esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Directions  
### Conclusions  
Advancements in HPSO for adaptive equalization are pivotal for addressing limitations and enhancing practical implementation. Research must target simplifying the algorithm while upholding performance, optimizing hybridization for reduced computational overhead and automating parameter selection for enhanced efficiency. Efforts should prioritize enhancing integration compatibility, robustness, and generalizability across diverse equalization schemes and problem contexts. The utilization of user-friendly frameworks and libraries could streamline development and foster HPSO adoption, ultimately leading to improved BER performance in real-world adaptive equalization applications.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions
### Coherence  
The hybrid of cellular and ad-hoc-based V2V architectures is effective, but integration and coherence remain the main issues. Issues include synchronization and data transfer, network coverage and performance differences, high implementation costs, and cumbersome processes.
# Detection of Transmission State of Multiple Wireless Sources: A Statistical Mechanics Approach
## 5 Results Using the Replica Approach
We then generate a random \(T\)-Poisson-distributed number \(r\) and pull \(r\) random samples from the \(H\) bin, which are then used to evaluate the function \(\tanh^{-1}[\epsilon_{\beta}(H)]\), and we associate this number with a randomly chosen element in the \(\widehat{H}\) bin. This procedure is iterated until the distributions of \(\widehat{H}\) and \(H\) cease to vary over time. As we can see, Equations (17) and (24) are at the heart of the above algorithm. When this message-passing approach converges to a steady state, the population and resulting empirical distributions, \(Q(H)\) and \(\hat{Q}(\widehat{H})\), no longer vary. The distributions of fields \(H\) and \(\widehat{H}\) for \(\beta=\infty\) are depicted in Figure 3.  
It should be noted that the probability of error (37) includes only cases where there is connectivity of each source with at least one SU. Thus, to normalize the source-outage  
Figure 3: \(Q(H)\) and \(\hat{Q}(\widehat{H})\) distributions of populations (fields) \(H\) and \(\widehat{H}\), respectively, for \(\beta\to\infty\) and resolution \(K=1000\). The criterion we used for the population dynamics algorithm to stop was to examine whether there were no fluctuations in these two fields after a specific number of iterations. In practice, the algorithm converges fast enough, always as a function of the resolution \(K\). Obviously, there is a trade-off between accuracy and convergence speed. probability by taking this observation into account, we must consider the probability of a primary source being completely unconnected to the secondary network, which is simply \(\mathbb{P}_{\text{unc}}=\exp[-R]\). Taking this into account, the resulting total error detection rate can be written as  
\[\mathbb{P}_{\text{total}}=\mathbb{P}_{\text{unc}}+(1-\mathbb{P}_{\text{unc}}) \mathbb{P}_{\text{err}} \tag{38}\]  
In concluding this section, it is worth summarizing the results [23]. First, we applied the message-passing algorithm to reliably detect the operational state of primary sources in a large random wireless network. At the same time, we developed an analytic methodology based on statistical physics, which obtains the probability of error of such an algorithm subject only to the statistical characteristics of the network. This analytic framework inputs the densities of primary users and secondary users, as well as the statistics of the propagation environment. From these inputs, it calculates the (Poisson) distribution of the connectivity of the bipartite network of primary users and SUs. Including the thermal noises in the detection process results in Equations (36) and (35), which then determine the densities \(Q(H)\) and \(\hat{Q}(\hat{H})\) of the effective message fields for such a resulting bipartite random graph. From the above densities, we finally derive the probability of error detection in (37) and then (38).
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Scenario** & **Transmitter** & **Minimum** & **Maximum** & **Mean (SD)** & **Geometric** & **Median** & **25 th–75 th** & **\%\textgreater{}14 V/m** & **\%\textgreater{}27.4 V/m** & **Compliance** \\
**No** & & & & & & & **Mean (GSD)** & & **Percentile** & & \\ \hline A & VHF band fixed & 0.240 & 8.670 & 2.629 (2.593) & 1.582 (2.916) & 1.700 & 0.603–7.107 & 0.00 & 0.00 & Yes \\  & (IC-M505) & & & & & & & & & \\ \hline B & VHF band fixed & 0.290 & 14.250 & 3.855 (4.117) & 2.066 (3.250) & 1.525 & 0.820–7.107 & 4.17 & 0.00 & Yes \\  & (Sailor 6006) & & & & & & & & \\ \hline C & MF/HF transceiver & 0.210 & 3.420 & 0.585 (0.361) & 0.505 (1.691) & 0.540 & 0.342–0.637 & 0.00 & 0.00 & Yes \\ \hline D & Satellite communication & 0.000 & 0.790 & 0.445 (0.162) & 0.415 (1.479) & 0.490 & 0.295–0.540 & 0.00 & 0.00 & Yes \\ \hline E & AIS navigation & 0.070 & 0.720 & 0.456 (0.144) & 0.432 (1.409) & 0.490 & 0.340–0.570 & 0.00 & 0.00 & Yes \\ \hline F & Radar X-band & 0.180 & 4.250 & 0.595 (0.330) & 0.531 (1.590) & 0.540 & 0.457–0.700 & 0.00 & 0.00 & Yes \\ \hline G & Radar S-band & 0.070 & 12.450 & 1.958 (2.490) & 1.081 (2.823) & 0.895 & 0.422–1.915 & 0.00 & 0.00 & Yes \\ \hline H & Group Z & 0.070 & 22.540 & 3.429 (5.599) & 1.416 (3.610) & 1.245 & 0.462–2.840 & 8.33 & 0.00 & Yes \\ \hline I & Group Z + VHF fixed (IC-M505) & & & & & & & & \\ \hline J & Group Z + VHF fixed (Saulor 6006) & & 27.520 & 6.227 (7.690) & 3.170 (3.170) & 2.300 & 1.110–9.390 & 16.66 & 4.16 & Yes \\ \hline K & Group Z + MF/HF transceiver & 0.230 & 20.160 & 3.144 (5.031) & 1.332 (3.386) & 0.970 & 0.512–2.302 & 4.16 & 0.00 & Yes \\ \hline \hline \end{tabular}  
* The limits for the general public (e.g., passengers) were exceeded on the bridge roof, and the occupational limits were respected everywhere.  
\end{table} 
Table 7: The descriptive statistics for electric field levels (V/m) emitted from the marine transmitters from each scenario. Larger surveys are desired to verify our findings and to provide reliable knowledge on radio hazard safety assessment for marine ship transmitters and the electromagnetic compatibility (EMC). As indicated in [15], the sensitivity of the location and standardised and well-described data collection settings are significantly important and should be considered in the comparison of different studies. To reduce electromagnetic fields, marine vessel designers may use low permeable materials, such as glass reinforced plastic (GRP), wood, aluminium hulls in the vessel and equipment construction [16]. The magnetic fields reduce rapidly with distance; hence, equipment containing inevitable magnetic sources, such as electric motors and transformers, should be located as far away as possible from areas normally occupied by crew members and passengers.
## 3 New Techniques Used in Detection of Forest Fire in China
### A New Algorithm for Fire Burned Areas Identification  
Many techniques use a set of thresholds based on spectral bands together with a time series analysis to detect rapid and dramatic land cover changes in order to monitor burned areas [67]. Some algorithms developed in the past decade included Global Environment Monitoring Index (GEMI) [68,69]; VI3T [70]; Shortwave Vegetation Index (SWVI) [71], Burn Area Index (BAI) [72], Normalized Burn Ratio (NBR) [73]. Based on the GEMI and spectral characteristics of burned areas in the short wave region, [PERSON] _et al._ developed an adjusted GEMI-Burned areas (GEMI-B) method to accurately extract the fire burned areas in grasslands of China by using medium-resolution EOS-MODIS satellite imagery with channels 5 and 7 [74] An automated extraction procedure was also developed using active fire (hot spot) as seeds and GEMI-B image. The GEMI-B algorithm was used to detect fire occurrence in the border areas between China and Mongolia on 21-31 May, 2003. The result showed that the burned areas extracted by the new method were consistent with that from the high resolution Landsat TM data [74].
## 2 Absorber Design
### Incidence Angle and Polarization Dependence  
The proposed design holds a highly symmetric configuration, and should have strong independence from the polarization and incidence angles of the incoming EM wave to the absorber. The calculated absorbance rates for the various polarizations and incidence angles are plotted in Figure 6. Again, this is simulated when the unit cell of the proposed absorber is set as an infinite array. First, the absorbance is calculated when the absorber is rotated along \(\phi\)--from 0\({}^{\circ}\) to 90\({}^{\circ}\)--with the incoming TEM plane wave. As expected, the absorbance does not change at all but stays the same because of the perfect symmetric configuration of the SRRs. In Figure 6b,c, the absorbances are compared when TE and TM mode waves are incoming; the TE mode polarization is made by rotating the absorber along the \(E\)-axis, as shown in the inset of Figure 6b, with the TEM incoming wave. The TM mode polarization is made via the same method, but, this time, the absorber is rotated along the \(H\)-axis, as shown in the inset of Figure 6c. In both cases, \(\theta\) changes from 0\({}^{\circ}\) to 30\({}^{\circ}\). Overall, a steady absorbance is observed at wide operating frequencies, with a little degradation of up to 80% in frequencies over 25 GHz. This is because the surface impedance seen from the incoming wave is deviated from 377 ohm as \(\theta\) increases, resulting in a higher reflection.  
Figure 5: Study of the frequency of 2 GHz with a low absorbance. (**a**) Surface current distribution, and (**b**) surface power loss.
Sensors_**2014**, _14_, 14932-14970; doi:10.3390/s140814932  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Holistic Approach to ZigBee Performance Enhancement for Home Automation Networks**  
**[PERSON] \({}^{1,2,*}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{1,2}\)**  
\({}^{1}\) Department of Telematics Engineering, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain;  
E-Mails: [EMAIL_ADDRESS] (C.G.); [EMAIL_ADDRESS] (I.D.);  
[EMAIL_ADDRESS] (J.P.)  
\({}^{2}\) Fundacio i2 CAT, 08034 Barcelona, Spain  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-93-401-7041; Fax.: +34-93-401-1058.  
_Received: 9 June 2014; in revised form: 18 July 2014 / Accepted: 29 July 2014 / Published: 14 August 2014_  
**Abstract:** Wireless home automation networks are gaining importance for smart homes. In this ambit, ZigBee networks play an important role. The ZigBee specification defines a default set of protocol stack parameters and mechanisms that is further refined by the ZigBee Home Automation application profile. In a holistic approach, we analyze how the network performance is affected with the tuning of parameters and mechanisms across multiple layers of the ZigBee protocol stack and investigate possible performance gains by implementing and testing alternative settings. The evaluations are carried out in a testbed of 57 TelosB notes. The results show that considerable performance improvements can be achieved by using alternative protocol stack configurations. From these results, we derive two improved protocol stack configurations for ZigBee wireless home automation networks that are validated in various network scenarios. In our experiments, these improved configurations yield a relative packet delivery ratio increase of up to 33.6%, a delay decrease of up to 66.6% and an improvement of the energy efficiency for battery powered devices of up to 48.7%, obtainable without incurring any overhead to the network.  
**Keywords:** ZigBee; home automation; wireless sensor networks; IEEE 802.15.4; performance improvement
# Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems
## 2 Materials and Methods
### MPA Algorithm
The following solution-improving agents were defined and implemented:  
* **randomMove--**moves a random element from the list to another, random position,
* **bestMove--**takes a random element from the list and moves it to a position maximizing fitness gain;
* **randomReverse--**takes a random slice of the list and reverses the order of its elements;
* **bestReverse--**takes a random element from the list and reverses the order of elements on all of possible consecutive sublists of different length starting from that element. Solution maximizing fitness gain is finally selected;
* **crossover--**requires two solutions. It takes a slice from one solution and adds to it the missing elements exactly in an order as in the second solution. Algorithm 2 shows how the **crossover** operation is implemented as Scala function _cross_. We also show how the list representing the example solution changes as the result of crossing it with another solution. ```
defcross(11:List[Int],12:List[Int],from:Int,len:Int)={ valslice=11.slice(from,from+len) valrest=12 diffslice rest.slice(0,from)::slice::rest.slice(from,rest.size) }
```  
solution1: 0,5,9,7,3,1,4,6,8,2 solution2: 0,1,4,2,8,6,9,5,3,7 slice of solution1: 1,4,6 result: 0,2,8,9,5,1,4,6,3,7 ```
**Algorithm 2** Function _cross_ and Example of Its Usage  
The MPA outlined above may be used for solving various combinatorial optimization problems in which a solution can be represented as the list of numbers. Among such problems there are the traveling salesman problem (TSP) and job shop scheduling problem (JSSP).
## 2 Data and Methods
### SSD Network Framework  
In this sub-section, the general architecture of the SSD framework is presented initially. After, the default bounding box and negative sample generation procedures are explained. Next, the loss function and detection flow steps are presented.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image Area** & **Acquisition Date** & **Incidence Angle (\({}^{\circ}\))** & **Sun Elevation Angle (\({}^{\circ}\))** & **Surface Area (km\({}^{2}\))** \\ \hline Ataturk Airport & 4/13/2017 & 21.16 & 54.43 & 15.03 \\ Esenboga Airport & 9/27/2015 & 19.4 & 46.42 & 14.97 \\ Sabiha Gokeen Airport & 4/29/2017 & 29.77 & 62.28 & 17.93 \\ Antalya Airport & 5/3/2017 & 16.88 & 64.61 & 23.42 \\ Izmir Airport & 3/28/2017 & 21.19 & 52.31 & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of Pleiades satellite images used for model construction and independent testing.  
Figure 2: Number of the pixel size of the airplane bounding boxes in the dataset.
## 3 Scaling Laws of Scheduling Gain--Perfect CSI  
In this section, we derive the scaling laws of scheduling gain for optimal user scheduling in terms of the number of BS antennas under the assumption of perfect CSI at the BS, i.e., no channel estimation errors. Thus, we obtain the variance of the individual rate and a closed-form expression for the achievable scheduling gain.
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 5 Prototype
### Dynamic Capacitor Switching  
In the final experiment, we demonstrate the practical feasibility of the hybrid dual-capacitor configuration for a simple sensing and data collection application, which senses a temperature reading, saves it to a flash storage and transmits wirelessly to the base station. To evaluate the dynamic switching storage, a sensor node was periodically moved from a distance of 3 to 1 m from the RF charging station to emulate low and high energy areas, respectively. The RSSI threshold was set to 2 dBm representing a distance of approximately 2 m, while the sojourn time near the charging station has been set to 40 s. In practice, these parameters are application dependent and can be tuned depending on the mobility of the node and transmit power of charging stations. The sensor node was configured to use the small capacitor at the initial boot. Whenever the node sensed that RSSI as higher than threshold value, it estimated the collocation time and saved it into a flash memory after each visit. After the third visit, the node was able to predict correctly that the collocation time within the high energy area would be sufficient to switch to a large capacitor, and after completing the switch, the node successfully sent the collected data to the base station. When moved out of the high energy area, the node waited until the large capacitor voltage dropped below 1.045 V to switch back to the small capacitor and resume the sensing task. The experiment has been repeated in a single fixed-capacitor configuration of either 50 mF and 200 mF by varying the sojourn time between 40 s and 10 s. With a 40-s sojourn time, both small and large capacitors were able to boot the node; however, only the large one was able to support the transmit operation. When the sojourn time was reduced to 10 s to mimic environments with lower energy, the node has never been able to boot on a large capacitor, but boosted successfully on a small one.
## 2 Theory
As an example, Figure 4a displays \(OX\) profiles calculated at the end of a \(\tau\) = 1 s burst, \(T\)(x,0,0,\(\tau\)), produced by a squared heat source of the same dimensions (\(w\) = \(h\) = 2 mm) and flux (\(\Phi\) = 10 kW/m\({}^{2}\)) as in Figure 3, that makes an angle of \(\varphi\) = 20\({}^{\circ}\) with the surface, for three different depths, \(d\) = 0.001, 0.2, and 0.6 mm. We can see that, as the depth increases, not only the signal diminishes, but also the curve is smoothed out, making the identification of the angle more difficult. In Figure 4b we illustrate the dependence of \(OX\)-profiles with the duration of the burst. We show \(T\)(x,0,0, \(\tau\)) corresponding to a heat source buried \(d\) = 0.01 mm, with the same geometry and heat flux as in Figure 4a, calculated at the end of three different bursts, \(\tau\) = 0.1, 1, and 10 s. For this geometry, the deepest side of the rectangle is buried \(d_{max}\) = 0.69 mm below the surface. As can be seen, the duration of the burst has a significant impact on the \(OX\) temperature profiles calculated at the end of the burst. If the burst is short (\(\tau\) = 0.1 s, associated thermal diffusion length \(\mu_{\tau}\) = \(\sqrt{4D\tau}\) = 0.23 mm, black line), the asymmetry of the profile is very pronounced because the heat produced at the deepest side of the square does not have time to reach the sample surface by the end of the burst. As the burst increases (\(\tau\) = 1 s, \(\mu_{\tau}\) = 0.72 mm, blue line), the information coming from deeper locations has reached the surface by the end of the burst, still featuring a noticeable asymmetry, characteristic of the inclination. If the burst is long (\(\tau\) = 10 s, \(\mu_{\tau}\) = 2.3 mm, red line) the heat coming from any position of the heat source has reached the surface. The profile contains information from all locations and is smoothed out by diffusion. It can also be noticed that in this evolution with \(\tau\), the position where the temperature is maximum at the end of the burst moves to the right, i.e., towards the geometrical center of the heat source. The previous analysis points out that the duration of the burst might affect the accuracy with which the dimensions, depth, and orientation of a rectangular heat source is determined. In the next section, we present a sensitivity analysis aimed at studying the optimum conditions to characterize rectangular heat sources of any inclination.
# An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space
## 2 Methods
### Description of the PnP Mathematical Model
\begin{array}{ccc}-X_{i}+Z_{i}u_{i}&2Y_{i}u_{i}&-2Z_{i}-2 X_{i}u_{i}&2Y_{i}&-X_{i}-Z_{i}u_{i}&-2Y_{i}&2X_{i}u_{i}-2Z_{i}&X_{i}-Z_{i}u_{i}&2Y_{i}u_{i}&X _{i}+Z_{i}u_{i}\\ -Y_{i}+Z_{i}v_{i}&2Z_{i}+2Y_{i}v_{i}&-2X_{i}v_{i}&-2X_{i}&Y_{i}-Z_{i}v_{i}&-2 X_{i}&2X_{i}v_{i}&-Y_{i}-Z_{i}v_{i}&2Y_{i}v_{i}-2Z_{i}&Y_{i}+Z_{i}v_{i} \end{array} \right]\]  
Since \(n\) spatial reference points all satisfy Equation (8), it can be obtained as follows:  
\[\left[ \begin{array}{c}A_{1}\\ A_{2}\\ A_{3}\\ \vdots\\ A_{n}\end{array} \right]\hat{t}=\left[ \begin{array}{c}N_{1}\\ N_{2}\\ N_{3}\\ \vdots\\ N_{n}\end{array} \right]v\Leftrightarrow A\hat{t}=NL \tag{9}\]  
In the case that L has been specified, the linear least square fitting is used to obtain:  
\[\hat{t}=A^{+}NL \tag{10}\]  
where \(A^{+}=(A^{T}A)^{-1}A^{T}\) is the inverse matrix of Moore-Penrose matrix. Equation (10) is substituted into Equation (8) to obtain:  
\[(A_{i}A^{+}N_{i}-N_{i})L=0\Leftrightarrow\left[ \begin{array}{c}A_{1}A^{+}N -N_{1}\\ A_{2}A^{+}N-N_{2}\\ A_{3}A^{+}N-N_{3}\\ \vdots\\ A_{n}A^{+}N-N_{n}\end{array} \right]L=0 \tag{11}\]  
\[\text{Let }J=\left[\begin{array}{c}A_{1}A^  
\[J=\left[ \begin{array}{ccccccccc}j_{11}&j_{12}&j_{13}&j_{14}&j_{15}&j_{16}&j_{17 }&j_{18}&j_{19}&j_{1,10}\\ j_{21}&j_{22}&j_{23}&j_{24}&j_{25}&j_{26}&j_{27}&j_{28}&j_{29}&j_{2,10}\\ j_{31}&j_{32}&j_{33}&j_{34}&j_{35}&j_{36}&j_{37}&j_{38}&j_{39}&j_{3,10}\\ &&&\vdots\\ j_{2n,1}&j_{2n,2}&j_{2n,3}&j_{2n,4}&j_{2n,5}&j_{2n,6}&j_{2n,7}&j_{2n,8}&j_{2n,9}& j_{2n,10}\end{array} \right]\]  
Some existing PnP methods adopt the obtained Equation (12) to construct the objective function, and take the Gauss-Newton method, Levenberg-Marquardt iteration, orthogonal iteration, and other iterative methods, to solve the pose parameters. According to Section 1, the selection of the initial value and proneness to local optimum of the iterative methods undermine their calculation accuracy and speed. In addition, some PnP methods also use the Grobner technique to solve the polynomial sets constructed here, which overcomes the defects of the iterative methods. Instead, its operational process generally requires a huge elimination template matrix, given the complexity of PnP problems, which seriously affects the efficiency of the algorithm. Accordingly, Section 4 employs a hidden variable method to solve the pose parameters of CGR in Equation (12).
## 4 Results and Discussion  
### Mean Wind Characteristics  
Mean wind characteristics refer to the average state and behavior of the wind within a certain period and spatial range, serving as a crucial basis for the quantitative analysis of wind fields. By analyzing the mean wind characteristics of structures, a better understanding and analysis of wind behavior can be achieved. The mean wind characteristics include wind profiles, wind speed amplification factors, wind yaw angles, and wind attack angles, which are indispensable factors in bridge structural design. The distribution of mean wind characteristics at the bridge site forms the foundation for calculating wind loads. A precise analysis of mean wind characteristics could enhance the safety of bridge design while maintaining economic efficiency.