esa-sceva/satcom-chunk-collection huggingface dataset:
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
## 3 Transmission Scheme
### Activation and Beacon Beam  
There is a need for system-level synchronization between the ISNs, the external power source (BS), and the recipient of information (user). This synchronization is crucial because it aids reliable information transmission without added frame errors. To realize this, we propose equipping the ISN with energy sensors that can detect the amount of energy a sensor receives from the BS. To activate the ISN, the BS sends a beacon beam towards it by transmitting a constant power signal for a duration of \(T\) symbols to awaken the ISN. The ISN is equipped with energy detectors that can measure the energy of the signal \(E_{l}\). A beacon beam is thus defined as a wake-up beam aimed at a certain ISN with an energy level exceeding a certain threshold, i.e.,  
\[E_{l}>E_{threshold}, \tag{14}\]  
To ensure a universal energy threshold \(E_{threshold}\) for all ISNs, the power allocation in this scheme is a channel inversion in which  
\[P_{l}=\frac{P}{\beta_{1}^{l}} \tag{15}\]  
to ensure equal reception of signal power at the ISNs.  
When the beacon beam impinges on the scheduled ISN, the ISN is activated and begins to modulate the signal using the BPSK symbols by adding a common phase \(\phi_{com}\in\{0,\pi\}\) across all \(N\) elements. Next, we detail how the IRS can send information to the user by piggybacking on the reference signal from the BS, then describe the received signal at the user.
## 4 Conclusions  
The effect of Nafion-112 membrane doping by incorporation of two IL-cations (1-butyl-3-methylimidazolium tetrafluoroborate or BMIM\({}^{+}\)BF\({}_{4}\)\({}^{-}\) and phenyltrimethylammonium chloride or TMPA\({}^{+}\)Cl\({}^{-}\)) with similar molecular weight and molar volume by proton/cation exchange mechanism was analyzed.  
Surface, thermal and electrical characterizations of the Nafion112 doped membranes were basically carried out by XPS, contact angle, DSC and impedance spectroscopy measurements. Results show the surface coverage of Nafion112 as a result of both BMIM\({}^{+}\) and TMPA\({}^{+}\) doping, which reduced the hydrophobic character of both modified membranes and give rise to thermal stable membranes when compared with the original Nafion112 membrane. However, membrane doping reduces the conductivity of both modified samples, but this effect is significantly higher for the Nafion112/TMPA\({}^{+}\) membrane. Although this result does not seem to favor the use of doped membranes in PMEFCs systems, it could be of interest in DMFCs due to a reduction in methanol cross-over.  
Diffusive transport of HCl solutions through Nafion112/BMIM\({}^{+}\) membrane was also analyzed by membrane potential measurements and compared with the original Nafion112 sample. Our results indicate a slightly higher transport number of H\({}^{+}\) across the doped Nafion112/BMIM\({}^{+}\) membrane, which can also be of interest for application in other electrochemical devices.
# Fake Base Station Detection and Link Routing Defense +
## 7 Implementation and Experimental Results
### Testing without Our Scheme: No Availability Against Fake Base Station  
We empirically validate that the 5G connection works well when the base station is the legitimate base station. Against a fake base station, we implement enabling vs. disabling our scheme. This section focuses on the case when our scheme is disabled, while Section 7.5 analyzes when our scheme is enabled against fake base stations (as well as legitimate and faulty base stations). The fake base station when our scheme is disabled can engage and hold the user equipment as much as it wishes. We implement the duration control for such withholding, and the user equipment attempts to connect to the fake base station by transmitting the registration request packets until the fake base station responds. If the fake base station chooses to continue to withhold, the user equipment continues with the connectivity setup protocol without trying another base station, depriving it of its connectivity availability.
## 3 Results
### Evaluation of ERA5 and ERA-Interim Reanalyses
Figure 4 shows maps of R and ubRMSD values between ERA5 daily mean surface SW in and in situ measurements at four stations (Figure 4a,b) as well as their differences against R and ubRMSD values from ERA-Interim (Figure 4c,d) over 2017. From Figure 4, one can clearly observe higher correlations and lower ubRMSD values for ERA5 compared to ERA-Interim for the considered four stations. This is consistent with the observed positive correlation differences (ERA5--ERA-Interim, Figure 4c) and negative ubRMSD differences (ERA5--ERA-Interim, Figure 4d). Figure 4 shows the 2017 time series of the daily SW in for ERA-Interim (blue), ERA5 (green), and the in situ observations (red) for Bobo (Figure 5a; 11.16\({}^{\circ}\)N, 4.30\({}^{\circ}\)W) and Dori (Figure 5b; 14.03\({}^{\circ}\)N, 0.03\({}^{\circ}\)W) stations belonging to the SG and SS zones, respectively. At these subtropical sites, the temporal structure of the annual cycle of SW in is strongly shaped by the top-of-the atmosphere incoming radiation, which drives the two well defined maxima of SW in [71; 72]. At Bobo, they occur in April and October while further North in Dori, the second maximum is less pronounced. Both reanalyses broadly capture these features, even though they tend to overestimate SW in (in particular during the monsoon, when clouds induce sharp drops which can reach more than 100 W/m2)--a similar bias also noted by [73] at the relatively close Sahelian site of Niamey. However, this bias is slightly reduced in ERA5. This implies that ERA5 performs better in representing SW variations than ERA-Interim over BF. This better performance of ERA5 is also probably related to the implementation of an improved radiation scheme (see [74] for more details). Figure 5: Incoming solar radiation temporal evolution for ERA-Interim, ERA5, and in situ measurements for (**a**) Dori and (**b**) Bobo stations for 2017.
## 5 Results and Discussion
### Simulation Results  
Simulink was used to test the PID-GA parameters in the simulation. The block diagram used to simulate the system is shown in Figure 9. The ARX331 transfer function obtained during the system identification process is designed to mimic the FBBA real plant. A 100-degree step source was used as the reference angle, and the controller's performance was measured in terms of overshoot (OS %), rise time (Tr), and settling time (Ts). The simulations were run with a sampling time of 0.005 s.  
Table 3 summarises the PID parameters used in the simulation. The parameters were as follows: \(K_{p}\), \(K_{i}\), and \(K_{d}\).  
Figure 10 compares the simulated closed-loop step response of the PID-auto-tuned and PID-GA performance. A reference angle with a constant value of 100 degrees was set as the desired position. From the result, PID-GA shows a better overall performance when compared to PID-auto-tuned.  
Table 4 summarizes the controllers' performance based on the rise time (Tr), settling time (Ts), and overshoot (OS %). Both controllers produced a value of 0 for OS %, but in terms of Tr and Ts, the PID-GA controller produced a better result with 1.26 s and 2.29 s, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller** & **Tr (s)** & **Ts (s)** & **OS \%** \\ \hline PID-auto-tuned & 2.12 & 3.81 & 0 \\ PID-GA & 1.26 & 2.29 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulations results.  
Figure 9: Simulation block diagram.
# Heart Attack Detection in Colour Images Using Convolutional Neural Networks
## 2 Related Work  
Many researchers are concentrating their efforts on solving different challenges in health care [11; 12], specifically through human activity recognition [13; 14]. This has opened the door to the detection of abnormal events in an automatic manner. Fall detection is by far the most commonly faced challenge and the top topic in health environments [15; 16], but there exist other challenges like monitoring [PERSON]'s disease [17] or even recognising emotional states [18; 19].  
Regardless of the event to be detected, it is possible to classify all publications into two principal approaches according to the acquisition sensors of the input data, which are non-visual and visual. For non-visual sensors, the use of accelerometers and gyroscopes [20; 21; 22] should be highlighted. Although these devices provide high precision, their main drawback is that they require that people wear them all the time, which is uncomfortable and not always possible. The second class uses cameras and is always based on computer vision to analyse the captured images or videos.  
Regarding the image-based approach, the use of the Microsoft Kinect device is a solution often found in the literature [12; 23; 24; 25]. This sensor simplifies some tasks like background subtraction and/or skeleton generation but limits or reduces the accuracy after a few meters of distance [26]. Another option consists in analysing the provided colour information directly by using artificial intelligence techniques [26; 27]. In fact, the use of neural networks, and especially CNNs, has obtained excellent classification results in identifying particular events in recent years [24; 27; 28].  
The most traditional approach to identify specific human events by analysing images is the identification of the human's posture [29; 30; 31]. This can be achieved by analysing the silhouette of the person [16; 25], obtaining the skeleton [32; 33; 34], processing the complete information of the person's image, which is the approach used in this work, or even a combination of techniques [35].
## 1 Introduction
A new objective method to relate hyperspectral RS and ecosystem diversity was proposed [29], and significant correlations were observed between grassland ecosystem diversity (expressed as conventional species diversity indices such as richness and Shannon index) and \"optical diversity\", an optically-derived metric of biodiversity defined as the coefficient of variation (CV) in spectral reflectance across space. Optical diversity proved to be a valid approach for the detection of grassland \(\alpha\)-diversity (at a local scale, e.g., at the plot level), while larger-scale models have been applied to measure the species turnover among sites (\(\beta\)-diversity, sensu [PERSON], 1972 [30]). We refer to [PERSON] et al. [31] for a review of the matter. Following the optical diversity approach, a significant positive linear correlation was observed between optical diversity and productivity, but only for very low productive grasslands (maximum productivity: 136 g\(\cdot\)m\({}^{-2}\); [2]), while studies covering a wide range of productivity are missing. The aim of this study was to use airborne optical RS data to investigate within-site spatial patterns of ecosystem diversity and functions in highly-diverse mature grassland canopies of the Italian Alps, considering different scenarios at decreasing spatial and spectral resolutions (using resampling techniques) to highlight the potential of S2 to capture ecosystem diversity and productivity patterns. In this context, a number of research questions are addressed in this paper:can we identify optical diversity and ecosystem productivity relationships (for a wide range of productivity) using remotely-sensed proxies? 2. are such relationships scale-dependent? 3. are the S2 data spatial and spectral resolutions suitable to detect such relationships? Firstly, simulating the S2 bands, we compared the performance of VIS-NIR SVIs and the performance of solely NIR-based (\(\geq\)740 nm bands) SVIs in estimating the NEE of five different grasslands, and we analyzed the relative contributions of different spectral domains for predicting the productivity of these ecosystems to highlight both structural and biochemical controls. Secondly, we calculated the coefficient of variation (CV) of reflectance across space (which was demonstrated to be a proxy of ecosystem biodiversity [29]), and thus we explored across-scale optical diversity and productivity patterns for the investigated grasslands using both hyperspectral and S2 simulated multi-spectral bands. Finally, we investigated the S2 data spatial and spectral resolutions suitability for detecting optical diversity and productivity spatial patterns.
# Damage Characteristics and Dynamic Response of RC Shells Subjected to Underwater Shock Wave
## 5 Discussion  
The damage characteristics and dynamic response of semi-spherical and semi-cylindrical bottom-sitting RC shells subjected to underwater shock waves were studied based on the Arbitrary Lagrangian-Eulerian (ALE) algorithm using LS-DYNA software. The water increases the damaging effect of an explosion compared to air. With an increase in explosive equivalent or decrease in explosive distance, RC shells are damaged more severely. Steel bars increase the anti-explosion capacity of RC shells. The water around the RC shells forms a 'hoop effect' within a certain range of hydrostatic pressure, which decreases the horizontal convex deformation of RC shells under explosion shock waves. However, the inward concave deformation of the semi-cylindrical shell center is increased by hydrostatic pressure. The adopted ALE method amalgamates the benefits of the Lagrangian and Eulerian methods. The Lagrange method is primarily applicable to solid materials. Its significant limitation lies in its inability to handle large deformations. The Euler method is generally more suitable for describing the behavior of fluids and gases, but the interface tracking of different materials is computationally large and not ideal. Due to its unique nodal motion, the Arbitrary Lagrangian-Eulerian (ALE) method amalgamates the benefits of both the Lagrangian and Eulerian methods [5]. However, the study considered the effect of explosion shock waves, but it did not consider the impact of bubble pulsation. In future research, we will consider performing relevant research, further determining the effect of bubble pulsation. In addition, with the increasing number of intentional or unintentional underwater explosions, enhancing the protection performance of underwater bottom-sitting shell structures is crucial. Also, indoor and outdoor underwater explosion  
Figure 41: The schematic diagram of the deformation in the semi-cylindrical RC shell cross-section.  
Figure 42: The curves of horizontal displacements of semi-cylindrical RC shells changing with \(\beta\) with 300 kg explosive equivalent and 7 m explosive distance under different hydrostatic pressures.  
model tests should be conducted to enrich and develop research on underwater explosions in bottom-sitting shell fields.
# A Hybrid Optimization Algorithm for the Synthesis of Sparse Array Pattern Diagrams
## 2 Quantum Hybrid Particle Swarm Optimization Algorithm Combined with Quantum Behavior
### Quantum-Behaved Optimization Algorithm  
The quantum-behaved optimization (QBA) algorithm mimics the movement of quantum particles in a potential field towards the lowest energy state. It analogizes the optimization search space to a quantum potential well, where the global optimum corresponds to the lowest potential energy point. Unlike the inertia-weight-driven convergence of PSO, the QBA approaches the optimum by leveraging quantum gate operations and tunneling effects. Its key advantage lies in the quantum probability distribution mechanism, which significantly enhances global exploration and effectively avoids premature convergence, addressing the local optimum trap issue of classical PSO. In the QBA, particle positions are updated based on the quantum potential well model and a Monte Carlo strategy, using the following formula [22]:  
\[X_{i}^{t+1}=p_{i}+\beta\cdot|M_{best}-X_{i}^{t}|\cdot\text{ln}\bigg{(}\frac{1 }{u}\bigg{)} \tag{6}\]  
Here, \(\beta\) is the expansion-compression factor, \(u\) is a uniformly distributed random number, and \(u\in(0,1)\). The individual particle mean \(M_{best}\) and the weighted average of individual and global optima \(p_{i}\) are defined as follows [23]:  
\[\left\{\begin{aligned} M_{best}=\frac{1}{N}\sum\limits_{i=1}^{N}p \text{{best}}_{i}\\ p_{i}=\varphi\cdot\text{{pbest}}_{i}+(1-\varphi)\cdot\text{{gbest}}_{i }\end{aligned}\right. \tag{7}\]  
This step, a core feature of quantum behavior, ensures that the search focuses on high-quality solution regions. Here, \(\text{{pbest}}_{i}\) is the individual's historical optimum. The quantum population optimum \(\text{{gbest}}_{i}\) is also defined using a random number \(rand\in(0,1)\).
# The Human Performance Impact on OEE in the Adoption of New Production Technologies
## 4 Case Study of a Semi-Automatic Packaging Machine in a Logistics Company
### OEE Definition: Semi-Automatic Packaging Machine  
The previously described OEE measurement methodologies can be applied to several typologies of production technologies through an adaptation of the different metrics. Indeed, the factors to be considered for the OEE evaluation vary according to the type of machinery and the kind of inefficiency typically encountered in the production start-up phase. To give a practical overview of the proposed OEE indicators, they are here adapted and applied to the described case study. Table 6 shows the specific parameters which have been adopted in the OEE evaluation. Note that this application can be performed for different types of machines to obtain relevant OEE measurements.  
Considering the \"not scanned products\" (P\({}_{\text{no, scan}}\)), these can be observed when the technology requires operators to scan a product identification code prior to processing which is either not performed by error or incorrectly performed. Differently, the \"not loaded products\" (P\({}_{\text{no load}}\)) consist of all the semi-finished products or raw materials that are transported to the machinery and yet not processed by the operators, even though they have been considered processable by the information system. This may happen when the operators conduct an inspection of the technical and physical characteristics of the items and decide that they are not compliant with the technology's processing conditions.  
For this reason, this operator's choice must be considered as a loss of efficiency due to a human error. In addition, the \"empty packages\" (Pempty) are defined as the number of void packages due to the constant operating speed of the machine, which is not adapted to the processing rate of the operators. Hence, this loss must be assumed as an intrinsic loss of efficiency caused by the machinery.  
These considerations lead to the following definition of potential products and target products for the semi-automatic packaging machine:  
\[\text{P}_{\text{pot, proc}} =\text{P}_{\text{no scan}}+\text{P}_{\text{no load}} \tag{27}\] \[\text{P}_{\text{pot, mac}} =\text{P}_{\text{empty}}\] (28) \[\text{Potential products} =\text{P}_{\text{pot}} =\text{P}_{\text{pot, process}}+\text{P}_{\text{pot, mac}}=\text{P}_{\text{no scan}}+\text{P}_{\text{no load}}+\text{P}_{\text{empty}}\] (29) \[\text{Target products} =\text{P}_{\text{target}} =\text{P}_{\text{c}}+\text{P}_{\text{nc}}+\text{P}_{\text{no scan}}+ \text{P}_{\text{no load}}+\text{P}_{\text{empty}} \tag{30}\]  
Therefore, in this case, the only loss of performance efficiency for the OEE process is given by the \"empty packages\" (Pempty). Indeed, as already mentioned, this loss is encountered for technologies that operate at a constant operative speed without adapting the production capacity to the processing rate of the operators.
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for LFM-CW SAR System
#### 4.2.3 Motion Compensation 1  
If the range \(R(n)\to R(n)+\Delta R(n)\), then motion compensation is applied, which consists of multiplying the data by \(H_{mc1}\) or equation (46) to remove the unwanted phase.  
\[H_{mc1}=exp\bigg{(}j\frac{4\pi\alpha\Delta R(n)}{c}\big{(}f_{0}+f_{t}\big{)} \bigg{)} \tag{46}\]  
where \(\Delta R(n)\) is the distance variation or range variation, and it is dependent on the time in azimuth \(n\).
## 5 Feasibility and Evaluation
### Architectural Comparison
#### 5.5.10 Extensibility and Maintainability  
EDAS, as well as agent-based, centralized and distributed architectures are relatively hard to maintain and may demand increased cost due to the increased number of components involved. End-user solutions have a limited scope and fewer components. Thus, they take less effort and cost to maintain them. However, this maintenance advantage comes with a limitation of extensibility. Since end-user architectures are designed to meet specific and limited objectives, they cannot be extended to accommodate other systems. Agent-based systems can also be extended at the cost of an extra component, _i.e._, the agent. Centralized and distributed architectures may also provide room forextension. However, the limited context they protect limits their extension scope. While EDAS can be extended to accommodate any system or thing that has a potential event framework.
## 3 Experimental Results and Discussion
### Cyclic Cleaning  
Once again, the same testing approach used for petroleum oil was applied. The predetermined period of time was 15 min, as this is the typical average value used industrially for ultrasound cleaning. After each cycle, the specimens are dried in silica for 45 min at 70\({}^{\circ}\)C. These procedures comprise a cycle and after each cycle three specimens were tested.  
The maximum shear strength values are depicted in Figure 17, 0 h defined the initial condition.  
Unlike that which was was verified for the petroleum oil, the cleaning fluid shows well-defined behaviour, showing a gradual decrease in strength. These results also show that the ultrasound cleaning is a suitable option for cleaning bonded joint milling tools, leading only to a small decrease in shear strength and enabling fast cleaning times.  
Looking at the data present in Table 3, which shows that for common fluids used in ultrasound cleaning, such as benzie, water/glycol and ethanol, a similar gradual decrease in strength is presented from 100 h to 1000 h. When compared with petroleum oil, the main drawback of this cleaning option would be a larger decrease over longer exposure times.
## 5 Simulation Experiments  
In indoor space, the positioning accuracy and precision of UWB are often affected by the surroundings of a positioning object. To validate the improved UWB/IMU tight combination model proposed in this study, the MATALB R2020a simulation tool was used to calculate the difference between the distance obtained by the secondary time-integration of the IMU accelerometer and UWB ranging. Then, the tool eliminated the results larger than the distance threshold to finally obtain more accurate position according to the filtered UWB distance results.  
The simulation experiment was set in a space of 10 m \(\times\) 10 m \(\times\) 5 m (i.e., length \(\times\) width \(\times\) height). We used the UWB hardware (UWB tag, base station) and software (measurement tool) supplied by Woxu Wireless Company [26] to validate the improved UWB/IMU positioning. We marked the ground truth line with a black belt on the ground as shown in Figure 5. The red and blue circles represent the location of the base stations, which were (2, 2, 4) as an endpoint of a pink line, (2, 8, 4) as an endpoint of a cyan line, (8, 2, 4) as an endpoint of a blue line, and (8, 8, 4) as an endpoint of a yellow line, and the red asterisk point represents the origin coordinate (0, 0).  
We used the MATLAB 2019a robotics system toolbox to set the position and attitude of the trajectory passing point to automatically generate a smooth motion trajectory. The target motion state included linear motion, curve motion, acceleration, deceleration, and uniform motion. In this experiment, the linear moving object was analyzed, and the motion speed was controlled within 4 m/s.