esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Other Works Analyzing Different Aspects of LoRaWAN
The authors conclude that the class B devices have a lower delay in downlink traffic compared to class A devices, and class B devices have a lower packet loss rate of downstream traffic. Furthermore, in [41], a framework is used to compare the price of LPWAN deployment in urban and rural areas; in this work, the main cost factors for the deployment of the LPWAN network are identified, such as the cost of equipment, installation cost, spectrum cost, and electric power cost. Additionally, with NS-3 simulations of LoRaWAN and SigFox technologies, it was concluded that the impact of inter-technology interference on scalability in the unlicensed band is negligible under low traffic conditions. Further, in [42], simulations are carried out and the performance of LPWAN technologies is evaluated in terms of signal propagation, coverage, and energy conservation; in LoRaWAN, if the sensitivity of the end nodes is increased by 10%, they can increase coverage by 142% without repercussions on energy consumption. In addition, the use of IoT devices in environments with a temperature of \(-20\)\({}^{\circ}\)C in LoRaWAN networks reduces their life by 53%. It is worth mentioning, in [46], a comparison between NB-IoT and LoRaWAN is made. This article compares these technologies in terms of the quality of service, cost, and energy consumption. In this work, the authors conclude that LoRaWAN is the one with the lowest costs and energy consumption, while NB-IoT has a better quality of service. Subsequently, experiments are made in different environments such as underground, underwater, and through metal in order to know the percentage of loss of a transmitted signal. After the experiments, it was concluded that NB-IoT has less signal loss in the different environments; however, it is the most expensive and consumes a greater amount of power compared to LoRaWAN. In addition, in [1], a study is carried out in which LoRaWAN, SigFox, and NB-IoT are compared in terms of quality of service, payload length, reach, latency, and power consumption. This paper concludes that Sigfox and LoRaWAN provide advantages in terms of battery life, scalability, payload length, and cost, while NB-IoT has better quality of service, longer payload length, and lower latency. Moreover, in [47], the authors carried out several simulations in OMNeT++, discovering that intense LoRaWAN networks can operate with variable traffic and high speed but will consume a large amount of energy and have a large number of collisions.
## 2 Materials and Methods
### Mechanical Design  
The core system includes  
1. Two eccentric micro-vibration motors, oriented 90\({}^{\circ}\) apart (X and Y axes).
2. A microcontroller-based astable multivibrator circuit using NPN BLC547 transistors to alternate motor activation.
3. Piezoelectric drivers for energy-efficient oscillation.
4. Plastic-compatible laser diodes for photonic treatment. 1. A total of 470 nm/(400 mW): Syringe wall pre-treatment to prevent adsorption. 2. A total of 700 nm/(500 mW): Continuous illumination to prevent aggregation during infusion.  
The total mechanical energy required for each vibration-induced agitation cycle is estimated to be approximately 0.3951 mJ per axis. Considering actuation along two orthogonal axes, the total energy consumption per cycle is approximately 0.7902 mJ. Agitation is applied intermittently to minimize interference with the primary forces exerted during the delivery process. The system includes two eccentric micro-vibration motors mounted perpendicularly, a custom transistor-based multivibrator circuit, and dual-wavelength laser diodes. Total power consumption is kept under 1.6 W. Photonic treatment helps reduce surface adsorption, while vibration ensures consistent drug dispersion.  
The core innovation of our approach lies in the integration of a two-axis vibration mechanism within the syringe pump. This system consists of (Figure 2):  
Figure 2: Proposed model of syringe pump with mechatronic homogenization system dual-axis vibration system.  
The 470 nm laser driver has the role of powering the laser diodes in the pre-filling phase of the syringe. This wavelength ensures a treatment of the polypropylene surfaces for 2 min. The automatic system announces by sound signal that the syringe is ready for the next stage, filling the syringe with the medicinal treatment. This diode automatically stops when fluids are present in the syringe so as not to distort the chemical structure of the drug by the presence of ultraviolet rays. The ultraviolet treatment of the syringe walls has the role of reducing the adhesion of chemical substances such as insulin to the syringe walls. The lower the adhesion to the syringe walls, the greater the degree of homogeneity of the substance injected into the patient.  
The 700 nm driver remains permanently on during the entire duration of the drug distribution; its role is to anti-aggregate the prepared drug.
# A Green Routing Protocol with Wireless Power Transfer for Internet of Things
## 7 Conclusions  
This paper proposes a new Objective Function for RPL protocol, named OF2, aiming at improving the global IoT network lifetime. To this end, OF2 jointly considers, for deriving the routing metrics, the consumed and recharged energy in order to select the best path to forward a data message. Specifically, we consider SWIPT technology for the recharging. The proposed OF2 allows an effective balancing of the energy distribution among the nodes in a DODAG, allowing the selection of a node with low battery level, since it can be recharged by the signal power received from a child. Performance evaluation conducted on a realistic scenario has shown the proposed OF2 effectively minimizes the energy consumption and, consequently, the network lifetime by selecting the best paths toward the sink. In particular, when compared with OFs commonly adopted, it is possible to point out a lifetime increasing of about 54% with respect to basic RPL (OF0) and 14% with respect to RPL with metric based only on residual level (OF1). Thus, the main contribution of this work is a new objective function for RPL that can minimize the energy consumption for typical resource constrained IoT applications.  
Figure 6: Jain’ index of the residual normalized battery level as a function of time for the considered RPL Objective Functions.  
**Author Contributions:** All the authors contributed equally to the paper. [PERSON] coordinated the research activities, F.C. and L.P. mainly worked on the system model and on the development of results. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data sharing is not applicable to this article.  
**Acknowledgments:** The Authors would like to sincerely thank [PERSON] for his contribution to the discussion by providing fruitful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## Appendix C Proof of Theorem 3
\begin{split}& h\Bigg{(}\frac{\mathbf{g}_{\text{se}}\mathbf{h}_{\text{ sd}}^{\mathcal{H}}}{|\mathbf{h}_{\text{sd}}|}X_{1}^{\prime}+\boldsymbol{\eta}_{e,1} \Bigg{)}-h\Big{(}\boldsymbol{\eta}_{e,1}\Bigg{)}\\ \leq&\log\det\!\left(\pi e\!\left(\!\frac{\mathbf{g}_ {\text{se}}\mathbf{h}_{\text{sd}}^{\mathcal{H}}}{\|\mathbf{h}_{\text{sd}}\|}X_ {1}^{\prime}+\boldsymbol{\eta}_{e,1}\!\right)\!\left(\!\frac{\mathbf{g}_{\text {se}}\mathbf{h}_{\text{sd}}^{\mathcal{H}}}{\|\mathbf{h}_{\text{sd}}\|}X_{1}^{ \prime}+\boldsymbol{\eta}_{e,1}\!\right)^{\mathcal{H}}\!\right)-\log\det\! \left(\pi e\sigma_{e}^{2}\mathbf{E}_{D}\right)\\ =&\log\det\!\left(\pi e\!\left(\frac{\mathbf{g}_{ \text{se}}\mathbf{h}_{\text{sd}}^{\mathcal{H}}\mathbf{h}_{\text{sd}}\mathbf{ g}_{\text{se}}^{\mathcal{H}}}{\|\mathbf{h}_{\text{sd}}\|}P+\sigma_{e}^{2} \mathbf{E}_{D}\right)\right)-\log\det\!\left(\pi e\sigma_{e}^{2}\mathbf{E}_{D }\right)\\ \overset{(\text{e}_{1})}{=}&\log\det\!\left(1+ \frac{\mathbf{h}_{\text{sd}}\mathbf{g}_{\text{se}}^{\mathcal{H}}\mathbf{g}_{ \text{se}}^{\mathcal{H}}\mathbf{h}_{\text{sd}}^{\mathcal{H}}}{\|\mathbf{h}_{ \text{sd}}\|\sigma_{e}^{2}}P\right)=\log\!\left(1+\frac{\|\mathbf{h}_{\text{sd }}\mathbf{g}_{\text{se}}^{\mathcal{H}}\|^{2}P}{\|\mathbf{h}_{\text{sd}}\| \sigma_{e}^{2}}\right)\!,\end{split} \] (122) where \((e_{1})\) follows from \(\log\det(\boldsymbol{AB}+\mathbf{E})=\log\det(\boldsymbol{BA}+\mathbf{E})\) and \(\mathbf{E}_{D}\) represents a \(D\times D\) identity matrix. Substituting (121) into (64), we conclude that \[\Delta_{I}\geq 1-\frac{\overset{\text{Information leaked by Source at time 1}}{\log\!\left(1+\frac{\|\mathbf{h}_{\text{sd}} \mathbf{g}_{\text{se}}^{\mathcal{H}}\|^{2}P}{\|\mathbf{h}_{\text{sd}}\|\sigma _{e}^{2}}\right)}}{N\mathcal{R}_{\text{FRC}}^{\text{NFR}}(N,e)}\] (123) where \(\mathcal{R}_{\text{FRC}}^{\text{NFR}}(N,e)\) is given in (50). \[
# Bearing Estimation for Indoor Localization Systems Using Planar Circular Photodiode Arrays
## 4 Measurements and Evaluation
### Light Intensity
#### 4.2.1 Effects of Reflective Surfaces  
For examining the effect of reflecting surfaces, a series of measurements were conducted utilizing the test setup depicted in Figure 7. In the measurements four beacons were placed at \(\pm 22.5^{\circ}\) and \(\pm 67.5^{\circ}\)  
Figure 8: Radiant sensitivity of the measurement channels. The plot shows the normalized reference matrix of a 16-channel PCPA. Different colors represent different rows of the matrix, corresponding to different channels; the \(m\)th value in a row is plotted at angle \(\varphi_{m}\). (**a**) All 16 channels of the PCPA. (**b**) For better, visibility 3 selected channels.  
and an aluminum foil was used as a reflective material. The reflective surface was placed at 0.5, 1.0, and 1.5 m from the center of the PCPA. During the measurements, the PCPA made a full rotation with a resolution of 2\({}^{\circ}\), and the light intensity values were recorded at each angular position.  
In Figure 9 the results of the experiments can be seen, where the blue lines represent the normalized light intensity values of the measurement channel facing 0\({}^{\circ}\). The angular placements of the beacons are represented with the red dots while the reflective surface is always at 180\({}^{\circ}\), denoted by a black arc; the length of the arc representing the viewing angle of the surface (the higher the distance between the surface and the PCPA the smaller the viewing angle). The first row of the figure represents the reference measurement where no artificial reflective material was present (but some unwanted reflection from the walls still may be present here).  
In Figure 9 several trends can be clearly seen: with decreasing distance between the PCPA and the reflective surface the deviations from the ideal (reference) characteristic are increasing. The most prominent abnormality is the bulb-like shape, created by the reflection of the beacon. In these angular positions the measured values should be zero because the photodiode at these positions is facing backwards, thus no direct line-of-sight is present.  
It is important to note that the measurement shown in Figure 9 is an extreme and conspicuous case of reflectivity, where a large piece (80 cm) of aluminum foil was used. For comparison, Figure 10  
Figure 9: Comparison of light intensity measurements with 4 beacons. From top to bottom: no reflective surface, aluminum foil at 1.5, 1.0, and 0.5 m. The red dot represents the angular placement of the beacon, black arcs denote the reflective surfaces.  
shows measurements of different materials with the same setup at a distance of 0.5 m. From the measurements it shows that the aluminum foil may have greater impact than the mirror (at least in the setup shown in Figure 10), probably due to the combined effect of reflection and scattering. Black paper has a minor, but still visible, effect, while white paper also shows significant impact.
## 4 Enabling Technologies for FSO Communication
### Aperture Averaging
#### 4.5.3 Impact of Aperture Averaging
The aperture averaging factor, denoted as \(A_{av}\), is described as the ratio of the normalized intensity variance in fluctuations for a Rx with a diameter \(D\) compared to that of a point Rx, and is given by [233; 235]  
\[A_{av}=\frac{\sigma_{N}^{2}(D)}{\sigma_{N}^{2}(0)}, \tag{43}\]  
where \(\sigma_{N}^{2}(0)\) represents the scintillation index for a point Rx with \(D=0\). In the regime of weak fluctuations, \(A\) can be estimated as [233; 235]  
\[A_{av}\approx\left[1+1.062\bigg{(}\frac{D^{2}}{4}\frac{k}{L}\bigg{)}\right]^{- 7/6}. \tag{44}\]  
The concept of aperture averaging to mitigate scintillation effects has been extensively studied both in theoretical research and practical applications [232; 234; 233]. The reduction in fading is commonly measured by the aperture averaging factor (refer to Equation (43)). Research indicates that a significant reduction in scintillation can be achieved, particularly under conditions of strong turbulence. Enhanced scintillation reduction might be possible by employing multiple beams at the Tx and/or multiple lenses at the Rx [233; 244; 245]. Additionally, the quest for a distribution that can precisely model the PDF of irradiance data across all AT conditions while considering aperture averaging continues to inspire significant research efforts [238]. To tackle this challenge, an alternative family of PDFs is presented to represent the PDF of received optical power in FSO communications, specifically the Weibull and EW distributions [46; 246]. Notably, the EW distribution demonstrates a remarkable fit to both simulation and experimental data across all aperture averaging scenarios, including weak and moderate turbulence regimes, and point-like apertures as well. Another appealing aspect of these distributions is the straightforward closed-form expressions for their corresponding PDF and cumulative distribution function (CDF) [238; 247; 248; 249; 250; 251].
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 3 Simulations
### Network Performance  
Two of the main features of our proposed system are the capability to interconnect isolated networks and to combine independent services. The performance of such features is heavily dependent on the network performance, which our proposed system tries to enhance. With the following simulations, we examine the performance of our proposed system's network. To simulate the methods as realistically as possible, we employ D-MUNS [26], a multiple UAV network simulator, which is based on ns3 [27] and RotorS [28].
## 2 Materials and Methods
### SPECT/CT Reconstructions
#### 2.3.2 Activity Quantification  
The calibration factors were obtained using the second phantoms acquisitions (with background activity). It was assumed that the region below the hot spheres region, which was uniformly filled with \({}^{166}\)Ho, was not influenced by the spheres themselves.  
Using Hybrid Viewer(tm), a total of 20 spherical VOIs were delineated in the background compartment. These VOIs were selected to have the same volume. The count rate within each VOI (\(\mathrm{C_{VOI_{i}}}\)) was recorded. The activity in each VOI (\(\mathrm{A_{VOI}}\)) was calculated by multiplying the activity concentration of the background compartment by the volume of the respective VOI. After applying Equation (4), the CF was calculated as a function of the iteration number for all reconstructions, also accounting for the influence of MC full collimator modeling. Alongside the CF, the associated uncertainty was also calculated, following the guidelines outlined in [17].  
\[\text{CF}=\frac{\sum\limits_{i=1}^{N}C_{\text{VOI}_{i}}}{N\cdot A_{\text{VOI}}} \tag{4}\]  
ARC as a function of sphere volume was determined using the first set of phantom acquisitions. An ARC for each insert was then determined using two different methods.  
In the first method, spherical VOIs matching the size of the physical insert were delineated on the CT scan and then copied into the corresponding SPECT image. The ARC for each sphere was calculated using Equation (5) (adapted from [14]).  
\[\text{ARC}_{i}=\frac{C_{i}}{\text{CF}\cdot A_{i}} \tag{5}\]  
Considering Equation (5), \(C_{i}\) is the total count rate within each VOI corresponding to insert i, CF is the calibration factor of the respective reconstruction, and \(A_{i}\) is the known activity in insert i (product of activity concentration of the spherical inserts and the volume of the respective insert).  
The second method used to determine the ARC followed the EANM (European Association of Nuclear Medicine) guidelines for uncertainty analysis [17]. Using Equation (6), it is the quotient between the observed count rate measured within the insert VOI, denoted as \(C_{i}\), and the count rate of all counts originating from an oversized VOI that encompass the sphere, denoted as Ctotal.  
\[\text{ARC}_{i}=\frac{C_{i}}{C_{total}}\times 100\% \tag{6}\]  
Finally, for each method, the ARC data fit was determined using the expression proposed by [17] and expressed through the following equation:  
\[\text{ARC}(v)=1-\frac{1}{1+\left(\frac{v}{b}\right)^{d}} \tag{7}\]
## 4 Proposed Solution
### Objective Function
\begin{array}{cc}-\frac{\exp{(RSS_{c}+RSS_{s})}}{ (\exp{(RSS_{s})}+\exp{(RSS_{s})})^{2}}&\frac{\exp{(RSS_{c}+RSS_{s})}}{(\exp{( RSS_{c})}+\exp{(RSS_{s})})^{2}}\\ \frac{\exp{(RSS_{c}+RSS_{s})}}{(\exp{(RSS_{c})}+\exp{(RSS_{s})})^{2}}&-\frac{ \exp{(RSS_{c}+RSS_{s})}}{(\exp{(RSS_{c})}+\exp{(RSS_{s})})^{2}}\end{array}  \right] \tag{14}\]  
the determinant of the Hessian matrix is zero, and both \(\frac{\partial^{2}f}{\partial x^{2}}\) and \(\frac{\partial^{2}f}{\partial y^{2}}\) are negative. This means that the Hessian matrix is NSD (as all the eigenvalues are non-positive for any \(RSS_{c}\) and \(RSS_{s}\) value). Thus, the function \(f\) is concave. Proof for Lipschitz continuity: to show that the gradient function \(\
abla f(p_{r})\) is locally Lipschitz continuous, it should be proven that there exists a positive constant \(L\geq 0\), such that \(\|\
abla f(RSS^{i+1})-\
abla f(RSS^{i})\|\leq L\|RSS^{i+1}-RSS^{i}\|\) in a bounded interval for \(RSS\in(RSS_{min},RSS_{max})\). Alternatively, one can prove this by showing that the derivative of \(\
abla f\) (which is \(\
abla^{2}f\)) exists and that \(\
abla^{2}f\) is continuous and bounded in the same interval of the RSS (using the theorem, a bounded derivative implies Lipschitz continuity [41]). We can come to this conclusion by analyzing \(\
abla^{2}f\) in Equation (14)and noting that in a bounded interval of the RSS \((RSS_{min},RSS_{max})\) set by the threshold limits, the function \(\
abla^{2}f\leq K\) is bounded with a constant \(K\) depending on the RSS interval. Stochastic optimization methods can work with noisy gradient measurements, which is a vital requirement, because the RSS measurements usually contain random variations that can be treated as a measurement noise in the optimization problem. Therefore, the key factor driving the choice of a stochastic optimization method (as a solution to Problem 2) is that the environmental factors affecting the RSS are not known prior, and hence, an optimization method that is both adaptable (to environmental changes) and robust (to noise) is needed. Though we can reach the global optimum if \(f(x)\) is concave, the stochastic nature of the RSS is not easing this phenomenon, and hence, the objective function has multiple local optima. With careful pre-processing techniques discussed in the following subsection, the local optima issue can be mitigated. Nevertheless, as the objective is to reach the optimum in a localized region, the issue of global optimization is not a concern.
## 1 Introduction
(2019) proposed an improved BI-RRT trajectory planning algorithm containing an artificial potential field to solve the problems of low support efficiency and low labor intensity of mechanized drilling and anchoring equipment in coal mines. Compared with the traditional RRT algorithm, the algorithm increased the optimization speed by 69.8% and shortened the trajectory length by 46.6%. Aiming at the problems of autonomy, safety, and efficiency of tea picking robots in tea gardens, [PERSON] et al. (2019) proposed an adaptive stepping RRT* (as-RRT *) path planning algorithm for tea picking robot arms. The experimental results demonstrate that the AS-RRT* algorithm reduces the path length by 14.18%, and the path planning time is less than 1 s. The proposed algorithm and the two improved algorithms are all improved versions of the RRT algorithm. The proposed algorithm can be applied to many scenarios if modified according to the actual operation requirements of the robot arm and has strong applicability. The obstacle avoidance planning algorithm proposed in this paper is suitable for multi-objective picking of clustered sand fruit. Compared with traditional algorithms, it shows that the algorithm can optimize both picking efficiency and obstacle avoidance path. While improving the shortcomings of the ACO algorithm, such as long operation time, slow convergence, and a tendency to fall into local optimal solutions, the strong robustness and global searching ability of the ACO algorithm are retained. The algorithm providesa new method for fruit tree picking automation equipment, which helps to promote the intelligence and efficiency of modern agriculture. In the second chapter, the Realsense D415 depth camera was used to obtain the cloud image of fruit tree points, and the cylindrical box method was used to envelop the robot arm and fruit tree branches to construct the environment. In Section 3, the ACO algorithm is improved by improving the transition probability and optimizing the updating of pheromone concentration, and the simulation experiment is compared with the traditional ACO. In Section 4, the artificial potential field method is combined with the RRT algorithm, and the simulation experiment is compared with the traditional RRT algorithm. In Section 5, the improved YOLOv5 algorithm and depth camera were used to obtain the three-dimensional spatial coordinates of the picking points, and the self-made pneumatic coupling manipulator and AUBO-E robot arm were assembled to build an experimental platform. Combined with the obstacle avoidance algorithm proposed in this paper, the multi-cluster sand fruit picking experiment was carried out, and the experimental results met the actual picking requirements. Finally, in Sections 6 and 7, the experimental results are discussed, and the full text is summarized.
## 4 Results
### Evaluation of Single Models  
Table 3 shows the results of using embeddings derived from a single model. As can be seen, ADA-002 demonstrates superior performance compared to SBERT with respect to both Recall@3 and MRR, in all subjects and both exercise types, with the sole exception being biology quizzes for Recall@3. The McNemar test shows that Recall@3 of ADA-002 is significantly better than SBERT (\(p\) = 0.000) for study questions, although not for quizzes (\(p\) = 0.081). Both ADA-002 and SBERT significantly outperform ConceptNet Numberbatch (both \(p\) = 0.000). The results are in line with [34], where contextual embeddings are used to represent entity descriptions in the entity typing task, and the results validate the superiority of ADA-002 in semantic representation over DistilBERT, a variation of the BERT model.