esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review of Previous Works
### Active AP Configuration Algorithm for Dual Interfaces
#### 3.3.1 Formulation  
The problem formulation for this algorithm is formulated as follows:  
1. _Inputs:_ * number of hosts: \(H\), * number of APs: \(N\), * estimated throughput between \(AP_{i}\) and \(host_{j}\) for \(i=1\) to \(N\), \(j=1\) to \(H\) at each interface: \(tp_{ij}\), * minimum throughput for the association: \(S\), * number of orthogonal channels (OCs) for each interface: \(C\), * minimum host throughput: \(G\), * available total throughput: \(B^{a}\).
2. _Outputs:_ * set of active APs with dual interfaces, * set of hosts associated with each interface at every active AP, * channel assigned to each interface at every active AP.
3. _Objectives:_ * \(E_{1}\) represents the number of active access points (APs) with the dual interfaces to be minimized under the _minimum host throughput constraint_: \[E_{1}=[\text{number of active APs}]\] (6)
* holding to the first objective, to maximize the _minimum average host throughput_\(E_{2}\): \[E_{2}=\underset{j}{\text{min}}\left[TH_{j}\right]\] (7)  
Figure 4: Example topology of an elastic WLAN system.  
Figure 5: Flowchart of dual AP configuration algorithm.  
where \(TH_{j}\) represents the average host throughput for \(AP_{j}\) that is given by: \[TH_{j}=\frac{1}{\sum\limits_{k}\frac{1}{lp}}\] (8) where \(tp\) represents the link speed between \(node_{j}\) and \(node_{k}\) (\(link_{jk}\)).
* holding to the two objectives, to minimize the _total interfered communication time_\(E_{3}\) for channel assignments: \[E_{3}=\sum\limits_{i=1}^{N}\left[\sum\limits_{\begin{subarray}{c}k\in I_{i}\\ c_{k}=c_{i}\end{subarray}}T_{k}\right]\] (9) where \(T_{k}\) is the \(AP_{i}\) total communication time, \(I_{i}\) represents the set of the APs interfered with \(AP_{i}\), and \(c_{i}\) indicates the channel assigned to \(AP_{i}\).  
* minimum host throughput: the throughput of any host must be larger than or equal to the _minimum host throughput_\(G\) on average when all the hosts are communicating simultaneously.
* total throughput: the total throughput for all the hosts must be smaller than or equal to the _available total throughput_\(B^{a}\).
* channel assignment: each interface of an AP must be assigned a channel.
# IoT Helper: A Lightweight and Extensible Framework for Fast-Prototyping IoT Architectures
## 5 Experiments
### Discussion  
Based on the experimental results reported in the previous sections, we believe that we can provide answers to the questions we intended to investigate.  
First of all, our experiments show that loT Helper performs very well as long as message frequency does not exceed two message per second. In these tasks, even with a relatively high number of sensors to read (up to five in our experiments) or concurrent clients sending commands (also up to five in our experiments), the system performed very well, with very limited delay in data acquisition, low average transaction times to handle commands and no errors.  
We consider the 2 msg/s limit to be very promising. In fact, it is definitely adequate to handle the initial design and fast-prototyping phases of any applications. In addition to this, we notice that many applications in Industry 4.0, even real-time ones, still require handling frequencies that are below this limit; therefore, we may conclude that loT Helper may be a solution in these cases even in a production environment.  
Performance degrades when frequencies rise above two messages per second, or the number of concurrent clients increases above five. Notice, however, that these are quite different problems. In fact, handling a larger number of clients can be easily performed by deploying multiple copies of loT Helper, each of which receives commands from a portion of the clients. Given the relatively inexpensive nature of the framework, this can be considered as a practical solution to the problem of increasing clients.  
Handling very high-frequency acquisitions is more complex. If applications are non-mission critical, i.e., a small percentage of data points (in the order of 5%) can be dropped without impacting the overall functionality of the system, loT Helper can still be a solution. On the contrary, for real-time mission-critical applications with zero-drop requirements, more powerful embedded systems are required in place of Arduino shields.  
Figure 9: Command -mode experiment. Average response time with different clients and different frequencies of requests. We report errors, if any, near each scenario.
# Research and Application of an Improved Sparrow Search Algorithm
## 3 Methods
### Algorithm Performance Testing
#### 3.3.2 AWSSA Compared with Other Algorithms  
The AWSSA, the SSA, and the PSO algorithm were compared for functional optimization. The population size was set to 30, the maximum number of iterations was 500, and the algorithm parameter settings are shown in Table 2. The three algorithms were run on the test function 30 times, and the average value was taken as a reference. Table 3 lists the detailed experimental results. It can be seen from Table 3 that in the unimodal function, the AWSSA achieved optimal results in F1\(\sim\)F4, which was dozens of orders of magnitude ahead of the other algorithms. It was only slightly lower than the SSA on the F5 and F6 functions. On the other hand, it can be observed that the AWSSA was far better than the SSA for the multimodal function F8, although the average values and standard deviations obtained by the AWSSA and the SSA were the same on F9 and F10. The information evident from the convergence curves depicted in Figures 4-13 is that the convergence speed of the AWSSA was better than that of the other algorithms. From the above results, it can be seen that the improvement strategy in this article has a certain effect on improving the convergence ability and optimization ability of the original algorithm.  
In the next section, we describe in detail the application of the AWSSA to multi-objective association rule mining, including the selection of coding rules and fitness functions, as well as its comparison with other algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Function Name** & **Dim** & **Range** & **Min** \\ \hline F1 & Sphere & 30 & [\(-\)100,100] & 0 \\ F2 & Schwefel’s Problem 2.22 & 30 & [\(-\)10,10] & 0 \\ F3 & [PERSON]’s Problem 1.2 & 30 & [\(-\)100,100] & 0 \\ F4 & [PERSON]’s Problem 2.21 & 30 & [\(-\)10,10] & 0 \\ F5 & Generalized Rosenbrock & 30 & [\(-\)30,30] & 0 \\ F6 & Step & 30 & [\(-\)100,100] & 0 \\ F7 & Quartic & 30 & [\(-\)1.28,1.28] & 0 \\ F8 & Generalized Schwefel 2.26 & 4 & [\(-\)500,500] & \(-\)418.9829D \\ F9 & Generalized Rastrigin & 4 & [\(-\)512,512] & 0 \\ F10 & Ackley & 4 & [\(-\)32,32] & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test functions.  
Figure 4: F1 convergence curve.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameters** \\ \hline AWSSA & ST = 0.8, PD = 0.2, SD = 0.1, \(\lambda\) = 2 \\ SSA & ST = 0.8, PD = 0.2, SD = 0.1 \\ PSO & W = 0.9, C1 = 1.49445, C2 = 1.49445 \\ WOA & B = 0.5 \\ SMA & Z = 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters setting of comparison algorithm.
## 2 Results and Discussion
### Adsorbent Synthesis and Characterization
Figure 2: EDX spectrum of PAB2. the weight loss had only an exothermic peak. In this temperature domain, the peak was relatively large, which suggested that only oxidative decomposition occurred. The final residue at 900 \({}^{\circ}\)C was \(\sim\)19%. The thermal behavior of the PAB3 sample in air (Figure 4b) showed an endothermic effect in the range of 60-450 \({}^{\circ}\)C, with a first weight loss of about 30%, followed by a sequence of exothermic effects in the range of 450-800 \({}^{\circ}\)C, with a weight loss of about 47% due to the oxidative degradation of the PAB3 sample. The total weight loss of the PAB3 sample at 900 \({}^{\circ}\)C was of about 81%. The difference in the thermal behavior of the PAB3 in air (Figure 4b) and in nitrogen (Figure 4a) was suggested by the more accentuated endo and exothermic processes in the air atmosphere. The products PAB1, PAB2, and PAB3 were very stable in the range of temperatures intended for their use as adsorbents (up to \(\sim\)150 \({}^{\circ}\)C). The adsorption of organic compounds is strongly influenced by the morphology and textural properties of the adsorbent: the distribution of surface functional groups, porosity, specific surface area, pore dimensions, and total pore volume [38; 39]. The BET isotherms and BJH distribution of the pore diameters for the PAB1, PAB2, and PAB3 are compared in Figure 5.  
As can be observed in Figure 5a, all the isotherms corresponded to type IV, as categorized by the IUPAC classification, with respect to a hysteresis loop, indicating the uniformity and regularity of the textural properties of all the sorbents. The textural parameters calculated based on the obtained isotherms for the PAB1, PAB2, and PAB3 are summarized in Table 2.  
Figure 4: TG/DTA curves of PAB3 sample in: **(a)** nitrogen atmosphere, **(b)** air atmosphere. Figure 5: BET isotherms **(a)** and BJH distribution of pore diameters **(b)** for the PAB sorbents. As can be seen in Table 2, the BET surface areas of the PAB adsorbents, which were calculated by applying the BET equation to the linear part (\(0.05<\mathrm{P/P_{0}}<0.305\)) of the adsorption isotherms, were 20.14, 27.89, and 34.56 m\({}^{2}\)/g, for PAB1, PAB2, and PAB3, respectively. These values show that the specific surface areas and total pore volumes of the porous sorbents increased from PAB1 to PAB3, which was in good agreement with the increase in the DVB content in the pristine S-DVB matrix.
# Chipless Radio Frequency Identification (RFID) Sensor for Angular Rotation Monitoring
## 3 Performance Assessment and Design Improvement
The final result is shown in Figure 5 where almost all the encodings satisfy both the threshold levels, with just six couples of coded positions between the two; they are (\(10^{\circ}\), \(15^{\circ}\)), (\(65^{\circ}\), \(70^{\circ}\)), (\(70^{\circ}\), \(75^{\circ}\)), (\(75^{\circ}\), \(80^{\circ}\)) and (\(85^{\circ}\), \(95^{\circ}\)). The only remaining critical couple is (\(5^{\circ}\), \(95^{\circ}\)). However, if the starting position and the sense of rotation are known, the angular position can be monitored without any ambiguity since 5 degrees and 95 degrees are not contiguous values. If the starting point and rotation sense are known, an angular range that spans 180 degrees can be considered successfully monitored as well (Figure 6). The resolution step of 3 degrees can also be achieved if the minimum requested distance for considering two different unambiguously coded angles (i.e., the value of _thres\({}_{2}\)_) is set equal to 2 dB (Figure 7). Figure 5: (a) Chart mapping the distance for the four-dipole structure: the three quantized levels of \(x=Dist(\phi_{A}\), \(\phi_{B})\) refer to the value assumed by the cross polar at \(f_{1}\) and \(f_{2}\) and by considering _thres\({}_{1}\)_ = 6 dB and _thres\({}_{2}\)_ = 3 dB. The unit cell is shown in the respective inset; (b) example of application of a 4 \(\times\) 4 chipless RFID tag with four dipoles applied to a rotating object with a known reference for monitoring the counterclockwise rotation. Figure 6: Chart mapping the distance for the four-dipole structure when the considered angular interval spans 180 degrees. It is important to underline that if both the reference and the sense of rotation are known, the proposed chipless RFID tag can potentially monitor the angular position of an object with a resolution of up to 5 degrees, by using four fixed frequencies.
## 2 Positioning in Cellular Networks
### Angle-Based Localization Techniques  
The angle-based techniques utilize either the Angle-of-Arrival (AoA), the Angle-of-Departure (AoD), or even both. The arrival angle is the direction from which the radio signal is received (see Figure 2), while the angle of departure is the direction where the signal is transmitted. In 5G networks, with massive MIMO, the BS will be equipped with hundreds or even thousands of antennas, which can provide an extensive array aperture and support beam-based operations [10]. During the beam search operation, the angle can be obtained easily in terms of the downlink Angle of Departure (DL-AoD) and the uplink Angle of Arrival (UL-AoA). Although UEs and BS search for the best beam for the radio link, DL and UL Reference Signal Received Power (RSRP) is measured to evaluate the signal quality of a beam. This enables the BS to analyze the uplink channels using pilot signals sent by the UEs, which can estimate the AoAs with high accuracy and low inter  
Figure 1: An example for a 5G positioning architecture [18].  
user interference effect; therefore, massive MIMO will boost the utilization of angle-based positioning approaches in 5G networks, which are not widely used in 2G-4G networks [18].  
Unfortunately, most of the time, the positioning requests are coming from dense multipath environments. In these circumstances, appropriate processing of measurements is essential for accurate positioning since only the Line-Of-Sight (LOS) measurements reflect the real angular relationship between the UE and the BS. This is why the most typical positioning approach is the two-step approach, where first an LOS/Non-Line-Of-Sight (NLOS) identification is performed, and then the identified LOS measurements are used for positioning [18]. In some cases, an LOS path may not even exist in the multipath environment, usually caused by large blocking objects, such as walls. When all signals are NLOS propagated, the localization errors are substantial.
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
###### Abstract  
Non-terrestrial networks (NTN) are expected to play a key role in extending and complementing terrestrial 5G networks in order to provide services to air, sea, and un-served or under-served areas. This paper focuses the attention on the uplink, where terminals are able to establish a direct link with the NTN at Ka-band. To reduce the collision probability when a large population of terminals is transmitting simultaneously, we propose a grant-free access scheme called resource sharing beamforming access (RSBA). We study RBSA for low Earth orbit (LEO) satellite communications with massive multiple-input multiple-output (MIMO). The idea is to benefit from the spatial diversity to decode multiple overlapped signals. We have devised a blind and open-loop beamforming technique, where neither the receiver must carry out brute-force search in azimuth and elevation, nor are the terminals required to report channel state information. Upon deriving the theoretical throughput, we show that RBSA is appropriate for grant-free access to LEO satellite, it reduces the probability of collision, and thus it increases the number of terminals that can access the media. Practical implementation aspects have been tackled, such as the estimation of the required statistics, and the determination of the number of users.  
LEO; massive MIMO; massive IoT; beamforming; grant-free; orthogonal frequency division multiplexing; non-orthogonal multiple access +
Footnote †: journal: Sensors
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 2 Mathematical Background
### BEC Equations over \(GF(2^{m})\)  
[PERSON] proposed the BEC model in 2007 [30]. This model provides the following mathematical formulation of BEC for the prime field:  
\[x^{2}+y^{2}=1+dx^{2}y^{2} \tag{1}\]  
Equation (1) defines the initial points of the curve as \(x\) and \(y\), with the curve parameter represented by \(d\). Nevertheless, dealing with curves in large prime fields is critical. To address this issue, [PERSON] introduced the binary version of Edwards curves, as described in Equation (2) [31]:  
\[E_{B,d_{1},d_{2}}:d_{1}(x+y)+d_{2}(x^{2}+y^{2})=xy+xy(x+y)+x^{2}y^{2} \tag{2}\]  
Equation (2) describes the binary representation of Edwards curves, with \(x\) and \(y\) representing the initial points on the curve. The curve parameters are denoted by \(d_{1}\) and \(d_{2}\). It is worth noting that Equation (2) is valid under the condition that \(d_{1}\) is nonzero and \(d_{2}\) is not equal to \(d_{1}^{2}+d_{1}\).
## 4 Numerical Simulation
### Milling Force and Mean Value of Tool Offset  
According to the milling parameters given in Section 4.1, we first obtain the milling force and its Fast Fourier Transform (FFT), as shown in Figure 6, when the speed \(n\) is equal to 1000 rpm. We can see that this spindle speed leads to a frequency of 33.33 Hz for the cutting force with two teeth.  
In this simulation, we use the _lsim_ function in MATLAB to compute the time-domain response of the joint deflections \(\Delta\mathbf{q}\) under the periodic milling force from Equation (1). Then, the cutter tool offset can be obtained from Equation (2) for every point along the tool path.  
Figure 7 shows the mean value of the tool offset amplitudes \(d_{m}\) with different \(x_{p}\), \(y_{p}\), and \(\theta_{p}\) and the variation of \(d_{m}\) in the \(x_{p}y_{p}\) plane with different \(\theta_{p}\). The following observations can be found. In Figure 7a, when \(y_{p}\) is equal to 0 m and 0.25 m, the deflection of joint 3 plays a major role when \(x_{p}\) is between 0 m and 0.2 m. When \(x_{p}\) increases, the torque acting on joint 3 due to the \(x\) component of the cutting force decreases. When \(x_{p}\) is between 0.2 m and 0.6 m, the deformation of joint 1 plays a major role, and the torque acting on joint 1 due to the \(y\) component of the cutting force increases with the increase of \(x_{p}\). When \(y_{p}\) is equal to 0.5 m, the deformation of joint 1 plays a major role; therefore, we see that \(d_{m}\) increases when \(x_{p}\) increases. In Figure 7b, it is seen that \(d_{m}\) is asymmetrical about the \(oxz\) plane because the cutting forces are in opposite directions in every two symmetrical positions about the \(oxz\) plane along the milling groove. Figure 7c shows that \(d_{m}\) increases when the inclination angle \(\theta_{p}\) increases. This is likely because the \(z\) component of milling force and the resulting torques on joints 2 and 3 increase as the angle \(\theta_{p}\) increases. Similarly, we find in Figure 7d that as angle \(\theta_{p}\) increases, \(d_{m}\) becomes larger. In addition, it can be seen from Figure 7d that when \(\theta_{p}\) remains unchanged,  
Figure 6: The milling force and its Fast Fourier Transform (FFT).  
decreases as the workpiece is placed closer to the robot. In particular, when vector \(\mathbf{p}\) is equal to [0.6 m, 0.5 m, \(\pi/3\)]\({}^{T}\) and [0.6 m, \(-\)0.5 m, \(\pi/3\)]\({}^{T}\), \(d_{m}\) dramatically increases since the natural frequencies of the robot along the milling path with these workpiece poses are close to twice that of the milling force frequency (66.7 Hz).
# Tensile Capacity of Adhesive Anchors in Damaged Masonry
## 2 Experimental Research  
### Specimen Geometry and Materials  
The tensile tests on post-installed adhesive anchors were performed in masonry elements previously damaged in in-plane cyclic tests.  
Overall, 5 masonry walls (1.29 m \(\times\) 1.30 m, three with thickness of 250 mm and two with thickness of 380 mm, English bond pattern) were considered (average compressive brick strength about 25 MPa and mortar M5).  
The anchors consisted in a \(\varphi\) 8 rebar (borehole 10 mm, embedment length 200 mm) installed with a hybrid adhesive in undamaged masonry with the pattern as shown in Figure 1. The adhesive characteristic tensile load given by the producer is 7 kN for an embedment length of 100 mm. Assuming a linear increase, for an embedment depth of 200 mm a doubling in resistance can be expected. The anchors had a 90-degree hook at the outside end to sustain the steel mesh of a reinforced concrete plaster layer that was placed on both sides of the wall. Before the anchor tension tests, the specimens had been subjected to diagonal shear tests according to ASTM E519 [38] but with an increasing cyclic load protocol according to the sequential phased displacement (SPD) procedure [39] and damaged until it was not possible to increase the displacement further. Overall, each wall underwent about 90 cycles. Figure 2 shows the applied test protocol, which did not consider reversal load, to be able to maintain the ASTM E519 [38] loading scheme.  
This approach allowed to load the specimen as shown in Figure 2b, where the behavior (load-displacement curve based on actuator displacement) observed in one wall is reported exemplarily. As a consequence, the wall is mainly damaged along the compressed diagonal, and thus the anchors were subjected to different stresses depending on the location within the wall.  
At the end of the wall tests the reinforced plaster was detached, and the masonry showed several major cracks along the diagonal. For the anchor tests, each anchor had a code (A1, A2... A8) depending on the position within the wall (Figure 1).  
Details of the positioning with respect to the brick/joint are exemplarily shown in Figure 3. After the wall testing phase, most of the anchors were bent (Figure 4) which required additional measures to perform a tension test. In these cases, the anchors were cut just 6 mm above the surface of the masonry and a steel washer and a new bar were welded at the top of the existing bar to be able to perform the anchor pull-out test (Figure 4). In some cases, anchor testing was not possible, because the bent part was too close to or below the masonry surface and welding could not be executed. In other cases, the anchor was  
Figure 1: Anchor position within the wall after installation (**a**) and after damage (**b**) (dimensions in mm).  
Figure 2: (**a**) Test protocol and (**b**) example of load-displacement curve of the actuator response of a wall (**b**).  
already loose because the crack was too large (e.g., along the diagonal of some walls in positions A4 and A6).