esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### System Measurement Index  
SOA uses the output \(SNR\) as the system measurement index [44]. When the noise component is less, the \(SNR\) is larger, and the effect of the SR system is better. The \(SNR\) output can be explained as follows [45; 46; 47]  
\[X(k)=\sum_{n=1}^{N}x(n)^{e-j2\pi(k-1)(n-1)/N},1\leq k\leq N \tag{9}\]  
where \(x(n)\) is the output signal of the SR system, \(X(k)\) is obtained by Fourier transform and \(n\) is the signal length.  
\[Y(k)=\frac{2|X(k)|}{N},1\leq k\leq\frac{N}{2} \tag{10}\]  
\[K_{0}=\frac{f_{\max}\times N}{f_{s}}+1 \tag{11}\]  
At the signal frequency \(f_{\max}\), \(K_{0}\) is the peak number of its spectrum and \(f_{s}\) is the sampling frequency. \(Y(k)\) is the magnitude of the signal amplitude spectrum. \(SNR\) can be expressed as follows:  
\[SNR=10\lg\frac{Y(K_{0})}{\sum_{k=1}^{N/2}Y(k)-Y(K_{0})} \tag{12}\]
## 7 Numerical Study
### Time Efficiency of the Optimization Model  
In this section, we analyze computation times of the solution algorithm applied in our optimization model based on the CPLEX solver. Table 5 summarizes the notations that are used in this section.  
Tables 6-8 present the averaged results illustrating time efficiency of the optimization model for small, medium, and large networks. The meaning of the letters A, B, C, and D in the columns' headers is the same as in the previous section. Additionally, the unit of the frame size remains the same, i.e., all of the frame sizes are expressed in the number of time slots (note that, in the case of the linear relaxation of the problem, the number of time slots does not have to be integer).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **A** & **B** & **C** & **D** \\ \hline \(T^{LR}\) & 42.4 & 30.2 & 21.2 & 21.1 \\ \(T^{MIP}\) & 54.7 & 36.9 & 28.7 & 28.4 \\ \(|\mathcal{C}|\) & 13.4 & 24.7 & 42.2 & 41.3 \\ \(t^{MP}\) & 1.6 s & 2.9 s & 6.2 s & 6.1 s \\ \(t^{PP}\) & 4.5 s & 51.1 s & 9 m 49 s & 2 m 48 s \\ \(t^{MIP}\) & 0.4 s & 0.5 s & 0.6 s & 0.7 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimization model efficiency-small networks.  
Figure 4: Frame size as a function of the network size.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(T^{LR}\) & optimal frame size obtained from the linear relaxation of the problem after the c-sets \\  & generation process \\ \(T^{MIP}\) & optimal frame size obtained from frame size minimization \\ \(|\mathcal{C}|\) & number of generated c-sets \\ \(t^{MP}\) & total computation time of solving master problem during c-sets generation process \\ \(t^{PP}\) & total computation time of solving pricing problem during c-sets generation process \\ \(t^{MIP}\) & computation time of solving the final MIP version of the problem \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numerical results—explanation.  
We first note that each problem variant, even for large networks, was solved in a reasonable time. However, the time that is needed to solve the problem increases significantly with the network size. Second, including MCSs assignment and TPC leads to a substantial increase of the number of generated c-sets. This suggests that it is the increased number of simultaneous transmissions possible that lead to the gain in traffic throughput discussed in the previous section. Finally, the quality of linear relaxation is not too high (contrary to the basic optimization model from [14]). This issue should be addressed in the future work, since it affects the effectiveness of the branch-and-bound (B&B) algorithm underlying the CPLEX solver.
## 4 Experimental Results and Discussion  
In this section we discuss the results obtained with our experiments. The performance of NTSA is analyzed and compared with TEA and its latest variations XTEA and XXTEA. A network with LPWAN and IoT infrastructures was set up in our lab. NTSA, TEA, XTEA and XXTEA algorithms were implemented in embedded devices. System architecture similar to [51] was used for the experimental set up. IoT configured mobile devices connected with LPWAN and interfaced with IoT cloud via the IoT gateway was set up. The text files were then transmitted from these devices to the IoT cloud configured in a mobile device through the IoT gateway platform. The text files were then stored in the database in the cloud server. The text files were then encrypted using the four algorithms separately in four different scenarios and were send to the IoT configured mobile devices. The encryption and decryption times for each algorithm was measured for varying file sizes and key sizes.
## 3 Architecture
### Task Execution Time and Energy Model  
#### 3.5.1 Execution Time on Mobile Devices  
If the subtask \(m\) of the task \(k\) is executed on the mobile device, that is \(D_{bm}=0\), and the execution time \(T_{bm}^{l}\) of the subtask \(m\) has a strong relationship with the task size \(w_{bm}\) and the CPU capacity \(f_{l}\) of the mobile device. The execution time \(T_{bm}^{l}\) of the subtask \(m\) can be expressed as follows:  
\[T_{bm}^{l}=\frac{w_{bm}}{f_{l}} \tag{1}\]
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 3 Methodology
### Noise Transfer Model of Differential Amplifier  
The noise from the differential amplifier is not located in the feedback loop. The noise transfer function of the differential amplifier equivalent to the input is essentially the inverse of the transfer function \(H_{a_{i}}^{U}(s)\). The noise transfer function of differential amplifier can be expressed as follows:  
\[H_{a_{d}}^{a_{i}}(s)=\frac{1}{H_{a_{i}}^{U}(s)}\approx\frac{H_{v}^{v}(s)K_{i}^ {a}K_{f}}{K_{m}}. \tag{41}\]From the Equation (41), it can be observed that the amplitude-frequency characteristic curve of the differential amplifier's noise transfer function is as follows:  
\[|H_{n_{da}}^{a_{i}}(s)|\approx 20\log(\frac{|H_{y}^{v}(s)|K_{i}^{a}K_{f}}{K_{m}}). \tag{42}\]  
Reducing the feedback loop gain (such as the PID controller gain \(|H_{y}^{v}(s)|\), \(K_{i}^{a}\), and \(K_{f}\)) while increasing \(K_{m}\) can help decrease the transfer function gain. The controller transfer function \(H_{y}^{v}(s)\) primarily dictates the amplitude-frequency characteristics of the differential amplifier noise transfer function. The characteristic curve decreases monotonically at first and then increases, with a corner frequency at \(w_{da,c}\):  
\[w_{da,c}=\sqrt{\frac{1}{\tau C_{1}R_{2}}}. \tag{43}\]  
When \(\omega=\omega_{da,c}\), the gain of the differential amplifier noise transfer function is as follows:  
\[\left|H_{n_{da}}^{a_{i}}(\omega=\omega_{da,c})\right|\approx 20\log(\frac{K_{i} ^{a}K_{f}}{R_{1}K_{m}})\;\text{dB}. \tag{44}\]  
And when \(w<w_{da,c}\), the amplitude-frequency characteristic curve can be approximated by a \(-20\) dB/dec asymptotic line. When \(w>w_{da,c}\), the curve follows a \(20\) dB/dec asymptotic line. The frequency at which the gain is \(0\) dB can be expressed as follows:  
\[w_{da,z}=\frac{1}{2C_{1}K_{i}^{a}}+\sqrt{\frac{1}{4{K_{i}^{a}}^{2}{C_{1}}^{2}} -\frac{R_{1}+1}{R_{1}R_{2}C_{1}\tau}}. \tag{45}\]
# Exploiting the Value of Active and Multifunctional Facade Technology through the IoT and AI
## 4 An IoT-Based Multifunction Facade Prototype
### IoT Sensors for Facade  
Several sensors are integrated in the building facade to monitor environmental parameters and regulate technologies automatically. Specifically, the IoT system is a proprietary kit [40] composed of three sensor nodes designed for a curtain wall facade for a full integration with an edge-AI to trig data transmission and optimize data collection. The kit is currently energy supplied by wire, and it communicates data collected via LoRaWan to a Modberry gateway. The LoraWan protocol was adopted due to a low energy consumption need and the low data rate for monitoring environmental trends. As a Modbus IoT kit system, as reported in Figure 5, IoT kit nodes are called Master, Slave 1, and Slave 2: the \"Master\" node is integrated in the lower transom of the vision part (not openable), and it monitors the transom temperature, glass temperature, and internal radiation passedthrough glass; the \"Slave 1\" node is integrated in the mullion at 140 cm from the interior floor and monitors indoor parameters and mullion temperature; the \"Slave 2\" node is installed into the mullion within the vertical and monitors outdoor parameters. The kit was designed to match sensor requirements for dimension, range, precision, and cost so to have a cost-effective, low-cost, and aesthetically appealing solution in line with curtain wall facade market expectations. Bespoke PCB have been designed, manufactured, and integrated within a 3D powder-bed fusion case. The selection of these sensors was established to support the data collection, analysis, and utilization of parameters to enable users' multidomain comfort and energy efficiency. The list of sensors integrated in the kits is shown in Table 2.
## 1 Introduction
### Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline
**Feature** & **Ref. [9]** & **Ref. [10]** & **This Paper** \\ \hline Input Type & Single-phase AC & Single-wire earth return & Single-phase 220 V AC \\ \hline DC Link Usage & No & Yes & Yes, 750 V DC link \\ \hline Control Strategy & Passive rectification + inverter & Unified PQ controller & PI + PR control with notch filter \\ \hline Validation Method & Simulation + partial & & \\ experiment & Simulation & Simulation + field experiment \\ \hline Load Conditions & Fixed frequency motor loads & Unbalanced voltage systems & Nonlinear, unbalanced, and motor loads \\ \hline Target System & Small industrial systems & Rural electrification & Low-voltage distribution system \\ \hline Semiconductor & Si-based devices & IGBT & SiC-MOSFET \\ \hline Application & Economic converter topology & PQ compensation in SWER & Three-phase supply for LVDS with PQ assurance \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison with previous papers. however, the methodologies adopted are based on theoretical modeling and simulation frameworks focused on distributed generation systems. Moreover, recent studies have focused on enhancing the accuracy of dynamic state estimation in PDSs by modeling nonlinear phenomena such as transformer core saturation, which can support a more precise assessment of system conditions under transient events [19]. Based on the aforementioned studies, it is necessary to investigate further how a practical power semiconductor-based converter interacts with actual LVDSs under real operating conditions.
## 3 Firefighting Robots
### Prevention  
As already explained, prevention is considered the first step of firefighting and encompasses two classes of actions. The first ones involve social activities that seek to prevent fires from occurring, usually developing awareness campaigns targeting key groups as farmers or tourists. The second ones group multiple works on vegetation to reduce the risk of fire and generate discontinuities to difficult their propagation. Logically, the potential of robots to improve current results is higher in these latter operations. The preparation of vegetation is an activity that requires remarkable efforts, where a lack of human and material resources is perceived. Robotic technologies can make this activity more efficient in two ways.  
On the one hand, drones can take aerial images that can be used to plan these tasks: detecting the most problematic areas, selecting the vegetation to remove, planning routes for its extraction, etc. Some techniques developed for precision agriculture can be applied in this context [12], such as the detection and identification of plants and trees in high-resolution images [13], three-dimensional LIDAR scans [14], and multispectral images [15] acquired by drones. In all these tasks, drones have been revealed as a suitable alternative to satellites, since they offer greater availability at a lower cost, as well as they are less dependent on the weather conditions in the area of interest [16].  
On the other hand, ground robots can support the activities aimed at remove vegetation in forests, playing an intermediate role between the manual labor of firefighters and the heavy machinery used by them. These robots can reach a compromise between the flexibility and precision of firefighters and the quickness and performance of machinery. Forestry and agricultural robots share some challenges and requirements [17], such as the locomotion in rough terrains, localization and mapping in unstructured environments, and planning under uncertainty [18].  
A comprehensive fire prevention solution is being developed in the SEMFIRE Project [19], which proposes a multi-robot system to reduce the fuel accumulation in forests and assist in landscing maintenance. This system consists of small flying robots for vegetation mapping and large-sized tracked mobile robots for forestry mulching.
# Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition
## 4 Experimental Test and Comparison
### Comprehensive Comparison Results  
The final experimental results are shown in Figure 17:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SI** & **GMS** & **SI-GMS (Average)** \\ \hline
**Single template** & \(<\)10 ms & 53 ms & 27 ms \\ \hline
**Ten templates** & 58 ms & 624 ms & 76 ms \\ \hline
**Twenty templates** & 110 ms & 1335 ms & 148 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the execution time.  
Figure 16: The number of the successfully matched feature points of these two images is only three, whereas the value of \(\beta^{\prime}\) calculated by the scanline intensity algorithm is 0.0743.  
Figure 17: Experimental results and the comparison of different navigation schemes. (**a**) Comparisons of algorithmic results in experiment 1. (**b**) Comparisons of algorithmic results in experiment 2.  
As shown in Figure 17, the black curve is the true UAV trajectory, the blue curve is the trajectory measured by pure INS, the purple curve is the trajectory of the results after using the SeqSLAM algorithm, and the green curve is the trajectory measured by the proposed brain-like navigation scheme. We can see that the positioning error of INS is accumulated when it works. However, after place cell recognition and position correction, the accumulation of INS errors could be effectively reduced, thus the navigation accuracy is improved. In experiment 1, according to the high-precision GPS reference information, the actual coordinates at the terminus are 38.017588\({}^{\circ}\)N, 112.44490\({}^{\circ}\)E, whereas the coordinates calculated by pure INS are 38.017921\({}^{\circ}\)N, 112. 44493\({}^{\circ}\)E, and the error distance is about 37.2 m. Meanwhile, by using the proposed brain-like navigation scheme, the coordinates at the terminus calculated are 38.017513\({}^{\circ}\)N, 1122.44489\({}^{\circ}\)E, and the error distance is only 8.4 m. It turns out that the navigation accuracy of INS has been improved by about 28.8 m per 500 m. As for experiment 2, the value of this data is about 16.6 m per 500 m. Tables 5 and 6 gives the latitude and longitude information of two groups of experiment terminus, and Table 7 shows the error comparison of several methods.  
It is obvious that the positioning accuracy can be improved by using the proposed brain-like navigation scheme compared to the pure vision-based algorithm and pure INS.
## 3 The Proposed Algorithm  
The procedure that a CF algorithm typically follows, when predicting a rating for user \(U\) includes three main steps:  
1. Find users having close/similar tastes with \(U\), by examining the similarity of already submitted ratings in the rDB, to identify \(U\)'s near neighbour (NN) users; these users will operate as recommenders to \(U\). Typically, in CF systems, the metrics used to quantify user similarity, is the Pearson correlation coefficient (PCC) and the Cosine Similarity (CS) [55; 56], which are expressed as shown in Equations (1) and (2), respectively:  
\[sim\_PCC(U,\ V)=\frac{\sum_{k}(r_{UL}-\overline{r_{U}})\cdot(r_{V,k}-\overline{r_ {V}})}{\sqrt{\sum_{k}(r_{UL,k}-\overline{r_{n}})^{2}\cdot\sum_{k}(r_{V,k}- \overline{r_{V}})^{2}}}, \tag{1}\]  
\[sim\_CS(U,\ V)=\frac{\sum_{k}r_{UL}\cdot r_{V,k}}{\sqrt{\sum_{k}(r_{UL,k})^{2} \cdot\sqrt{\sum_{k}(r_{V,k})^{2}}}}. \tag{2}\]  
Generally, for a user \(V\) to be considered as \(U\)'s NN, their quantified rating similarity value has to exceed a specific threshold, e.g., the value 0.0 for the PCC metric [55].  
2. Predict the rating value that \(U\) would give to an item \(i\); in order to compute the rating prediction \(p_{U,i}\), the standard CF rating prediction formula [26; 57] is typically applied:  
\[p_{U,i}=\overline{r_{u}}+\frac{\sum_{V\in NN_{u}}sim(U,\ V)\cdot(r_{V,i}- \overline{r_{V}})}{\sum_{V\in NN_{u}}sim(U,\ V)}. \tag{3}\]  
The weight/importance of each NN to the prediction value is based only on its numeric similarity with \(U\), calculated during the previous step.  
3. Recommend to \(U\) the items having the highest prediction values; the number of recommended items is determined by the administrator of the recommender system [58; 59].  
The proposed algorithm targets at augmenting the importance of each NN \(V\), when \(V\) and \(U\) (a) mutually agree on their opinion on some items and (b) their opinion on the same items deviates from that of the majority of users; to this end, the proposed algorithm adjusts the rating prediction formula given in the second step above (Equation (3)).  
More specifically, the proposed algorithm modifies Equation (3) by considering a _black sheep factor bsf(U, \(V\))_ between user \(U\), for whom the rating prediction is computed, and each of his NNs, \(V\), as shown in Equation (4):  
\[p_{U,i}=\overline{r_{u}}+\frac{\sum_{V\in NN_{u}}sim(U,\ V)\cdot bsb(U,\ V)*(r_{V,i}- \overline{r_{V}})}{\sum_{V\in NN_{u}}sim(U,\ V)\cdot bsb(U,\ V)}, \tag{4}\]  
Effectively, the _bsf(U,V)_ factor is an adjustment assigned to each NN's contribution to the prediction computation, based on the degree to which users \(U\) and \(V\) mutually agree on the rating of items, while at the same time disagreeing with the majority of other users on the same items.  
For the application of this algorithm, the _bsf(U,V)_ factor needs to be determined; the setting of the _bsf_ factor to its optimal value is experimentally explored in the following section, along with the prediction accuracy gains of the proposed algorithm.
# Study of Deformation and Breakup of Submillimeter Droplets' Spray in a Supersonic Nozzle Flow
## 1 Introduction
Still, it showed that the final size distribution, in general, conforms to the expected one, derived from the existing breakup models. Results provided by [PERSON] and [PERSON] [19] and further developed in [21], showed that under supersonic and overheating conditions, droplets exhibit breakup stages and morphology in some aspects different from commonly adopted. In the work [19], authors investigated a dynamics, breakup and vaporization of single volatile (overheated) and non-volatile small droplets in the underexpanded jet at supersonic gas-to-droplets relative Mach numbers. Droplet's maximum Weber number in this experiment varied from 200 to 280 for different liquids, covering _We_ range, typically associated with sheet-stripping breakup regime. One of the important features of this experiment was that droplets' _We_ number varied significantly during acceleration in the air flow: in the initial acceleration region, _We_ remained in the low range but quickly increased to the values of We > 100. One of the main conclusions brought by the authors was that for different test liquids droplet breakup followed a similar pattern that included four subsequent stages: the initial deformation of the droplet, sheet stripping, 'primary breakup' and a catastrophic breakup. Although not mentioned by the authors, the primary breakup could arguably be regarded as the transition phase from sheet stripping to the catastrophic regime. Authors also noted that the _We_ at which the mentioned breakup regimes occur are generally higher than those reported previously for shock-tube experiments, and the total breakup times are an order of magnitude shorter than would be expected in shock-tube flows for similar Weber numbers. In the present paper, a study of dynamics and breakup of submillimeter droplets, resulting from the primary breakup of the liquid jet, in a flow accelerating from subsonic to supersonic speed in a converging-diverging nozzle is reported. The work is focused on the deformation and breakup of small droplets and related flow characteristics at different regions of the flow. The motivation for the work was to bridge the gap in the experimental data for small droplets behavior at low, near-critical _We_ numbers in continuous accelerating flow.
## 4 Simulation Results
### Discussion  
In this section, we discuss the differences between GB-PEDAP, TBEEP, GSTEB, and PEDAP, which are shown in Table 2. The hierarchical architecture of GB-PEDAP is two layers, whereas those of the other protocols are single-layer architectures. The data transmission of GB-PEDAP contains direct transmission and tree-path transmission. The energy efficiency of GB-PEDAP is the best. The uniformity of remaining energy of GB-PEDAP is very good.  
In TBEEP, the sensing area is divided into three sections. The sensor with the highest remaining energy in the first section is preferentially selected as the head as it is closer to the BS. The nodes in the second and third sections mainly transmit data to the head of the first section to the BS through the head. During the data transmission, if all the sensors in the first section are in the dead state, the head will continue to be selected for transmission to the second section until all nodes die. Due to the head selection mechanism of TBEEP,  
Figure 7: Energy distribution of sensing area when the first node dies: (**a**) GB-PEDAP; (**b**) TBEEP; (**c**) GSTEB; (**d**) PEDAP.  
the energy consumption of the nodes that are closer to the BS is higher. Therefore, the remaining energy of sensors closer to the BS is lower, while the energy of nodes farther from the BS is higher. GSTEB selects the sensor with the highest remaining energy in the sensing area to be the root node. Each node finds the parent node according to the distance required to form a tree-like structure. Although the scheme has a strategy of balancing energy, it is possible that a node far from the BS could be selected as the root node. In this case, the energy of the node (acting as a root node) would be consumed quickly. Since the tree construction of PEDAP does not consider the remaining energy of the current sensors, PEDAP would make the energy distribution relatively unstable. However, the proposed GB-PEDAP mainly builds a grid structure in the sensing area, thus balancing energy consumption by using the cell head mechanism. Using [PERSON]'s algorithm for a tree structure also helps to reduce the cost of the overall transmission path.