esa-sceva/satcom-chunk-collection huggingface dataset:
We adapted the client architecture and applied advanced traffic reduction functions for an Ethereum--based smart-grid switch and counter [25]. Using a binary difference service we managed to reduce the size of the communication traffic to the end device as long as the binary patch was smaller than the patching file. Beside the evident impact in reduction of communication traffic, the proposed solution maintains high flexibility and can be easily adapted to, e.g., changes in remote APIs, or can accommodate other/new communication and application protocols. In our research we performed the following. * Designed and implemented an application-aware gateway, LDAF, which can be a part of a fog computing architecture. It enables fog data services for reduction of communication traffic of bandwidth constrained IoT devices to connect them to arbitrary cloud APIs. * We set-up and executed a decentralized application use case with blockchain-enabled IoT devices, where the LDAF gateway was applied and adapted to the application's operation, to reduce the size and number of messages needed for the IoT application. * For blockchain-enabled IoT devices we measured that the binary encoding of message content reduced the periodic traffic from 16 kB/s to ~1.1 kB/s, i.e., 7% of the initial traffic. Additional optimization of the application protocol in the gateway and message filtering further reduced the periodic traffic to ~1% of the initial traffic, without any tradeoffs in the application's functionality or security. In two other supporting use cases we demonstrated the possible applicability of LDAF for other fog services. In Section 2 we briefly present the adaptations of the communication networks and protocol stacks in IoT systems. Section 3 elaborates the architecture and objectives of the LDAF framework and Section 4 positions it in the modern fog architecture. In Section 5 we present three use cases where LDAF was applied to optimize communication traffic of constrained IoT devices. Section 6 concludes our research and highlights further research and application options.
# Specific Resonant Properties of Non-Symmetrical Microwave Antennas
## 2 Materials and Method
### Method for Evaluating Resonant Properties of Microwave Antennas
In aviation, from an aerodynamic point of view, it is necessary to implement an antenna with the shortest possible length. At the same time, this antenna must have the required range of operating frequencies at these minimum physical dimensions. With the above knowledge of antenna theory, we entered into the required experimental measurements with the goal defined in the introduction above. It is well known that thicker antenna wires of the same length as thinner antenna wires always have a lower resonant frequency. This rule applies when comparing them at any length. This fact is expressed by the illustrative graph in Figure 7. The parameter is the diameter of the antenna conductors, where it is planned to use diameter values of \(\phi\) = (6, 12, 25) mm. It is clear from the sketched graph that on each equal length of the antennas, their resonant frequency can be defined by the relation: \(f_{res}\ \phi\)6 \(>\)\(f_{res}\ \phi 12\)\(>\)\(f_{res}\ \phi 25\). Such a simplified expression of resonant frequency ratios is taken as regularity in antenna theory. We also relied on this rule at the beginning of the production of our antenna models [18; 21]. To apply the above theory, the graph in Figure 7 introduced antenna diameters through the antenna slimmess coefficient \(\Omega\). The coefficient of the slimmess of the antenna expresses the ratio of the length of the antenna \(l\) to its diameter \(\phi\), (\(\Omega\) = \(l/\phi\)), which means that the larger the value of \(\Omega\), the slimmer the antenna is. In the case of our measurements, we consider antenna wires with a diameter of \(\phi\) = (6, 12, 25) mm. The slimmess coefficient is different for each length. However, the property applies to the listed cylindrical antennas (\(\Omega_{\phi 6}\) = \(\Omega_{\phi 12}\) \(>\)\(\Omega_{\phi 25}\)). Figure 6: Determining the steepness of the reactance line and the position of the resonant points.
# Space-Efficient Fully Dynamic DFS
## 4 Query Reduction to Orthogonal Range Search Problem
### Efficient Reduction
Figure 1: The configurations of \(T(w)\) and \(path(x,y)\) in \(T\) that can appear in \(Q(T(w),x,y)\). From Claim 1 we prove (a) under \(U=\emptyset\) and \(x\preceq y\). Lastly, if \(y\prec x\), all we must do is swap \(x\) and \(y\) and perform almost the same as described above, except that we solve an ORP (not ORS) query on \(\mathcal{G}\). Next we show (b). First we assume that \(U\) consists of only vertices and contains no edges. In this setting we can confirm from Definition 2 that for the query \(Q(T(w),x,y)\), \(T(w)\) and \(path(x,y)\) have no vertices in \(U\). Thus, \([t_{b},t_{e}]\) contains no vertex ids of the vertices in \(U\). We can also say that even if \([f(x),f(\operatorname{LCA}(y,w))]\) in Claim 1 contains some vertex ids of the vertices in \(U\), these vertices are all in the branches forking from \(path(x,y)\) and cause no trouble. Therefore, even if \(U\) contains some vertices, Claim 1 also holds. For the query \(Q^{\prime}(w,x,y)\), we use the original reduction described in Section 4.1. We can also say from Definition 2 that \(\{w\}\) and \(path(x,y)\) have no vertices in \(U\). Thus, \([f(w),f(w)]\) and \([a_{i},b_{i}]\) (\(i=1,\ldots,k\)) contain no vertex ids of the vertices in \(U\). Finally, we consider the case \(U\) contains some edges. In this case, it seems that deletion of these edges from \(\mathcal{G}\) is needed. However, deleting one edge \(e=(u,v)\) can be simulated by one vertex deletion and one vertex insertion as follows: first record \(u\)'s incident edges \((u,w_{i})\) (\(i=1,2,\ldots\)) excluding \(e=(u,v)\) itself, second delete \(v\), and then insert a vertex \(u^{\prime}\) with its incident edges \((u^{\prime},w_{i})\) (\(i=1,2,\ldots\)). In the algorithm of [PERSON] et al. [4], vertex insertions are treated separately from the queries \(Q\) and \(Q^{\prime}\). Thus, in this way we can avoid deleting \(e\) from \(\mathcal{G}\). This completes the proof of Lemma 3. Lastly we briefly explain that \(Q^{\prime}\) cannot be converted to single ORS (ORP) query in the same way as \(Q\). The five patterns of configuration of vertices drawn in Figure 1 can also appear in \(Q^{\prime}(w,x,y)\). However, there is a corner case that can appear in \(Q^{\prime}(w,x,y)\) but cannot appear in \(Q(T(w),x,y)\): \(w\prec x\preceq y\), as drawn in Figure 2. This pattern cannot appear in \(Q(T(w),x,y)\) since if \(w\prec x\preceq y\) then \(T(w)\) overlaps with \(path(x,y)\). This corner case is relatively hard to convert to single ORS/ORP query on \(\mathcal{G}\), since every vertex of a branch forking from \(path(x,y)\) has ancestor-descendant relation with \(w\). Please note that it does not matter if \(Q^{\prime}(w,x,y)\) is solved \(O(\log n)\) times slower than \(Q(T(w),x,y)\) (see Lemma 1).
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 3 Research Background
### Location-Allocation
#### 3.1.1 Minimum Facility Location Problems  
The objective of minimum facility location problem is to find the minimum number of facilities while covering all demand. It can be stated that each and every demand point has at least one server located within some distance or time standard [22]. When the cost of building facilities is not considered, a demand point inside the impedance cutoff has been allocated to the nearest facility [30]. The formulation of the model is as follows:  
Sets:  
\(I=\) set of demand nodes (indexed by \(i\));  
\(J=\) set of eligible facility sites (indexed by \(j\));  
Parameters  
\(N_{i}=\left\{j\middle|\ d_{ji}\leq S\right\}\);  
\(d_{ji}=\) the distance from potential facility location \(j\) to demand node \(i\);  
\(S=\) the distance standard for coverage;  
Decision Variable:  
\(x_{j}=\) Binary variable equals 1 if a facility is located at node j, 0 otherwise:  
\[Min\sum_{j\in J}x_{j} \tag{1}\]  
subject to  
\[\sum_{j\in N_{i}}x_{j}\geq 1\ \ \forall i\in I \tag{2}\]  
\[x_{j}=0,1\ \ \forall je\ J \tag{3}\]  
Note that \(N_{i}\) is the set of all those candidate sites for the potential location of facilities that are within distance \(S\) of demand node \(i\). When a facility is located in any of the candidate sites, demand node \(i\) is covered. The objective in Equation (1) minimizes the number of facilities required. Constraints in Equation (2) ensure that there is at least one opened facility within standard distance s from each demand node [22].
## 3 Methodology
### Application Type for Supervised Learning
#### 3.1.1 Gradient Boosting Model  
Gradient boosting is an ensemble technique that creates a strong predictor by combining the outputs of several weaker models. It is capable of working with different types of features, such as numerical, categorical, and even missing data [23]. The GB Regression algorithm was chosen for predicting ground station loads, demonstrating superior performance compared to other tested machine learning models, including multiple linear regression, logistic regression, support vector machine with various kernel functions, random forest, and extreme gradient boosting [24]. The boosting method is a type of ensemble method that starts with an initial prediction and progressively refines it by learning from the remaining errors. At each step, a small decision tree is trained to predict these residual errors and is then added to the ensemble, thereby reducing the overall error. In machine learning models, including those based on boosting, the total error can be broken down into bias, variance, and irreducible error [25].  
The GB training procedure adheres to [PERSON]'s least squares gradient boosting (LSGB) formulation [26]. Given a dataset \(I=\{(a_{1},b_{1}),(a_{2},b_{2}),\,\ldots,(a_{n},b_{n})\}\), where \(a_{i}\) represents the input features and \(b_{i}\) the corresponding target values, the objective is to minimize a differentiable loss function \(L(y,f(a))\), which measures the difference between the predicted value \(f(a)\) and the actual value \(b\). This is performed by sequentially fitting base learners \(h\,(a;\phi)\) to the gradients of the loss function, updating the prediction model as follows:  
\[f_{m}(a)=f_{m-1}(a)\ +\ v.\ h_{m}(a) \tag{1}\]  
where \(v\) is the learning rate and \(h_{m}(a)\) is the weak learner trained at iteration \(m\). Weights are calculated for each leaf node to minimize the loss locally after each tree divides the input space into regions. The iterative procedure keeps going until a maximum number of iterations or a convergence criterion is met.
## 3 Peak-Shaving Costs and Economic Dispatch Model
### Modeling Peak-Shaving Costs
#### 3.1.1 Coal Consumption Cost  
The coal consumption cost of unit \(i\) during period \(t\) is characterized by a quadratic function given as follows [25].  
\[f_{i}(P_{i,t})=\left(aP_{i,t}^{2}+bP_{i,t}+c\right) \tag{1}\]  
where \(a\), \(b\), and \(c\) are the coefficients determined experimentally by the generator producers.
# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 5 Results and Discussion
### Average Error Probability Bound of Short Packet URLLCs  
For the simulation of proposed RFI-aided URLLCs with short packet, we drew on potential deployment schemes for URLLCs from existing works [28] to establish typical application scenarios. For simplicity but without loss of generality, a 2D scenario is considered in Figure 8. The BS located at \((0,100)\) and two smart devices were involved. In our simulation setup, Device 2 was located at fixed position \((57.7,0)\) and Device 1 could move along the \(x\) axis. To acquire a general analytical framework of the error probability bound, an additive Gaussian channel and Device 2 were assumed in first stage. Our system was configured with 20 MHz bandwidth. The error probability definition in (23) shows that, if the BS acquires perfect channel state information, then \(P_{Error}\) converges to the outage probability, which can be defined as  
\[P_{Error}=\mathbb{P}\bigg{[}\log\bigg{(}1+\frac{\rho h^{2}}{\sigma^{2}} \bigg{)}<R\bigg{]} \tag{39}\]  
where \(h\) denotes the channel gain, and \(R\) represents the upper bound of information rates. Lastly, for the joint RFI-aided URLLCs framework, we could obtain total error probability by  
\[P=1-P_{RFI}(1-\mathbb{P}\bigg{[}\log\bigg{(}1+\frac{\rho h^{2}}{\sigma^{2}} \bigg{)}<R\bigg{]}) \tag{40}\]  
where \(P_{RFI}\) represents error detection probability, and \(P\) denotes total system error probability. RFI accuracy performance impacts general system reliability. We achieved physical access without any additional overload, and all those time-frequency resources could be utilized to transmit useful information.  
As shown in Figure 9a, theoretical outage probability and approximation methods were compared. We assumed that they all had the same received SNR. Results demonstrate that the saddle-point approximation matched well with Monte Carlo testing. With the increase in number of antennas \(M\), there was a gradual differencein those methods due to the estimation error except saddle-point approximation. Figure 9b shows the relationship between \(R\) and error probability. Specifically, a fixed number of antennas \(M=128\) is considered. Only the saddle-point method was an exact actual random coding union (RCU) bound. In summary, outage probability and normal approximation do not always provide good estimations of the error probability achievable in URLLCs with massive MIMO over  
Figure 8: Simulation scenario with BS located at \((0,100)\) and assisted by massive MIMO serving two different intelligent devices.
## 3 Challenges in Monitoring Traffic Conditions in Real-time Using Loop Detector Data
### Locating the Back of the Queue Using Segment Travel Times  
Figure 3a shows the travel time (_i.e._, average travel times across all lanes) reported by INRIX along the 2.87 miles of segment near the RSRB toll plaza observed on 15 May 2014. The length of the segment is greater than the longest tail of the queue originating from the toll plaza and encompasses links whose travel times were potentially affected by the changes made at the toll plaza. Figure 3b shows the cumulative travel time at time 7:10 a.m. on 15 May \(C\)\((d,07:10)\) with respect to the distance from the toll plaza. The horizontal distances between the white circles were predetermined by INRIX. The horizontal distances between links in the 5-mile segment are shown in Figure 3b. The \(y\)-axis represents the cumulative travel times from the TP. The slope of the curve represents the changes in time to travel the additional distance towards the toll plaza. The slope of \(C\)\((d,07:40)\) (see Figure 3c) is steep until it reaches 2.87 miles upstream of the TP, where it rapidly decreases. This indicates that the tail of the queue from the TP resided somewhere downstream of the segment, represented by the black circle labeled \(P\), by 7:40 a.m. \(P\) divides the affected region from the unaffected region further upstream. Figure 3d,e shows the evolution of the location of \(P\) with respect to time, and this is how the length of the segment used in the analysis was determined.  
Figure 3: \(Cont.\)  
After locating the tail of the queue, an input-output diagram [15] was constructed using the travel times. Since there are both on- and off-ramps within the segment, the arrival curve, constructed based on the travel times and the discharge rate the toll plaza, can be either greater or less than the actual arrival curve. However, as demonstrated below, the reconstructed input-output diagram using the traffic count and segment travel time provides sufficiently accurate information for the purpose of developing a proxy measure for dynamically determining toll staffing strategy.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 3 Proposed Approach
### Proposed Architecture
#### 3.1.1 Domains and Entities  
Figure 7 presents a more detailed perspective of Figure 6. The proposed approach can be separated into three domains: cellular network, ITS centre and ITS-G5 network.  
**Cellular network**: In this domain, the entities (physical computational nodes) are the smartphones used by soft-mobility users and legacy vehicle drivers. The mobile app can receive and verify messages as well as disseminate protected messages. Technologies such as 5G and 6G can be used to fulfil reliability and low-latency requirements on information exchange [11; 37; 38].
**ITS centre**: Represents an ITS central system. It is composed of a \"server\" entity that hosts two main services: The CA service, which is crucial for user registration, cryptographic material exchange and security updates, among others, and the pub/sub broker, where the pub/sub communication pattern suits the hybrid C-ITS environment since decoupled and asynchronous communications are desired, and multiple producers/consumers exist [39]. Thus, a broker is critical to enable the ITS centre to flow data bi-directionally. Furthermore, the pub/sub model has been used as a powerful tool to develop many distributed applications; it suits the paradigm of edge computing and fog computing (in which this work is embedded), as we are dealing with latency-sensitive applications [17] and resource-constrained devices.
**ITS-G5 network**: Its entities are the ITS stations. The applications of OBUs and RSUs allow them to communicate via G5. The RSUs have direct communication with the ITS centre, thus being the connection point between the cellular and G5 networks.  
The padlock label in Figure 7 indicates a secure exchange of messages using MFSPV [14] or DLAPP [13]. Therefore, each application must implement these protocols, as they will introduce security guarantees. Note that during message exchanges, only one protocol is used. The configuration of which protocol to use occurs at the initialisation of each app.
###### Abstract  
In order to solve the control problem of the solid oxide fuel cell(SOFC), a novel adaptive tracking constrained control strategy based on a Wiener-type neural network is proposed in this paper. The working principle of SOFC is introduced, and the dynamical model of SOFC is studied. Besides, a Wiener model formulation for SOFC is proposed to approximate the nonlinear dynamics of the system, and an adaptive Wiener model identification method is utilized to identify the parameters of the model. Moreover, an adaptive exponential PID controller is designed to keep the stack output voltage stable. Meanwhile, the saturation problem is considered in the paper including input magnitude and rate constraints. Additionally, an anti-windup compensator is employed to eliminate the abominable influence of the saturation problem. Then, the stability of the control plant is analyzed and proven via the Lyapunov function. Finally, the simulation based on the MATLAB/Simulink environment is carried out, and the conventional PID controller is added and simulated as a contrast to verify the control performance of the proposed control algorithm. The results indicate that the proposed control algorithm possesses favorable control performance when dealing with nonlinear systems with complex dynamics.  
adaptive tracking control; Wiener-type neural network model identification; dynamic anti-windup; exponential PID control; solid oxide fuel cells
## 3 The Principle of the Leak Location Method Based on the AE Signal Energy Attenuation
### Multilayer Perceptron Neural Networks Classifier  
The mathematical model of the biological neural network is defined as the artificial neural network [33]. The relationships between the input and output vectors can be obtained after the artificial neural networks are trained. Neural network can be applied to solve nonlinear problems, MLPNN is a commonly used model. Figure 4 shows the perceptron neural networks with three layers. The input and output layers accept the external input vectors in the learning mode. Every neuron in the input  
Figure 3: Group speed dispersive curves of the flexural modes in the frequency band 0–400 kHz for the given gas pipe.  
and hidden layers connects to every neuron in the hidden and output layers, respectively. The input layer \(L_{1}\) is comprised of \(P\) variables \(x=[x_{1},\cdots,x_{P}]^{T}\), which are processed by the following equation:  
\[a_{j}=\sum_{i=1}^{P}\omega_{ij}^{(1)}x_{i}+\omega_{0}^{(1)},\ j=1,\cdots,M, \tag{14}\]  
where \(M\) is the number of neurons in the hidden layer, and the superscript indicates that the parameter \(\omega_{ij}^{(1)}\) is the connection weight between the neuron \(x_{i}\) in the input layer \(L_{1}\) and the neuron \(z_{j}\) in the hidden layer \(L_{2}\). The parameter \(\omega_{0}^{(1)}\) represents the bias of the variable \(a_{j}\), which prevents \(a_{j}\) from becoming zero. Then \(a_{j}\) is transformed by an activation function, which is the logistic sigmoid function in this study, and it is given by:  
\[z_{j}=f\big{(}a_{j}\big{)}=\frac{1}{1+e^{-a_{j}}}. \tag{15}\]  
Similarly, the neuron \(y\) in the output layer \(L_{3}\) receives the variables \(z_{j}\) as shown in the following equation:  
\[y=\sum_{j=1}^{M}\omega_{j}^{(2)}z_{j}+\omega_{0}^{(2)}, \tag{16}\]  
where \(\omega_{j}^{(2)}\) and \(\omega_{0}^{(2)}\) are the connection weight and the bias in the hidden layer, respectively. For the regression problem, the aim of the training process is to minimize the squared error function as shown in the following equation by the gradient descent method:  
\[E(\omega)=\frac{\big{|}y(\omega)-d\big{|}^{2}}{2}, \tag{17}\]  
where \(d\) is the distance between the vibration source and one sensor, \(\omega\) is the set of all weights and biases, and \(y(\omega)\) represents the neuron in the output layer. All the weights and biases need to be updated by training the MLPNN on labeled data until the error function becomes small enough or the maximum iteration is reached; then the MLPNN can be used as a leakage locator.