esa-sceva/satcom-chunk-collection huggingface dataset:
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 4 The Problem
### Illustrating the SF12 Well Phenomenon  
This subsection illustrates the behavior of the EDs and the network performance over time for two example cases, which correspond to the lowest and highest total offered loads considered in our study. In the first case, there are 30 CEDs, each one generating 0.9 packets/h. In the second case, there are 100 CEDs, and each ED aims to transmit 18 packets/h.  
Figure 4 depicts the SF value used by each node over time, for all nodes in the two described scenarios. In both cases, all the CEDs abandon the initial SF value (i.e., SF7), progressively increase the SF values used, and end up using SF12. In this paper, we define the Well Fall Time (WFT) as the time interval from the start of the network operation until the network reaches the SF12 Well state (i.e., when all the CEDs are using SF12). The WFT is equal to 1714.7 h and 1.1 h in the lowest and highest load scenarios considered (Figure 4a,b), respectively.  
Figure 5 shows the SF value distribution for all the CEDs over time. We can observe how, as both the number of CEDs and the EDL increase, the CEDs start using greater SF values earlier. When a high ratio (~90%) of the CEDs reach SF12, the number of EDs using  
Figure 3: Example of EDs and gateway spatial distribution on the scenario area, where there are 80 CEDs.  
SF12 increases more moderately, until all the CEDs reach SF12 at the WFT. At that time, the CEDs use the transmission medium in the most inefficient way, as SF12 leads to the highest ToA.  
We next evaluate network performance during the considered simulated time (86 days) for both example scenarios. We first examine the evolution of the MAC frame collisions and the packet losses due to duty cycle restrictions over time (see Figures 6 and 7, respectively). Figure 6 illustrates, for each given time value, the cumulative number of MAC frames that collided until that time, divided by the total number of MAC frames that were transmitted during the whole simulation time. Figure 7 depicts, for each given time value, the cumulative number of application packets that were dropped due to duty cycle restrictions, divided by the total number of application packets that were requested to be transmitted during the whole simulation time.  
Figure 4: SF value used by each CED over time. (**a**) Lowest load: 30 CEDs, using an EDL of 0.9 packets/h. (**b**) Highest load: 100 CEDs, using an EDL of 18 packets/h.  
Figure 5: Percentage of CEDs using each SF value over time. (**a**) Lowest load: 30 CEDs, using an EDL of 0.9 packets/h. (**b**) Highest load: 100 CEDs, using an EDL of 18 packets/h.  
The results show an exponential increase in the cumulative number of frame collisions (Figure 6) and the cumulative number of application packet drops (Figure 7), approximately until the WFT. This happens because the nodes' SF values increase, and thus the ToA increases as well. After the WFT, the cumulative number of collisions and dropped packets continues to increase, but rather linearly, since the SF values used cannot increase beyond SF12.
## 6 Results and Discussion
### Simulation Runs and Sensitivity Analysis
#### 6.2.1 Individual Member Scenario Run  
This experiment starts with the configuration of the initial values of the IMAgent A's parameters. Figure 21 illustrates the scenarios S1.1, S1.2 and S1.3 for the IMAgent A.  
At the beginning of the simulation run, which corresponds to the S1.1, the value of the tuple (valence, arousal) corresponds to _frustration_, as expected. This agent represents a company that has joined the CN a few days ago, so its metrics are still below average. Nonetheless, following Scenario S1.2, it receives an invitation to form a VO and, as can be seen in t = ~35, both valence and arousal increase substantially (activating the _excitement_ IME), denoting both the satisfaction and the stimulus that this event provoked in company A. Then, for a considerable period of days, its IME state remains stable. After a couple of months, the VO is finally created, and is reflected in the results with the decrease in valence and arousal at t = 170, activating the _frustration_ IME. Meanwhile, some metrics are  
Figure 21: IMAgent A scenarios simulation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Description** & **Sensitivity Analysis** & **Expected Outcomes** \\ \hline \multirow{2}{*}{**S.2.1**} & This scenario runs the initial condition & Initial conditions from the & \\  & of the involved agent. & involved agent. & Contentment \\ \hline \multirow{3}{*}{**S.2.2**} & During the runtime the VO under & CNTrust, CNVSAlign decreases a & \\  & creation failed, thus the level of values & portion of its current value. & Depression/ \\  & alignment and trust decreases & VOsCreation diminishes 1 and & Contentment? \\  & substantially. & VOsFailed augments 1. & \\ \hline \multirow{3}{*}{**S.2.3**} & During the runtime there is a shift in & ContFreq decreases in the same & \\  & members’ IME states from content & value that the DepreFreq & Depression \\  & to depressed. & increases. & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Scenarios for the SimulCN collaborative network represented by the CNAgent SimulCN.  
updated, such as the number of VOs or the CN income, shown in the increase in valence in t = 180. However, company A is still frustrated; it is still a young company in the CN and its goals are not yet met. Finally, the occurrence of a CN trust breach (S1.5) at t = 210, conducts the IMAgent A state to _depression_, as expected.
# The Shear Behavior of the Curved Interface in Polyurethane-Concrete Composite Structures
## 3 Results and Discussion
### Effect of Arc Diameter on the Shear Behavior of the Curved Composite Specimens  
#### 3.2.1 Shear Stress-Displacement Curves of Curved and Planar Specimens with Different Arc Diameters  
Figure 11a-d depict the shear stress-displacement curves of curved polyurethane-concrete composite specimens with different diameters. It can be observed that as the diameter of the curved interface increases, the stress during the fracture phase of the specimens decreases gradually, transitioning from an instantaneous vertical drop to a gentle decrease. In Figure 11a, it is evident that the shear stress of specimen R200 drops significantly in a short period, nearly vertically, approaching residual strength directly, indicating thorough fracture failure of the R200 specimen. As the arc diameter increases, as shown in Figure 11b,c, the magnitude of the linear decrease in shear stress during the fracture phase of specimens R250 and R350 gradually diminishes, and the corresponding shear displacement range for the linear decrease increases. This suggests that the occurrence of fracture failure requires a larger displacement range, gradually transitioning from brittle to plastic failure. Figure 11d indicates that with larger arc diameters, the decline in shear stress of specimens becomes more gradual.  
Figure 12 depicts the shear stress-displacement curves of polyurethane-concrete composite specimens with four different arc diameters and five polyurethane densities, as shown in Figure 12a-e. It is observed that as the interface diameter increases, the peak shear strength, shear modulus, yield strength, peak shear displacement, and residual strength of the specimens decrease. With the increasing interface diameter, the shear stress-displacement curves of the composite specimens gradually approach those of the R1000  
Figure 11: Shear stress-displacement curves of curved and planar composite specimens with different arc diameters: (**a**) curved specimen R200, (**b**) curved specimen R250, (**c**) curved specimen R350, and (**d**) planar specimens.  
specimen (i.e., planar specimen). Similarly to the explanation in previous sections, during the fracture phase, the stress drop transitions from an instantaneous vertical decrease to a gradual decline as the interface diameter increases. It can be inferred that as the arc diameter increases, the curved specimens of the same size approach that of a planar interface. Therefore, the shear stress-displacement curves of curved specimens, with increasing arc diameter, ultimately converge towards those of planar specimens.  
Figure 12: Shear stress-displacement curves of curved and planar composite specimens with different arc diameters: (**a**) density = 0.33 g/cm\({}^{3}\), (**b**) density = 0.42 g/cm\({}^{3}\), (**c**) density = 0.51 g/cm\({}^{3}\), (**d**) density = 0.58 g/cm\({}^{3}\), and (**e**) density = 0.66 g/cm\({}^{3}\).
## 3 Results
### PDA Communication Experiment  
The RSSI of the PDA affects its communication ability and can lead to socket pipeline rupture and information loss, in addition to interfering with the normal operation of the feeding system. In order to investigate the relationship between RSSI and PDA_RT, five ESF were randomly selected as samples and divided into eight groups with RSSI as the variable. The test process was as follows: (1) the PDA sent packet-a to the ESF, and recorded the time of sending; (2) the ESF received packet-a and replied with packet-b; (3) the PDA received packet-b and recorded the time of receipt. The number of times packet-a was sent, the number of times packet-b was received, the times at which packet-a was sent, and the times of receipt of packet-b were then counted, and the PLR and PDA_RT calculated.  
During the communication request process, to ensure the uniqueness of the packet-b response, the PDA could only run one request at a time. The PDA released the socket channel for other requests only after a complete process was finished. If the PDA sent packet-a but did not receive packet-b, the socket channel could be continuously occupied, causing subsequent communication requests to be blocked. Therefore, the upper limit of the communication duration was set at 5000 ms. If packet-b was not received within 5000 ms, the communication request would be regarded as timed out and terminated.  
Figure 10 shows the results. When the RSSI was from \(-80\) dbm to \(-70\) dbm, the PDA sent 146 packet-as and received 141 packet-bs, with a PLR of 3.425%. When RSSI was from \(-90\) dbm to \(-80\) dbm, the PDA sent 47 packet-as and received 32 packet-bs, with a PLR of 31.91%, and the communication pipeline was easy to disconnect, which explained the small sample size at this interval. When the RSSI was from \(-70\) dbm to \(-10\) dbm, there was no packet loss, the average PDA_RT was 556.05 ms, and the communication between the PDA and ESF was stable. Therefore, the power of the ESF WLAN transceiver in the ESF should be adjusted to an RSSI between \(-70\) dbm and \(-10\) dbm to meet the requirements of the application used by the gestation unit.
## 4 Discussion
### Effect of Activation of AEB on Injury Risk  
When evaluating the effect of an occupant's forward excursion due to AEB activation on injury risk, it was found that the average risks for head, brain, and c-spine injuries decreased by up to 2.6% compared to those under a no-AEB activation condition (Figure 13a). These reductions were more pronounced in the reclined seatback condition, which showed up to a 5.1% reduction in injury risk for head and neck regions (Figure 13b).  
In frontal crashes, the forward excursion of the occupant caused by AEB activation reduced the distance between the occupant and the frontal airbag. This reduction in distance might have improved the interaction between the head and the frontal airbag, leading to a decreased risk of head and neck injuries, particularly in the reclined condition. Another modeling study has also demonstrated that occupant excursion caused by pre-crash braking can influence the injury risk in frontal crash scenarios [28]. In this study, the head injury criterion (HIC) was reduced by 19%, although chest deflection was increased by 20%. Similarly, another study has reported a reduction in neck torsion under pre-impact braking conditions compared to no-braking conditions with an upright seatback [30].  
Overall, the effect of occupant excursion due to the activation of AEB (0.8G for 0.5 s) was smaller than that caused by a 20-degree reclination of the seatback from its upright posture (Figures 12 and 13a). The impact of AEB activation was primarily protective and more pronounced when the seatback was reclined instead of upright (Figure 13c). This protective effect from AEB activation raises an interesting question about pre-crash pre-sioning devices [11; 28]. While such systems may reduce occupant excursion during AEB activation, their effects on injury risk for reclined occupants warrant further investigation.
# Laser Technology in Photonic Applications for Space
## 2 Laser Devices
### Other Types of Laser Sources  
Other types of lasers have been studied for space applications, such as gas, dye, and chemical lasers, but they present more difficulties to be adapted to space environments [11]. Common drawbacks include short lifetime and poor radiation resistance [22]. In particular, excimer lasers have lifetimes of less than 100 h due to the degradation of the gas charge, and ion lasers' lifetime is less than 1000 h. Dye lasers are not robust and suffer from a low efficiency, and chemical lasers, such as hydrogen fluoride (HF) lasers, have short lifetimes limited by the fuel charge [23]. Variations of the lasers described in the current section might become relevant for space applications in the future, such as Quantum Dot semiconductor lasers, disc lasers, or photonic crystal fiber lasers. Lasers pumped by other means are also being studied; for instance, a solar-pumped laser was demonstrated in 1966 [24] and such devices have been considered since by both NASA [25] and ESA [26], as they would be extremely relevant in the space environment, although the deployment is not yet realistic due to the low maturity level of such devices.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
###### Abstract  
Femtocell technology has been gaining popularity as an inexpensive means of enhancing cellular coverage and capacity. However, under co-channel deployment--expected to be commonplace due to high spectrum costs--femtocells may incur harmful interference to existing macrocells, and vice versa. To alleviate this problem, This paper proposes a new architecture called _HandOver-driven Femtocell Interference Management_ (HO-FIM) that offloads macrocells by handing macrocell users over to \"hybrid-access\" femtocells that allow the access of both subscribed and unsubscribed users. This use of handovers adds a new degree-of-freedom for solving the interference problem, thus overcoming the capacity limit of the existing approaches that exploit radio resource coordination only. A new challenge associated with this approach is how to minimize handovers while reaching a desired system condition due to the inevitable service interruptions that accompany handovers. This paper proposes to overcome this challenge by triggering handovers only when needed and, once triggered, to find an optimal sequence to reach a target system condition with minimal handovers. This paper designs a greedy per-user control algorithm that has a fine control granularity and thus enables convergence to an optimal solution. The in-depth evaluation of the paper shows that HO-FIM improves the service quality of both macro- and femtocell users; in an example simulation scenario, HO-FIM is shown to improve the signal-to-interference and noise ratios (SINRs) of macro- and femtocell users by up to 23.3 and 2.9 dB, respectively, on average, compared to the case of no handovers.  
femtocell; interference management; handover; co-channel deployment +
Footnote †: journal: Applied Sciences
## 4 Implementation  
Following is the description of the algorithms under testing. To showcase the performance improvements of the optimizations made, intermediate steps of the previously presented equations have been implemented and confronted starting from the continuous model all the way to the optimized discrete algorithm (31) which, the paper argues, is the best compromise for fast, reliable and DSP-optimized integration.  
The main outputs of the _for loop_ implementing the integration are the fluxes in the natural reference frames, but intermediate calculation could also be available.  
For example, for the calculation of \([\Delta T_{i}]\) using (25), the elevation of power of \([\Delta T_{i}]\) can be done recursively at every iteration and the final rotational matrix \([T_{m}]\) and its corresponding inverse could be given as output together with the predicted flux. Another approach could be to calculate the final rotational matrix outside the loop by having \(\Delta\theta=\theta_{k}-\theta_{k-1}\).  
Some implementations presented below already require the elevation of power to occur inside the loop for the proper calculations, so the first method has ben used. However, in the proposed best solution, it has been chosen to use the second approach even though the recursive calculation in the loop could be used. For the testing application, it allows reducing to the minimum the operations inside the _for loop_, thus increasing the performance especially when incrementing the number of sub-intervals.  
Every implementation described has a specific block that minimizes the trigonometric functions for calculating the rotational matrix and its inverse given in (11) by computing only one cosine and one sine both for \([T_{k}]\) and \([\Delta T_{i}]\).  
The parameters used are the resistance matrix \([R]\), the inverse of the inductance matrix \([L]^{-1}\). For the implementations using the matrix \([M]\), all the machine parameters are hidden inside that matrix, as in (19).  
For the sake of simplicity, the implementation refers to an induction machine with magnet flux matrix \([\varphi_{e}]\) set to zero and then not shown in these schemes. To show how the implementations would differ for machine with magnets fields, Appendix A shows the full schemes including the representation of the excitation flux.
## 2 Materials and Methods
### Egocentric Coordinate System Relative to Human Body Direction  
In previous study [10], global coordinate system was used in which rotation invariance has been achieved up to 90 degrees by training the users in different posture angles by providing 45 degrees of freedom in training session. Additionally, it requires too much time and effort to train the users which is not automatic by the system and the posture angles are sometimes different on different users which creates lower performance in the classification.  
In this study, an egocentric coordinate system, which is relative to human body direction, is presented. Similar with the previous study [10], Kinect depth sensor is employed to obtain skeleton joints. Since angles are used, proposed system is already scale invariant. In order to provide rotation invariance, body relative direction in egocentric coordinates is calculated. The 3D vector between right hip and left hip is used to get the horizontal axis, and its cross product with the vertical axis of global coordinate system is assumed to be the depth axis of the proposed local coordinate system. Different from the previous study [10], 3D joint angles are not being used, instead, 8 number of limbs and their corresponding 3D angles with \(X\), \(Y\), and \(Z\) axes of the proposed coordinate system are employed as the feature vector. In order to construct egocentric coordinate system, \(X\) axis is assumed as the normalized vector between the right hip and left hip. Additionally, \(Y\) axis is assumed as the vertical  
Figure 2: Flow chart of the proposed human activity recognition system.  
Mathematically speaking, let \(\hat{u}\) is an unit vector denoted as \(\overrightarrow{u}=u\hat{u}\) and \(\overrightarrow{u}=u_{x}\hat{i}+u_{y}\hat{j}+u_{z}\hat{k}\), where \(\hat{i}=(1,0,0)\) is the unit vector along the \(X\) axis, \(\hat{j}=(0,1,0)\) is the unit vector along the \(Y\) axis, and \(\hat{k}=(0,0,1)\) is the unit vector along \(Z\) axis. The third vector, which is orthogonal to both \(\overrightarrow{u}\) and \(\overrightarrow{v}\), is found by the cross product of two vectors: \(\left|\overrightarrow{u}\times\overrightarrow{v}\right|=uv\sin\theta\), where \(\theta\) is the angle between \(\overrightarrow{u}\) and \(\overrightarrow{v}\). The calculation comes from the basic dot product formula shown below. The cosine angle between two vectors is actually the dot product of two vectors, which are normalized by dividing their components with the magnitude of each vector as follows:  
\[\cos\theta=\frac{\left(\overrightarrow{u}\cdot\overrightarrow{v}\right)}{ \left(\left|\overrightarrow{u}\right|\left|\overrightarrow{v}\right|\right)} \tag{1}\]  
Since all the axes (\(X\), \(Y\), and \(Z\)) and limb vectors are normalized, it is easy to find the angle between each limb vector and \(X\) axis, \(Y\) axis, and \(Z\) axis separately. Finally, arccosine of the dot product gives the 3D angle between two vectors in a range of \([0,\pi]\).
## 1 Introduction
It has been found out in our earlier studies [13; 14] that variations in moisture content in grain under a convective-microwave zone substantially affect the depth of microwave field penetration into the grain layer. However, the dependence of microwave field direction on the moisture content variations had not been investigated. This may be substantially important for the uniformity of grain processing, in a convective-microwave zone. Figure 1: Convective–microwave grain treatment unit comprising two identical processing modules: (1) casing of the convective–microwave module; (2) casing of the convective–microwave module; (3) waveguides with slot radiators. Moreover, it was not yet clear whether the operational matching between the waveguide structure and the magnetron system changes depending on the moisture content in the grain. Do the radiation and total efficiency of waveguides change under these conditions? It is known that the radiation power (\(P_{rad}\)) transmitted from the source to the load depends on the radiation efficiency (\(RadEff\)) and total efficiency (\(TotEff\)), expressed as \(P_{rad}=f(RadEff,\ TotEff)\). It is necessary to determine how these parameters depend on the moisture content of the processed grain. The results of studying these issues could contribute to better evaluating the operational efficiency of convective-microwave processing units, making it possible to define the limits of the moisture content in grain under processing within which such processing techniques are economically reasonable. A convective-microwave zone is a resonator chamber filled with a product subject to processing (grain, in our case) [15; 16]. Ideally, the resonator chamber structure has to be designed to have the maximum possible Q-factor value at the magnetron's fundamental radiation frequency (2.45 GHz). In this case, all of the magnetron radiation energy will be enclosed and distributed over the grain layer [17; 18]. However, unlike the microwave signal transmission techniques used in telecommunications, resonator chambers applied in technological processes for the treatment of various products are not void volumes. As a rule, they are entirely or partially filled with a material subject to processing or/and are occupied by certain structural elements. In what way might the Q-factor of the microwave-active zone change? The answer to this question will make it possible to define more accurately the parameters required for calculating convective-microwave zones.