in the inverter.
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 4 Linearized Cadzow Iterations in the Signal-Estimation Problem
### Limit Property of the Composition of Projections
#### 4.4.1 Example of Bad Convergence as \(\alpha\) Tends to Zero
Applying Corollary 2 for \(\mathbf{X}=\mathbf{N}\) and \(\mathbf{a}=0\), we obtain that \(\|\widehat{\mathbf{N}}(0)-\widehat{\mathbf{H}}(0)\|_{\mathbf{I}_{L}\mathbf{R}( 0)}=0\); this yields the statements of the corollary due to the smoothness of the left-side and right-side expressions. The declared result is described in the following proposition. **Proposition 5**. : _Let \(L>2r>0\) and \(N=2L\). Then,_  
\[\cos(\widehat{\mathcal{H}}(\mathbf{I}_{L},\mathbf{R}(\mathbf{a})),\widehat{ \mathcal{N}}(\mathbf{I}_{L},\mathbf{R}(\mathbf{a}))\xrightarrow[a\to 0]{}1.\]  
Proof. : Note that the cosine of the angle between a vector and a subspace (see Definition 1) is equal to the cosine between this vector and its projection to the subspace. Thus, we can use Corollary 3. It follows from Corollary 3 that the numerator and denominator in the definition of cosine are equal, and therefore, it is sufficient to prove that \(\|\widehat{\mathbf{N}}(0)\|_{\mathbf{I}_{L}\mathbf{R}(0)}\
eq 0\). The latter can be shown by constructing such a matrix \(\widehat{\mathbf{N}}(0)\) from the trajectory matrix \(\mathbf{S}_{0}=\mathcal{T}_{L}(\mathbf{S}_{0})\). Let \(S^{\text{T}}\) be a row of the matrix \(\mathbf{S}_{0}\) with a non-zero element at the first or last position (such a row always exists since \(\mathbf{S}_{0}\
eq 0\)). Without loss of generality, we suppose that the first element of \(S^{\text{T}}\) is not zero. Denote \(\mathcal{G}:\mathbb{R}^{N}\to\mathbb{R}^{L}\) the mapping that truncates the vector to the first \(L\) coordinates. Then, let \(Q\in\mathbb{R}^{L}\) be a nonzero vector, which does not lie in \(\mathcal{G}(\mathcal{Z}(\mathbf{a}_{0}^{2}))\), and \(\mathbf{N}=QS^{\text{T}}\). By construction, \(\mathbf{N}\in\mathcal{N}(\mathbf{S}_{0},\mathbf{L},\mathbf{R})\) and \(\widehat{\mathbf{N}}(0)\) have a nonzero first column, and therefore, \(\|\widehat{\mathbf{N}}(0)\|_{\mathbf{I}_{L}\mathbf{R}(0)}\
eq 0\). Thus, for a popular case \(N=2L\), we have shown that the cosine of the \((2r+1)\)-th principal angle can be as close to \(1\) as one wishes; hence, the convergence of the Cadzow method becomes as slow as one chooses with a small \(\alpha\). Recall that the sense of choosing a small \(\alpha\) is to increase the estimation accuracy.
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 2 Related Work
Moreover, JRCA suffers from overhead and this aspect was not evaluated in the paper. In [18], the authors proposed DiffQ, which is queue differential prioritization-based and aims to control congestion in wireless multi-hop networks. DiffQ uses the queue size difference between two neighboring nodes and sends traffic to the node that minimizes this queue size difference. DiffQ supposes that data packets have many destinations and relies on a routing protocols that specifies the next-hop of each packet. DiffQ is based on the current number of packets in the queues of neighbor nodes, which does not give an idea about the queueing delay of neighbors. A node with a high queueing delay might have fewer packets in its queue simply because it was not chosen as a next-hop by other nodes. In addition, DiffQ is not considered as a routing protocol, and it uses the already existing ETX [19] routing approach for next hop selection in the presented experiment. Moreover, in the presented experiment results, many packets are loss due to queue overflow, about 2500 drops over 2 min experiment duration in an IEEE 802.11 network. The IETF Routing Over Low power and Lossy networks (ROLL) Working Group designed a routing solution for low power and lossy networks (LLNs) including wireless sensor networks. The working group has specified the Routing Protocol for Low power and lossy networks (RPL). RPL is designed to meet the core requirements for data transmission in LLNs. It is being developed as a standard to be deployed in a number of environments: urban networks, smart grid networks, industrial networks, building and home networks. The routing scheme of RPL consists of constructing a Directed Acyclic Graph (DAG) rooted at the sink, which will minimize the cost of reaching the sink from any node in the network as per the Objective Function (OF). The OF of RPL can consider the Expected number of Transmission (ETX) or the number of hops from each node towards the final destination (HopCount) to construct the DAG. Nevertheless, RPL is not designed for high data rate networks and suffers from congestion because it lacks a load balancing mechanism. Most of the presented joint channel allocation and routing protocols either have a synchronization phase [15; 16] that increases the packet end-to-end delay or overloads the network with periodic beacon frames [13] for channel allocation and routing information sharing. Our contribution,ABORt, presented in the next section, is a routing protocol for multichannel WSNs that avoids the synchronization period once the network is in operational mode and achieves low end-to-end delays and limited overhead.
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 3 Rapid Progress of UAV-Based Electrostatic Spray Technology
### Beginning of the Research on UAV-Based Electrostatic Spray Technology  
In 2015, [PERSON] et al. [31] tried the first combination of electrostatic spray technology and plant protection UAV with an XY8D unmanned helicopter and carried out field tests in a rice field, as shown in Figure 2. A preliminary experiment on spray pressure, flow rate, and charging voltage were conducted to determine the optimal parameters for field tests. Field spraying test results showed that the droplet deposition and coverage rate on the target crop was effectively improved by the UAV-based electrostatic spraying when compared with non-electrostatic spraying. The influence of environmental factors and physical properties of the liquid was not verified in this experiment. However, the first study of carrying the electrostatic spray system on a UAV was of great value. Chinese scholars then began to make continuous innovations and breakthroughs in UAV-based electrostatic spray technology.  
Figure 1: Increasing number of plant protection UAVs and operating area covered in China (2014–2020).  
Figure 2: Rice field test with an XY8D UAV-based electrostatic spray system [31].
## 5 Implementation and Evaluation
### Testing of Smart Broker Components  
The implementation view of the smart broker is presented in Figure 5. The smart broker operates through two parallel running components: the Publisher End Module and the Subscriber End Module. The smart contracts within each module operate based on the following network conditions:  
* _Good communication_: The blockchain network mainly operates at the application layer, so network delays do not hamper the application's outcome. The IoT sensors would be sending data at an interval of at least 10 s, making any network-level latency variations and jitters insignificant to the drift score calculations.
* _Distributed functionalities_: The smart contracts all study fail-safe features. Due to their distributed nature, the functionalities will always be studied as long as there is at least one node. Multiple peer nodes are expected for a practical application, but for this paper, we tested the performance with one peer node. The functionality of the smart contracts is as follows: (I) _Publisher End Module_: The smart contracts within the publisher end module are working as follows:
* _PTJ_: This smart contract performs a topic registration process, which involves verifying essential tags and topic pairing. It updates the correlation matrix accordingly. The contract also stores topic details, including tag information, along with the updated correlation matrix. It also communicates with TDM, TDQAM, and TRS about the  
Figure 5: Implementation view of the smart broker.  
newly registered topic and its details so that TDM, TDQAM, and TRS can initiate their assessment of the topic.
* _TDM_: This smart contract retrieves publishing interval information for topics and continuously monitors the drifting score. It stores the drifting score after each interval, ensuring real-time tracking and analysis.
* _TDQAM_: This smart contract conducts quality assessments on topic data. It retrieves topic pairing data streams, correlation information, and publishing interval details from the ledger. Using this data, the topic's quality is calculated, and the real-time quality score is stored in the ledger.
* _TRS_: The smart contract on the publisher side retrieves the topics' assessment scores, quality scores, and drifting scores from the ledger. It then calculates the topic's reputation score and stores it in the ledger.
2. _Subscriber End Module:_
* _SRAM_: This smart contract handles subscriber requests, providing data along with its quality score to the subscriber. It also prepares recommendation topics, if required, by calculating the similarity score, retrieving correlation information, and calculating the reputation score. Then, it prepares the recommendation score for the topics and sends it to the subscriber.  
The components within the smart broker begin to assess the topic and its data based on configurations predefined and specified by the publisher (e.g., publishing interval) at the time of registration. The parameters for the experiment are shown in Table 2.