esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 OFDM Signal Identification Based on Inherent Features
### High-Order Cumulant-Based OFDM Signal Detection
 \tag{39}\]  
In the expression provided, \(s_{n}\) is the transmitted signal, \(f_{c}\) is the carrier frequency, and \(\varphi_{0}\) is the initial phase. Consequently, the fourth-order cumulant of the said signal is expressed as \[C_{4x}[r(t)]=\frac{1}{\sqrt{N_{\text{SC}}^{4}}}\sum_{n=-N_{\text{SC}}/2}^{N_{\text{ SC}}/2}C_{4x}\left[\sum_{k=1}^{\infty}s(t)\right]. \tag{40}\]  
From the aforementioned expression, it can be observed that due to the independence of the subcarriers in OFDM, the fourth-order cumulant of an OFDM signal is reduced by a factor of \(\frac{1}{N_{\text{SC}}}\)) compared to that of a single-carrier system. In this section, we differentiate between OFDM and single-carrier signals through the use of eigenvalue \(d_{1}\) for detection and discrimination purposes. \[d_{1}=\frac{C_{42}}{C_{21}^{2}}=2(k_{20}-1)=2\left(\frac{M_{42}}{M_{21}^{2}}-1 \right). \tag{41}\]  
As illustrated in Table 1, the theoretical eigenvalue \(d_{1}\) for single-carrier and OFDM signals is presented. It is evident that the \(d_{1}\) feature of OFDM signals is distinctly different from that of single-carrier signals, allowing for their identification through the establishment of decision thresholds. The procedural flow of the OFDM signal detection algorithm based on higher-order cumulants is depicted in Algorithm 1. ```
0: receive signal \(r(n)\);
0: Detection Outcome: Differentiation Between OFDM Signal and Single-Carrier Signal;
1: Set the identification threshold \(\lambda_{mul}\) as indicated in Table 1;
2: Calculate the higher-order moments \(M_{42}\) and \(M_{21}\) of the received signal in accordance with Equation (34);
3: Compute the feature value \(d_{1}\) based on Equation (41). 4:if\(d_{1}<\lambda_{mul}\)then
5: Identify the signal as an OFDM signal and output the identification result
6:else
7: Identify the signal as a single-carrier signal and output the identification outcome. 8:endif
```  
**Algorithm 1** Procedure Flow of the OFDM Signal Detection Algorithm Based on Higher-Order Cumulants
## 2 Related Work
### Controllable Mobility Aided Routing Protocols
Collected data include video frames that are encoded and streamed to the sink for further analysis along with other user data. For example, video frames can be encoded using MPEG-4 technology that can control the output data rate from 9.6 Kbps to 1.024 Mbps [31]. Mobile sensors and robotic nodes are both equipped with a high bandwidth communication device. They can use IEEE 802.11b/a/g based communication devices which can provide a data rate of 1-54 Mbps (can be higher than 100 Mbps with IEEE 802.11n). It is also assumed that each node can know its geographic position using positioning systems, such as global positioning system (GPS) or other localization technologies [32, 33]. Note that RoCoMAR can work only using the relative positions of nodes. For example, if a RN is equipped with devices (e.g., laser radars and beam lasers) that can measure the distance and direction to other nodes, it can readily approach the midpoint position between two nodes.
# Exact Relationship between Black Phosphorus Thickness and Behaviors of Field-Effect Transistors
## 2 Materials and Methods  
The preparation of the device was based on the mechanical exfoliation method of thin adhesive tape described in previous reports in detail [28]. The thin layer of BP is obtained from the BP crystal, and the BP sheet is exfoliated to the SiO\({}_{2}\)/Si (285 nm SiO\({}_{2}\)/highly doped n-type Si) substrate by tape. Through the difference of color and transparency under the microscope, the material with light color and high transparency is selected as the experimental material. The silicon substrate before use is rinsed with deionized water and dried with nitrogen gas gun. Standard electron beam lithography (EBL) and metal evaporation are used to prepare electrodes. The two-dimensional material sample was first transferred to a silicon substrate with alignment marks. The sample is placed into a scanning electron microscope (SEM, model FEIF50), and then it is positioned using the alignment markers, and an electrode is written on the sample using a pattern generator. The sample surface is spin-coated with 500 nm thickness of electron beam photoresist PMMA and baked on a hot plate for 90 s (temperature of 180 \({}^{\circ}\)C). Then, the sample is put into the electron beam exposure system and the drawn electrode pattern is exposed. Then, the samples are developed in the developing solution (MIBK:IPA = 1:3) for 1 min, followed by washing with IPA for 1 min and blowing dry with a nitrogen gun. The sample is then placed on an electron beam evaporation table to evaporate the metal electrode (e.g., Ti/Au). After the vaporization, the sample is immersed in acetone, and the PMMA and the metal on the surface are removed by the lift-off process, cleaned in IPA for 5 min, and dried with a nitrogen gun to complete the electrode fabrication. The device preparation process is shown in Figure 1a.  
The schematic diagram of device is shown in Figure 1b. One prepared device is shown in Figure 1c, and the ratio of the length to width of channel of the device is kept approximately at 1.2 during EBL. The electrode part of the device is a chromium/gold electrode (10 nm/20 nm). The electrode materials, electrode thickness, substrate, insulation layer thickness, device preparation method, device preparation conditions, the time between device preparation and test, and the device test conditions used by the five devices are identical, and the devices are not subject to any treatment such as annealing, lighting, etc.  
The electrical characteristics of the device are measured through B1500A Semiconductor Device Parameter Analyzer configured on the Cascade DC probe platform. Optical microscopy and atomic force microscopy (AFM) were used to find layer samples and to determine their thickness. All tests were performed at room temperature. Because all devices have the same process and all materials with thickness above 20 nm have a similar dark green color, Figure 1 only shows the preparation process and thickness measurement process of one device.
# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 2 Problem Statement  
This section formulates the trajectory planning problem for an autonomous vehicle as an OCP. In this formulation, the state variables consist of the vehicle's position \((x,y)\), velocity \(v\), heading angle \(\theta\), and front-wheel steering angle \(\phi\), while the control variables include the acceleration \(a\) and the angular rate of the steering angle \(\omega\).  
The objective of the OCP is to determine an optimal sequence of control inputs that generate a vehicle trajectory that is spatially smooth and dynamically stable while satisfying all imposed constraints--namely, kinematic feasibility, two-point boundary conditions, and collision avoidance. The formulation is detailed in the following subsections.
# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
## 4 Results and Discussion
### Constant Speed Driving Condition and the Influence of Precooling Phase
Similarly, the energy consumption of the batteries during the driving period due to the HVAC system without the adoption of the precooling phase can be calculated by using Equation (45) and changing the time interval to [0-2400 s]. In this case, Equation (45) gives a value of 0.78 kWh, which is about 16% higher than the case with precooling. However, the adoption of the precooling phase requires additional energy consumption during the battery charging, which in this case is 0.42 kWh; therefore, the total amount of energy requested by the HVAC system is 1.08 kWh. In reducing the precooling time to 840 s instead of 1200 s, the total amount of energy required for vehicle cooling (840 s of precooling and 2400 s of driving) decreases to 0.99 kWh, saving about 8.6% of the energy for battery charging. Then, the adoption of the precooling phase reduces the energy consumption during the driving period and increases the overall energy consumption. Therefore, with the model developed, it is possible to predict whether the adoption of the precooling phase gives advantages in terms of energy consumption and comfort conditions.
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 3 Model Architecture
### Bi-Level Routing Attention Mechanism
To prune the association graph, only the top k connections per region are retained, yielding the routing index matrix \(I^{r}\):  
\[I^{r}=topkIndex(A^{r}). \tag{9}\]  
This function \(topkIndex()\) selects the indices of the top k most relevant regions for each region from the adjacency matrix \(I^{r}\). Specifically, it performs a row-wise top-k operation and returns an index matrix \(I^{r}\), where each row contains the indices of the k most relevant regions for that particular region. Using \(I^{r}\), token-to-token attention is confined to the top k regions. The gathered key-value tensors \(K^{g}\) and \(V^{g}\) are reconstructed as follows:  
\[K^{g}=gather(K,I^{r}), \tag{10}\]  
\[V^{g}=gather(V,I^{r}). \tag{11}\]  
This function \(gather()\) collects key-value pairs from the original key \(K\) and value \(V\) tensors according to the index matrix \(I^{r}\). It extracts the keys and values specified by \(I^{r}\) from the entire feature map, enabling the application of the attention mechanism on these regions. The output feature map \(O\) is computed as follows:  
\[O=Attention(Q,K^{g},V^{g})+LCE(V), \tag{12}\]  
where the \(Attention()\) function implements the standard attention mechanism, and \(LCE(V)\)[42] is a local context augmentation term. For few-shot and weak-feature datasets, although deep residual structures can effectively alleviate the vanishing gradient problem and ensure effective feature extraction with limited data, overly deep residual networks are prone to overfitting. Thus, ResNet18 was chosen as the backbone for AugNet. AugODNet_BRA further optimizes AugNet by integrating dynamic convolution and BRA modules. As shown in Figure 5, traditional convolutions in the Res-2 and Res-3 stages were replaced with ODConv, enabling adaptive feature learning. BRA modules were added after Res-2 and Res-3 to enhance cross-scale information flow, particularly for small targets and weak features.
## 4 Experimental Analysis of the Occupied Walkway  
Dynamic field tests were performed under pedestrian excitation, for the two-side supported panel of Figure 5. The experimental measurements were collected in two different periods of the year, with variations in the reference temperature and humidity (see Table 1). In doing so, the test instruments and methods were kept identical.  
The acceleration measurements were performed using six micro electro-mechanical system (MEMS) triaxial accelerometers prototyped in [47]. Their position was aimed at properly capturing the fundamental mode of the system, based also on past studies (see [20] and Figure 7).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Month**} & \multirow{2}{*}{**Test Time**} & \multicolumn{2}{c}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Relative Humidity (\%)**} \\ \cline{4-5}  & & & **Daily (Max-Min)** & **Test (Mean)** & **(Test Time)** \\ \hline P\#1 & March 2019 & 9–10 a.m. & 7.5/15.0 & 13.2 & 76 \\ P\#2 & November 2017 & 8–10 a.m. & 4.1/7.2 & 6.5 & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference configurations for the field vibration tests.  
Figure 6: Reference two-side supported panel along the central nave path: (**a**) General view, with (**b–c**) D1 and D2 details (reproduced from [20] with permission from Elsevier, license n. 4585290441718, May 2019).  
The imposed random vibrations involved up to 20 adults (with \(\approx\)80 kg their average weight). Most of the walking conditions were detected to reproduce realistic operational conditions for the structure, i.e., with visitors moving slowly by small groups, standing on the walkway, etc. In Table 2, the overall setup features are grouped by period of the year (P#_n_, see Table 1), and listed by total number of occupants (\(p\)). A further distinction is provided in terms of occupation density \(d\) (\(p\)/walking area), number of standing/walking persons and qualitative features of movements.  
The tests were carried out with the support of a variable number of male and female volunteers, asked to walk back and forth (or randomly) on the monitored system. The step frequency of normal or consistent walks was established in the conventional range of 1.5-2.5 Hz. In some cases, additional measurements were carried out with a single occupant standing on the structure, by imposing an impulsive vibration (in-place jump). Special care was hence spent for the analysis of major dynamic effects due to (a) unsynchronized motion of the occupants (\(p\geq 2\)), (b) normal or consistent walk of the occupants, (c) regular (i.e., longitudinally to the central wave, back and worth) or irregular (i.e., random) walking path.  
Figure 7: First vibration modes and frequencies of the empty structure (ABAQUS, axonometric and bottom view). Numerical study derived from the FE model presented in [20].
###### Abstract  
On November 2014, the Municipality of Grandate, near Lake Como, had to deal with a great emergency that was caused by the flooding of factory undergrounds. The authors realized a hydrogeological study to understand the causes of groundwater flooding and to prepare a pre-feasibility study concerning possible actions for groundwater control. The hydrogeological structure is rather complex and required time-consuming reconstruction of the conceptual site model. A transient numerical model was developed to analyse the system behaviour in different scenarios. The flow model was calibrated in a steady and unsteady-state using the automatic calibration code Model-Independent Parameter Estimation (PEST). The study demonstrated that the reason for floods was mainly due to the concurrence of three causes: (1) the hydrogeological structure of the area was recognized as a stagnation zone, (2) groundwater rising, and (3) extremely heavy rainfall in 2014. Through the PEST RandPar function, 100 random rainfall scenarios were generated starting from rainfall data for the last 20 years. The model was used to run 100 1-year long simulations considering the probability distribution of recharge related to the 100 randomly generated rainfall scenarios. Through collecting the piezometric heads that resulted from the simulations, monthly probability curves of groundwater exceeding a threshold level were obtained. The results provided an occurrence probability of groundwater level exceeding the underground structures level between 12% and 15%.  
Keywords:stagnation areas; groundwater rising; mathematical modeling; stochastic analysis; MODFLOW +
Footnote †: journal: Applied Sciences
#### Article
###### Abstract
As was mentioned at the beginning of this section, the author derives the ANN to code a stochastic reflection coefficient of ordinary patch antennas concerning random design variables under uncertainty associated with the manufacturing process. Separate ANNs are derived to model the mean and the standard deviation of the random reflection coefficient of the ordinary patch antenna. The Stochastic Gradient Descent (SGD) is used to optimize the values of the weights and biases of each ANN. The author implements the algorithm of SGD with Replacement [20]. This approach lets him obtain a better quality ANN model than the approach with the Random Reshuffling, [20; 21], for the type of data variation associated with the analyzed antennas. The learning rate of the ANN is controlled using the ADAM algorithm [22]. It was observed that about 11-20 neurons in the ANN layer are required to keep the average relative approximation error for the training and testing data at about or under 5%. The activation function for each neuron is the ReLu function. The paper is organized as follows. In the second section, the author presents the measurement results of two series of patch antenna arrays and gives the extracted uncertainty of the geometrical design random variables. The third section of the paper presents a novel application of the method from [15] to FDTD-PCE computations, which enables a reduction of the number of required FDTD simulation runs for the computation of the mean and standard deviation of \(\left|\,\right.\)S\({}_{11}\left|\,\right.\) for a predefined set of nominal values of design parameters. The novel results of modeling the stochastic behavior of \(\left|\,\right.\)S\({}_{11}\left|\,\right.\) of ordinary patch antennas by ANNs for the 24.5-28.7 GHz frequency band are presented in Section 4 of the paper. In the last section of the paper, the author gives conclusions and points to future work.
# Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms
## 1 Introduction
In summary, nature inspired algorithms search six gains for the speed tracking of a PMSM. The control system is based on three PI controllers where the gains are tuned using nature-inspired algorithms. The external PI control loop regulates the rotor speed to the desired reference value and sets the reference values for the internal control loop which regulates the currents on the \(d\)-axis and \(q\)-axis of the PMSM (Figure 1). The internal control loop consists of two PI controllers whose tasks are to regulate the \(i_{q}\) current to the value set by the external control loop and keep the \(i_{d}\) current equal to zero. It is important to note that these optimization algorithms are increasingly popular in engineering applications because: (I) they are based on fairly simple concepts and are easy to implement; (II) they do not require gradient information; (III) they can overlook local optimals; (IV) they can be used in a wide range of problems spanning different disciplines. Figure 1: Speed control scheme of a PMSM.
## 4 Proposed Alternating Optimization Algorithm to Minimize MSE
### Optimization of IRS Configuration  
Similarly, if the precoding matrix \(\mathbf{W}^{(r)}\) is provided at the \(r\)-th iteration, the problem \(\mathcal{P}2\) can be simplified to  
\[\mathcal{P}2-b:\min_{\mathbf{V}} \text{MSE}\Big{(}\mathbf{V},\mathbf{W}^{(r)}\Big{)}\] (39) s.t. \[\eqref{eq:p3},\eqref{eq:p3}.\]  
In practice, solving for the auxiliary matrix \(\mathbf{V}\) as an integer is challenging. To address this computational complexity, we adopt a more practical approach by relaxing the values of \(\mathbf{V}\). We constrain each element of \(\mathbf{V}\) to lie between \(0\) and \(1\), and then we restore \(\mathbf{V}\), based on the criterion of minimizing the projection distance.  
The relaxed form of problem \(\mathcal{P}2-b\) is  
\[\mathcal{P}3-b:\min_{\mathbf{V}} \text{MSE}\Big{(}\mathbf{V},\mathbf{W}^{(l)}\Big{)}\] (40) s.t. \[\text{C1}:\sum_{i=1}^{MK}v_{n,i}\leq 1,\ \forall n\in\mathcal{N},\] \[\text{C2}:0\leq v_{n,i}\leq 1,\ \forall i\in\mathcal{I}.\]According to (33), the expression for \(\text{MSE}\Big{(}\mathbf{V},\mathbf{W}^{(t)}\Big{)}\) is the sum of two quadratic functions of \(\mathbf{V}\). Since the sum of convex functions remains convex, \(\text{MSE}\Big{(}\mathbf{V},\mathbf{W}^{(t)}\Big{)}\) is a convex function of \(\mathbf{V}\). Constraint C1 is also convex (affine), while constraint C2 ensures that the elements of \(\mathbf{V}\) are constrained between 0 and 1, forming a convex set. Therefore, problem \(\mathcal{P}3-b\) is convex and can be solved directly using CVX.  
After MSE converges, we can recover the two configuration matrices, \(\mathbf{F}\) and \(\mathbf{R}\), of IRS from \(\mathbf{V}\) solved in problem \(\mathcal{P}3-b\). Let \(\bar{\boldsymbol{v}}_{n}\in\mathbb{R}_{+}^{1\times MK}\) denote the \(n\)-th row of \(\mathbf{V}\), and let \(\boldsymbol{e}_{p}\) be a one-hot row vector of the same dimension as \(\bar{\boldsymbol{v}}_{n}\), with the \(p\)-th element equal to 1. In addition, define \(\boldsymbol{e}_{0}=\mathbf{0}\). The index corresponding to the minimum distance projection of the \(n\)-th IRS unit is  
\[p^{*}=\arg\min_{p}\bigl{\|}\bar{\boldsymbol{v}}_{n}-\boldsymbol{e}_{p}\bigr{\|} _{2}^{2}, \tag{41}\]  
where \(\boldsymbol{e}_{p}\in\{\boldsymbol{e}_{0},\boldsymbol{e}_{1},\cdots, \boldsymbol{e}_{MK}\}\).  
Based on the (29) and Proposition 1, the associated LED and PD can be obtained via  
\[m=\lfloor(p^{*}-1)/N\rfloor+1, \tag{42}\] \[k=\text{mod}(p^{*}-1,N)+1, \tag{43}\]  
where \(p^{*}\in\{0\}\cup\mathcal{I}\).
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Link Layer
#### 5.2.4 Power Regulation Mechanism (PRM)  
The selection of the minimum suitable transmission power level for outgoing packets is managed through a mechanism based on the received signal strength indicator (RSSI). For this purpose, a safety margin for reliable communications is defined by \(\text{RSSI}_{\text{min}}\) and \(\text{RSSI}_{\text{max}}\). If a node is transmitting data packets (or ACKs) to its parent (or child) at a power level making the received RSSI higher than \(\text{RSSI}_{\text{max}}\), it will be asked to decrease it for the next transmission. Similarly, if the received RSSI is lower than \(\text{RSSI}_{\text{min}}\), it will be asked to increase it.  
PRM requests are included in an RSSI control field of data packets and ACK headers. Possible values of this field are: _increase_, _keep_, and _decrease_. Once computed the requests from parent and  
Figure 5: Example of a _staggered wakeup pattern_ in a three-ring LPWAN with a single transmission window performing uplink communications.  
children, the STA determines whether and how to regulate its own power level depending on the following considerations:  
* If one or more STAs ask for a higher value, increase the power level.
* If all STAs ask for a reduction, decrease the power level.
* Otherwise, keep the current power level.  
In addition, if an STA needs to retransmit a packet to its parent, it will also increase the power level in each new transmission window. Regarding the association process, whenever an STA listens to a discovery request, it will answer at maximum power. The STA selected as parent will keep the maximum power level at the beginning and regulate it by following the previously described procedure. Instead, those STAs not selected as parents will set their power back to the level they had before answering to the discovery request.  
In summary, the main advantages of using such a link layer are:  
* Clock synchronization is inherent to TDMA, with nodes being periodically set in time by means of beacons.
* Groups of nodes have their time slots clearly allocated, and collisions within groups are sensibly reduced or even avoided by using CSMA/CA.
* Network overall lifetime is increased by putting nodes in non-active modes for most of the time and only periodically waking up to check for activity.
* Association and routing mechanisms included in HARE also fit for this scheme, so that intermediate and already associated nodes do not have to constantly listen to hypothetical network discovery requests.
* The uplink transmission scheme is suitable for performing data aggregation methods.
* Changes in the network configuration or even new firmware can be easily distributed in a coordinated manner.