esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring ISAC: Information-Theoretic Insights
## 4 Sensing at the Rx (Rx-ISAC) with Sensing Distortion  
In certain practical systems, sensing is performed at a device that differs from the radar-emitting device. Such situations are often referred to as bi-static radar. The information theory literature has considered various bi-static ISAC scenarios [21; 61; 62; 88; 89]. In this section, we focus on bi-static ISAC where the sensing task is performed at the communication Rx, and the sensing task is to estimate the target (state) up to a given distortion constraint.  
The presented model captures one of the major challenges in bi-static ISAC, which is that the sensing terminal is a priori not aware of the channel input sequence and thus due to the memory in the channel input sequence, symbol-by-symbol estimators based solely on the observations are suboptimal. We shall see that when the sensing is performed at the communication Rx, this difficulty is easily solved by first decoding the data and  
Figure 5: RL average reward composed of \(\beta\) times the information rate and \((1-\beta)\) times the negative sensing distortion.  
reconstructing the input sequence, which contains all the memory in the system. In this case, a symbol-wise estimator based on this reconstructed input sequence and the observed sequence of channel outputs achieves minimum distortion (As we shall see in the section on Network ISAC, it is more complicated to characterize the optimal estimation strategy when the sensing terminal is not a priori required to decode all the transmitted data and codewords).
# The Status of Space Weather Infrastructure and Research in Africa
## 3 Human Capital Development
### Funding Sources  
Space weather research in Africa has been supported through a variety of funding streams. These include:  
1. External financing: Most of the space weather capacity building has been supported through external funding, especially for the acquisition and installation of research equipment, but local African universities have often provided their students with the necessary facilitation to enable them to undertake their research work. More often than not, the facilitation has been in terms of tuition fee waivers and in-kind support.
2. Government: Some national space agencies and research councils/agencies are now funding space weather research and training workshops to build a critical mass of expertise within the shortest time possible.
3. Industry: A few industry stakeholders, like the aviation industry and electric power distribution companies, in some few African countries have also come on board to support space weather research, although their involvement is still at a very low level. The challenge here has been that the industry players are not aware of the relevance of space weather research to their businesses. This calls for more awareness campaigns to enlighten the industry, and thus possibly attract more funding from the sector.
## 3 Results and Discussion
### Bentzo[a]pyrene Equivalent Toxicity of PAH Mixtures in the Adriatic Sea  
Toxic equivalent quantity (TEQ) for 9 known carcinogenic PAH compounds and for 16 EPA compounds, calculated for the studied sediment samples are shown on Figure 8. It can be seen that summed TEQ for 9 and 16 PAHs exceeded 600 ug kg\({}^{-1}\) only at the stations in Sibenik Bay. At the remaining stations, the TEQ values were below 200 ug kg\({}^{-1}\), regardless of the number of compounds considered. In contrast, TEQ values in Sibenik Bay were significantly higher, especially at the stations near the former ferroalloy factory and the tourist marina. When all 16 PAH compounds were considered, TEQ values at most stations exceeded 600 ug kg\({}^{-1}\), indicating a high carcinogenic risk associated with sediment PAH concentration in Sibenik Bay. Results obtained by calculation of TEQ are in good agreement with the results of PAH-related sediment toxicity obtained by applying the sediment quality guidelines, where a high environmental risk was observed for the total area of Sibenik Bay [38]. However, in the same study, the results of the bioassay application indicated poor environmental status associated with the PAH sediment concentration at stations outside Sibenik Bay. This suggests that assessing the toxicity of PAH solely on sediment concentration may significantly underestimate the environmental risk [38].  
Figure 8: Toxic equivalent quantities for 9 known carcinogenic PAH and 16 EPA PAH in the sediments of investigated stations in the Adriatic Sea.
## 3 Results
### Industrial Trial Result  
Figure 7a,b shows the zinc concentration in the slag bath over the batch time of one the trials with only coal injection, and one of the trial with coal and SRM, respectively. The slope of the line indicates the zinc-reduction rate (wt %/min). The average of zinc reduction rate and the standard deviation was calculated for the trials with each material and reported in Table 3.  
The rate of coal injection during the trial with only coal injection was 6.3 ton/h, which was reduced to 5.6 ton/h and supplemented by 1 ton of SRM. The important finding is that, even though the coal injection was reduced by 0.7 ton/h, the zinc reduction rate was not significantly affected. This indicates that it is possible to partially substitute coal with plastic materials in a zinc fuming process.  
Figure 6: Total conversion times for the tested materials at different particle sizes.  
Figure 7: Zinc-reduction rate with (a) coal injection, (b) the co-injection of coal and SRM.  
Among the trials with the co-injection of plastic materials and coal, the trials with PUR showed the highest zinc reduction rate. It is possible to calculate the amount of plastic material, which would correspond to the reduced coal injection, while the carbon amount entering the process remained the same. In that case, 1.5, 1.9 and 2.1 ton of PE, PUR and SRM, respectively, should have been injected to the process. However, the amount of PE and SRM injected was lower, while the amount of PUR injected was higher (Table 2). To have a better comparison between the plastic materials, the average rate of the total molar content of reducing elements (C and H\({}_{2}\)) for trials with different plastic materials was also calculated and is presented in Table 3. Among the plastic materials, PUR had the highest rate per reducing elements, while SRM and PE were similar.  
Another important change during the trials with injection of plastic materials was the steam generation. The steam generation during the trials with only coal injection was approximately 400 kg/min; while it increased to average amounts of 500 kg/min, 620 kg/min, and 475 kg/min for the co-injection of PE, PUR, and SRM, respectively. The ratio of the produced steam in kg/min to the plastic material that was charged were 1.6, 1.76, and 1.7 for PE, PUR, and SRM, respectively. The results indicate that PUR had the highest value for the calculated steam production per material injected. The increase in steam generation suggests that part of the plastic materials injected did not react in the slag bath and it combusted at the combustion shaft and participated in steam generation.
# A Quad-Band RF Circuit for Enhancement of Energy Harvesting
## 5 Simulation Results and Discussion
On the other side, the proposed design achieved conversion efficiency higher than the reported in [52; 55; 57; 59; 61; 69; 74; 78] whereas it is close to the reported in [3]. Moreover, in [66], the efficiency is 40% if there is a single frequency incident upon the antenna. The efficiency is doubled when there are 4 frequencies whereas in our work the efficiency is nearly stable. It is 80% when there is a single frequency available as shown in Figure 23. Furthermore, in [67] the measured results showed that the maximum harvested DC power in typical outdoor and indoor environments was 26 \(\mu\)W and 8 \(\mu\)W, respectively. This comparison clearly shows the importance of this work since the conversion efficiency and output power have been enhanced beside the number of harvested frequency bands. In our study, the output power is 2 mW = 2000 \(\mu\)W, as shown in Figure 22.  
Figure 23.0 A chart for the conversion efficiency for the quad band vs. load variation.
# An IoT-Based Solution for Intelligent Farming +
## 2 Related Work
### IoT/M2M Protocols and Communication Technologies
#### 2.1.6 Non-IP Protocol Stacks for M2M  
There are several applications that avoid the use of IP-based solutions in order to optimize resources, processes and communications. Therefore, there are several approaches that resort to non-IP communication solutions, either resorting to ALs, implementing communication stacks as a whole, or both.  
ZigBee is a practical example of a full communication stack. It is based on the IEEE 802.15.4 standard for the Physical and MAC layers, the higher layers being defined by the Zigbee Alliance [19]. It aimed at offering a low power wireless communication mechanism and gained particular popularity on WSN deployments. However, as introduced in Section 2.1.1 IEEE 802.15.4 presents relevant constraints when considered for TDMA scenarios.  
BLE is another popular and trendy solution for low power communication solutions. It is an enhanced version of the classic Bluetooth, allowing communication ranges 10 times higher (around 100 m), lower radio power and lower latencies. Its use is more popular on WSN applications [40] and vehicle-to-vehicle communications [41], but besides its effective reduction of power consumption and increased range, it still presents severe limitations on both star and mesh topologies deployments [42].  
EPCglobal and Z-Wave can both be used as fully integrated solutions or by the mean of adaptation layers (as introduced in Section 2.1). The former is relevant for UHF RFID devices while the latter was designed for home automation applications.  
In sum, there are a multitude of solutions to deploying an IoT/M2M application, either in terms of protocol stacks or communications technologies. The standardization efforts regarding communications on those domains are impaired by the high heterogeneity of applications and their requirements.
# Verifying Mutual Exclusion Algorithms with Non-Atomic Registers
## 5 Modeling Examples with Non-Atomic Registers
### Tournament Tree-Based Mutual Exclusion Solutions  
As shown in [11; 14; 26; 27] a mutual exclusion algorithm for \(N=2\) processes can be exploited as the _arbitration unit_ in a _binary tournament tree_ (TT) to achieve a general, standard, and efficient solution for \(N\geq 2\) processes. For implementation purposes, the TT can be mapped onto the same linear array often used for supporting the heap sort algorithm. Processes enter the leaf nodes, which are located on the last level \(\lceil\log_{2}N\rceil\) of the TT. Intermediate nodes are indexed from 1 (the root node) to \(2^{\lceil\log_{2}N\rceil}-1\). The entering process \(i\), \(1\leq i\leq N\), deterministically occupies the leaf node \(j=2^{\lceil\log_{2}N\rceil}+i-1\). Position \(j\)_represents_ the process in its current position in the TT. Process \(j\) can admit a _sibling_ process at node \(s\), provided \(j/2==s/2\), that is, processes \(j\) and \(s\) share the same ancestor node \(j/2\). Pairs of sibling processes compete using the chosen algorithm for arbitrating 2 processes. The winner moves to the ancestor node, from where it will compete with another sibling and so forth. The process that first arrives at the root node gains permission to enter its critical section. Variables are assumed to represent the nodes the process visited during its movement in the TT. On exiting from the critical section, all the nodes that were previously visited in the upward movement (path) toward the root node are reset exactly in the opposite order, thus possibly awakening processes in arbitration nodes that can resume their advancement in the TT.  
Two representative cases are shown in this section. The first one (TT-P2) is related to the use of [PERSON]'s solution in a TT organization. The second one (TT-S2) exploits the algorithm for two processes derived from [PERSON]'s solution [25], which was previously described in this paper.
## 2 DTPPF Fault Identification Method
### Detuning Detection of DTPPF  
The impedance at the resonant frequency (\(h\)-th harmonic current) and the quality factor of the ECFR are provided by  
\[\left\{\begin{matrix}X_{(h)}=\omega_{h}L=\frac{1}{\omega_{h}\zeta_{s}}=\sqrt{ \frac{L}{\zeta_{s}}}\\ Q=\frac{X_{(h)}}{R}=\frac{\sqrt{\frac{L}{\zeta_{s}}}}{R}\end{matrix}\right. \tag{1}\]  
where \(X_{(h)}\) is the impedance at the resonant frequency (\(h\)-th harmonic current), \(Q\) is the quality factor, \(\omega_{h}\) is the angular frequency of the \(h\)-th harmonic current, and \(R\) is the equivalent resistance of the DTPPF, the value of which is generally quite small.  
The quality factor \(Q\) can characterize the sharpness of the impedance curve of the DTPPF, with the band-pass width of the frequency provided by  
\[\left|Z_{f}\right|=\sqrt{2}R \tag{2}\]  
where \(Z_{(f)}\) is the impedance of the DTPPF and \(Q\) is the quality factor.  
The resonant frequency shift is defined as  
\[\xi=\frac{\omega-\omega_{h}}{\omega_{h}} \tag{3}\]  
where \(\xi\) is resonant frequency shift and \(\omega\) is the actual angular frequency of the DTPPF.  
Here, \(R\) is ignored because it is very small, meaning that the reactance of the DTPPF is  
\[\left|X_{f}\right|=\left|\omega L-\frac{1}{\omega\zeta_{\rm s}}\right| \tag{4}\]  
where \(X_{f}\) is the inductive reactance of the DTPPF.  
Substituting Equations (1) and (3) into Equation (4), we obtain  
\[\left|X_{f}\right|=\left|\left(1+\frac{1}{1+\xi}\right)\xi\right|X_{(h)}. \tag{5}\]  
For \(\left|\xi\right|\leq 10\%\), Equation (5) can be simplified as follows:  
\[\left|X_{f}\right|\approx 2|\xi|X_{(h)}. \tag{6}\]  
When \(\left|Z_{f}\right|=\sqrt{2}R\), i.e., \(\left|X_{f}\right|=R=2\times(1/2Q)\times X_{(h)}\), the resonant frequency shift is  
\[\xi_{0}=\frac{1}{2}Q \tag{7}\]  
where \(\xi_{0}\) is the edge of the band-pass width frequency; when \(\xi>\xi_{0}\), the DTPPF is out of resonance.
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 3 Methodology
### Solving Algorithm
#### 3.2.2 Algorithm Process  
Based on the optimization model in Section 3.1 and the algorithm analysis in Section 3.2.1, the rolling optimization algorithm for solving the original model is presented in Table 1. This algorithm is referred to as the rolling optimization-based algorithm (RO-based).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline \multirow{2}{*}{**Step 1:**} & Initialize relevant parameters, such as the total optimization duration \(T\), the optimization time window \(\Delta T\), the free-flow speed of each road segment \(v_{i}\), the safety time headways \(\tau\) and \(\omega\), the current time window index \(k=1\), and the total number of time windows \(K=\lceil T/\Delta T\rceil\). \\ \hline \multirow{2}{*}{**Step 2:**} & **2.1:** Let \(t_{ij}^{-}\in[(k-1)\Delta T,k\Delta T]\), and substitute it into the optimization model. \\  & **2.2:** An interior point method is adopted to solve the linear programming model (Equations (3)–(8)). \\  & **2.3:** The optimal \(t_{ij}^{+}\) for the k-th time window \([(k-1)\Delta T,k\Delta T]\) is obtained. \\ \hline \multirow{2}{*}{**Step 3:**} & Check if \(k\geq K\). If true, output the optimal solutions for all time windows, which constitute the final optimal solution for the model; otherwise, set \(k=k+1\) and return to Step 2. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rolling optimization algorithm steps.
# Enhanced Beacons Dynamic Transmission over TSCH
## 6 Conclusions and Future Work  
The minimal 6 TiSCH configuration describes the association mechanism for TSCH through the periodic sending of enhanced beacons. The configuration of the time between beacons has a direct impact on the network formation time. The shorter the time between beacons, the less time necessary for a node to associate with the network and consequently the less time it will take for the network formation. Reducing the time required for a node to associate with the network guarantees that data packet transmission can start earlier. On the other hand, permanently reducing the time between beacons also implies an increase in traffic and energy consumption for the devices.  
The EBDT-TSCH mechanism proposed in this work performs dynamic management of the time between EB transmissions that permits starting with a high EB transmission rate to shorten the average time necessary for the nodes to get associated. After the intensive phase, during which all or most of the nodes could associate, the EB transmission rate is decreased to avoid unnecessary energy consumption.  
Both the theoretical model and simulation results confirm the effectiveness of the proposed approach. In scenarios where communication between nodes may fail due to channel conditions, the EBDT-TSCH mechanism also demonstrated a reduction in network formation time and energy consumption required for association compared to the minimal 6 TiSCH configuration. The implementation of EBDT-TSCH is a simple solution that does not imply a high implementation cost and can be used in any application that uses the association mechanism proposed by 6 TiSCH.  
A future extension of our work aims to combine the proposed mechanism with the information extracted from the routing protocol to optimize the duration of the intensive phase. We also intend to evaluate the performance of the proposed mechanism in scenarios with greater node density and consider mobile nodes to determine the impact of our proposal on the performance of the handover mechanisms.  
Conceptualization, E.O.G., M.M.M. and C.M.G.A.; methodology, E.O.G. and M.M.M.; software, M.M.M.; validation, E.O.G.; formal analysis, E.O.G. and S.M.-S.; writing--original draft preparation, E.O.G. and M.M.M.; writing--review and editing, E.O.G., S.M.-S., H.C.-E., K.S. and C.M.G.A. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by ANID FONDECYT Regular 1241977 and by VLIRUOS project CU2023 ISN368A105: \"Smart water management to improve water balance control and environment preservation in hydrographic basins\".  
The data used in this work correspond to results from simulations carried out with CONTIKI-NG. The parameters used in the simulations and the software version are described in the work. Based on this information, the results of the work are reproducible without additional difficulty. The specific data for the preparation of each of the figures that show results of the simulations are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Representation of Knowledge  
In the field of visual 3D reconstruction, the representations of knowledge are diverse, such as 2D/3D/4D models, schedules, physical, and logical relationships, images and videos, point clouds, contours, patches, and so on. These representations of knowledge can be roughly categorized on three fronts:  
1. Direct as-planned information: 2D/3D/4D models are widely known as the as-planned information which depict the planned process and final states, and the core purpose of these as-planned models, during the 3D reconstruction process, is to serve as a reference standard. Schedules and weekly work plan representing project execute process are usually combined with 3D models to form 4D models. Physical relationships represent the spatial connection between geometric primitives (including aggregation, topological and directional relationships) [24], while logical relationships represent the sequential relationship among building components due to procedural or technical requirements, similar to the construction sequence under the constraint of the activity-on-arrow network. Both physical and logical relationships can be used to assist decision-making [25].
2. Direct as-build information: Image is one of the most common as-build information including photographs, videos, and depth images. With the recent advances in smart devices and camera-equipped platforms, an exponential growth in the volume of images and videos that are recorded on construction sites [12; 26]. Compared with ordinary photographs, depth images/RGB-D images generated by the range camera contain depth information, which makes them easy to generate as-build point clouds. Furthermore, laser scanned point cloud is also a common way to represent as-build models.
3. Derived information: Derived information comes from images or point clouds and provides support for the construction process reasoning. First, the point clouds derived from images or videos are a kind of derived information. Presently, taking real-time videos or time-lapse images and then aligning these sequential frames/images via feature detection, matching, and homography transformation to generate point clouds are common practices [12; 27]. In addition, if point clouds are projected onto the plane which runs parallel to the floor/wall, contours of buildings can be extracted through the algorithm from Suzuki [28; 29] to reason walls, doors, windows or other apertures [29]. Moreover, there is also a lot of useful information generated from images. For example, some researchers project 3D model elements onto image planes and the images are segmented into patches for the progress reasoning [30]. Furthermore, the image patches can be used for creating multiple discriminative material classification model and the Construction Material Library (CML) for the progress reasoning [31].
## 2 Materials and Methods
### Fatty Acid Methyl Ester (FAME) Analysis and Biodiesel Quality Assessment
#### 2.6.2 Biodiesel Quality Assessment Based on FAMEs  
The quality of biodiesel was assessed by determining various parameters based on the FAME profile. These parameters included the cetane number (CN), degree of unsaturation (DU), saponification value (SV), iodine value (IV), oxidative stability (OS), long-chain saturation factor (LCSF), cold filter plugging point (CFPP), kinematic viscosity (\(v\)), and density (\(\rho\)). Prior studies documented the use of an established equation for fuel quality assessment in microalgae to evaluate the biodiesel quality based on its fatty acid composition [36; 37].  
\[\text{SV}=\sum\frac{560\times\text{F}}{\text{MW}},\]  
\[\text{IV}=\sum\frac{254\times\text{F}\times\text{N}}{\text{MW}},\]  
\[\text{CN}=\sum\left(-7.8+0.302\times\text{MW}-20\times\text{N}\right)\times \text{mass fraction}\]  
\[\text{LCSF}=\left(0.1\ \times\ \text{C16}\right)+\left(0.5\ \times\ \text{C18}\right)+\left(1\ \times\ \text{C20}\right)+\left(1.5\ \times\ \text{C22}\right)+\left(2\ \times\ \text{C24}\right),\]  
\[\text{CFPP}=\left(3.1417\ \times\ \text{LCSF}\right)\ -\ \left(16.477\right),\]  
\[\text{DU}=\sum\text{MUFA}+\ \left(2\ \times\ \text{PUFA}\right),\]  
\[\text{ln}\ (v)=\sum-12.503+2.496\times\text{ln}(\text{MW})-0.178\times\text{N},\]  
\[\rho=\sum 0.8463+\frac{4.9}{\text{MW}}+0.0118\times\text{N},\text{ and}\]  
\[\text{OS}=\frac{117.9295}{\text{X}}+2.5905\ (0<100)\]In this Equation, N represents the percentage content of each fatty acid, MW is the molecular weight, D is the number of double bonds, and X represents the content of linoleic and linolenic acid at each FAME value.