esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Classification of WBAN Routing Protocol
#### 2.1.6 Cross Layered Routing  
The cross-layer routing protocol is mainly used for the development of wireless sensors networks. The fundamental responsibility of the cross-layer approach is to provide synchronization among  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Methods** & **Objective** & **Limitations** & **Domain** \\ \hline \multirow{3}{*}{TARA [45]} & Compute the temperature-rise of nodes and redirect the packet from hotspot area. & Failed to provide hotspot avoidance. High node temperature with less network life cycle. & Cancer and retinal detection \\ \hline \multirow{3}{*}{LTRT [43]} & Temperature-rise is less. Packet rate is high and throughput. & Network life cycle is less with no hotspot avoidance. & Monitoring system for cardiac patients \\ \hline \multirow{3}{*}{RAIN [42]} & Efficiently route selection toward sink in an id-less BSN. Diminish the problem of hotspot. & Delay in packet delivery. & In-vivo network of implanted sensors nodes \\ \hline \multirow{3}{*}{M-ATTEMPT [38]} & To route the packet away from hotspot area. & Failed to select new route in case of hotspot. Load distribution on node is not uniform. & Homogeneous and Heterogeneous WBAN \\ \hline \multirow{3}{*}{Re-ATTEMPT [38]} & To route the packet away from hotspot area and increase network life. & The network life cycle is less with a heterogeneous WBAN \\ \hline \multirow{3}{*}{HPR [40]} & Diminish the problem of hotspot in network. & The network life cycle is less with a heterogeneous WSN’s \\ \hline \multirow{3}{*}{THSR [41]} & To minimize the temperature of the node and avoid hotspot creation. & Network life cycle is less with a heterogeneous WSN’s \\ \hline \multirow{3}{*}{LTR [44]} & To diminish the temperature of the node in a network and route the packet away from the hotspot & Packet throughput is less. High node temperature. Network life time is less & Monitoring system for cardiac patients \\ \hline \multirow{3}{*}{M2E2 [37]} & To route the packet away from hotspot area and increase the network life in wireless body & High node temperature. & Heterogeneous WSN’s \\ \cline{1-1}  & & sensor networks. & \\ \hline \multirow{3}{*}{ALTR [44]} & To minimize the temperature of the node in a network & High end-to-end delay with less network life. & Monitoring system for cardiac patients \\ \cline{1-1}  & In a network & Packet drop throughput is less. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: State-of-the-art analysis of temperature-aware routing protocols.  
the layer without affecting the MAC layer functionality. Moreover, cross-layer routing protocols significantly enhance the network performance and resolve issues related to network and MAC layer. Cross-layer routing protocols are divided into four categories, i.e., Biocomm and Biocomm-D [49], TICOSS [50], CICADA [51], and WASP [52].
## 2 Emerging Applications of 6G
### Smart Homes  
Another well-known IoT-based system we use on daily-basis is smart home systems. IoT technology facilitates day-to-day household appliances to be connected to the Internet, thereby controlling them remotely and automatically. Examples of smart home systems range from turning on a light to managing the entire security of a premises. A well designed smart home system has the ability to reduce power consumption, and thereby the overall operational cost of the house, by turning off unused lights and appliances, manipulating the temperature levels accordingly, and adjusting the intensity of lights. To facilitate such needs, widely available and device-compatible communication technologies are required. Moreover, IoT can also add enhanced functionalities, such as implementing cameras and sensors to monitor and detect intruders [25]. A smart home can also consist of an indoor greenhouse, where humidity, temperature, watering levels, and fertilizing can be managed and automated with IoT. Furthermore, plant vitals, monitoring, and growth predictions can also be conducted with IoT systems. To enable these smart home applications, low latency and more reliable communication standards that ensure prompt alerting and accurate notifications are required.  
A few other indoor applications that benefit from IoT-based smart home systems are remote education, indoor navigation systems, assisted technologies for people with disabilities, and financial systems [26]. However, most existing IoT systems still operate using legacy technologies such as 3G and 4G. Besides being mature and predictable technologies, they do not have the ability to address the resource requirements of emerging IoT technologies. For this purpose, new technologies and standards need to be explored to support emerging smart home applications [9; 27].
# Quadcopters in Smart Agriculture: Applications and Modelling
## 3 Agricultural Applications of Quadcopters
### Planting and Seeding  
Quadcopters also play a significant role in planting, surpassing traditional agricultural methods. This modern utilization offers precision and efficiency in seed dispersal, allowing for the optimal use of resources.  
A quadcopter was built in [101] with an appropriate mechanism to sow seeds in forests and roadside areas. A quadcopter was designed in [100] to disperse seeds for the specific tree species \"Tamarindus Indica\" in revegetation fields in West Java. The authors in [104] demonstrated how the use of a quadcopter in farm seeding can assist land machinery in sowing procedures or even surpass them. The study presented a quadcopter equipped with a pneumatic system to dispense seeds at specific planting depths, making it suitable for low-cost farming applications. A quadcopter was built in [97] to plant seeds in inaccessible agriculture areas for better cultivation.  
The authors in [112] used a GMH-3 quadcopter to optimize a rice-seeding spreader for better uniformity in distribution.
## 5 Measurements and Evaluation
### Degree of Depth of Focus (DoF)  
As is always mentioned, the holographic display is most distinguished from other conventional 3D displays in the sense that it has the capability of supporting accommodation of human visual system. This means that holographic display can render an image at a specified depth plane, which can be focused as real objects. In previous studies, this has been simply demonstrated by showing two or more objects rendered at different distance, one of which is focused the others become defocused and blurred when captured by a shallow DoF camera. We can evaluate this capability of a display by using a very similar method to measuring 3D-MTF. For an MTF test pattern imaged at a certain depth plane, measuring MTF around the plane in viewing direction can tell how well the image is focused to the plane and defocused elsewhere. Figure 13a-c shows modulation depth values measured for a test pattern imaged at distances of 57 mm, 158 mm, and 213 mm from the virtual SLM plane, respectively. There are discrepancies between the measured image depth and the intended depth rendered in the CGH algorithm, which are 40 mm, 120 mm, and 160 mm. This is believed to be caused from the  
Figure 12: Color gamut and color fidelity of implemented system, (**a**) color gamut, (**b**) 24 color Gretag-Macbeth ColorChecker, (**c**) hologram image, and (**d**) measured color fidelity.  
slight difference between the dimensions of designed and actual optical signal path of the system. It represents varying depth representation capability of the display through the image rendering volume. These modulation depth curves are obtained using the exact same measurement method as is used for obtaining MTF curves. Since for a real hologram image the spherically converging wavefront is supposed to focus at the image plane, defocusing or blur in front or behind this focal plane should be symmetric. So, deviations more than measurement error from this expected symmetry may come from aberrations of the optics of the system.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 3 System Expected Capabilities
### Number of Expected Observable Debris Objects  
ATLAS was designed to track space objects in LEO orbits below 1000 km of altitude, however, several objects in this range might not be observable due to several constraints:  
* Objects with an RCS below the minimum detectable threshold;
* Orbits with an elevation range below the minimum elevation required for detection and data acquisition;
* Orbital speeds exceeding the maximum antenna tracking speed;  
In order to make an estimate on the number of objects trackable by ATLAS, we retrieved the latest two-line element (TLE) files (which contain the orbital elements) from all the objects categorized as debris with an apogee less than 1000 km from the Space-Track public catalogue [36]. With the orbital elements, we predicted the orbit of each object for the next seven days (counting from the epoch of the TLE file) with an SGP4 based algorithm [37]. Then, we intercepted the predicted orbits with the visible portion of the sky for ATLAS, which is obtained by the information provided in Section 3.2. Objects that do not intercept the visible sky are discarded.  
Since the catalogue does not provide detailed information about the objects RCS, it is not possible to filter out by RCS. All the objects were considered regardless of RCS and the filtering was done by minimum elevation and maximum velocity. Figure 3 depicts the number of expected observable objects during a complete day.
## 4 Conclusions  
In conclusion, hierarchically porous 3D rGOAs could be produced using a simple one-step hydrothermal technique with VC as a reductant that assists the synthesis process. The prepared rGOA-1 exhibited excellent properties, having the highest surface area (137.9 m\({}^{2}\) g\({}^{-1}\)) and a superior water contact angle (143.8\({}^{\circ}\)). Furthermore, rGOA-1 also showed the highest theoretical adsorption capacity for hexamethyldisiloxane (104.9 mg g\({}^{-1}\)) at 0 \({}^{\circ}\)C. Moreover, low inlet concentration and bed temperature yielded better L2 adsorption by rGOA-1. In addition, used rGOA-1 was less sensitive to the water presence, and it exhibited good regeneration ability for multiple uses upon annealing at 80 \({}^{\circ}\)C. Therefore, the superior performance of rGOAs suggested its great industrial application potential.  
Figure 11: Adsorption breakthrough curves for dry L2 of rGOA-1 in the repeated cycles.  
Figure 10: Influence of the presence of water on the adsorption of L2.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11188486/s1](https://www.mdpi.com/article/10.3390/app11188486/s1)]([https://www.mdpi.com/article/10.3390/app11188486/s1](https://www.mdpi.com/article/10.3390/app11188486/s1)), Figure S1. Molecular structure of siloxanes. Figure S2: Photographs of of the rGOAs (a: rGOA-0, b: GOA-0.5, c: rGOA-1, d: rGOA-2). Figure S3: Image showing a water droplet on the surface of IGGO and rGOAs film., Figure S4: Relationship between \(Q_{\text{B,th}}\) and \(S_{\text{BET}}\) (a), \(Q_{\text{B,th}}\) and \(V_{\text{tot}}\) (b), \(Q_{\text{B,th}}\) and contact angle (c) for the five adsorbents. Table S1: Influence of the presence of water on different adsorbents for siloxanes. Table S2: Adsorption and regeneration capacities of different carbon materials for siloxanes.  
**Author Contributions:** Conceptualization, Z.M. and X.M.; methodology, Z.M. and X.M.; formal analysis: [PERSON]; investigation: [PERSON], X.H. and Z.H.; resources, Z.M.; data curation: Y.Z., X.H. and Z.H.; writing--original draft preparation: [PERSON], and X.H.; writing--review and editing, Z.M. and X.M.; supervision, Z.M. and X.M.; project administration: Z.M.; funding acquisition, Z.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Hebei Province, grant number B2021205022.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article or Supplementary Materials. The data presented in this study are available in Supplementary Materials.  
**Acknowledgments:** The authors would like to express their gratitude to the Natural Science Foundation of Hebei Province for the financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 3 Materials and Methods  
Direct spread spectrum technology is a wideband modulation in which the original bit sequence is converted into a pseudo-random spreading sequence [18]. As a result, we obtained the transmission of a significantly larger number of extended (direct) sequence bits in the same time interval. Consequently, the frequency range for transmitting signals increases in proportion to the length of the spreading sequence [19].
## 3 Results and Discussion
### Chemistry
The \({}^{13}\)C NMR spectrum displayed 34 signals, which were assigned to one methoxy, eight methyls, eight methylenes, three methines, and fourteen quaternary carbons, including six \(sp^{2}\) and three carboxylic carbons. Extensive study of the \({}^{2,3}\)J\({}_{\rm C-H}\) correlations (HMDEC) allowed us to establish the structure of A and B rings and build the nor-triterpene skeleton for **7**. The regiosubstitution of the acetate groups was deduced by a ROESY experiment, showing ROE effects between H-1/OAc-2 and Me-23/OAc-3. This data established the structure of **7** as 2,3-diacetoxy-pristimerol. Compounds **8** and **9**, both with the molecular formula C\({}_{34}\)H\({}_{48}\)O\({}_{6}\) (EI-HRMS), differ from that of **7** by the presence of two additional hydrogen atoms. The most remarkable difference in their NMR spectra, when compared to that of compound **7**, was the absence of the double bond C-7-C-8. Thus, in the \({}^{13}\)C NMR spectra of **8** and **9**, the signals assigned to C-7 and C-8 resonated in the region of aliphatic carbons (\(\delta_{\rm C}\) 18.2 and 43.5 in **8**, and \(\delta_{\rm C}\) 30.5 and 56.0 in **9**). Moreover, differences between **8** and **9** were also observed for the chemical shifts of Me-25 (\(\delta_{\rm H}\) 1.60, \(\delta_{\rm C}\) 27.3 in **8**, and \(\delta_{\rm H}\) 1.46, \(\delta_{\rm C}\) 36.8 in **9**) and  
Figure 3: Selected HMDEC (single arrow) and ROE (doubled arrow) correlations for compound **6**. Me-26 (\(\delta_{\rm H}\) 0.88, \(\delta_{\rm C}\) 15.9 in **8**, and \(\delta_{\rm H}\) 1.21, \(\delta_{\rm C}\) 25.8 in **9**). Analysis of 2D COSY, HSQC, and HMBC spectra allowed us to define their core structures. The ROE effects of H-8/Me-25 and Me-26 observed in a ROESY experiment for compound **8**, and those of H-8/Me-27 observed for compound **9**, further supported the stereochemical assignment for epimers **8** and **9**. Their structures were also confirmed by chemical correlations; hydrolysis of **8** and **9** yielded compounds whose spectroscopic data were identical to those previously reported for 6-deoxydepharodol (**25**) and 8-_epi_-6-deoxydepharodol (**26**) [21], respectively. Consequently, the structure of compounds **8** and **9** were deduced to be 2,3-diacetyl-6-deoxydepharodol and 2,3-diacetyl-8-_epi_-6-deoxydepharodol, respectively.
## 2 Materials and Methods
### Processing Methods  
Figure 7 illustrates the methodological approach adopted in this study. Firstly, an assessment of the processing methods useful to discriminate between submerged and emerged areas was made, and the NDWI was chosen as the most appropriate. Secondly, NDWI images were generated in Google Earth Engine, and then imported in QGIS for the pixel analysis. This latter stage was carried out using Saga's _Add Raster Values to Points_ algorithm on the archaeological sites point shapefile. The analysis of the extent of the emerging sites was carried out in QGIS as well, and it required a reclassification of the NDWI images beforehand, paired with the use of the _Zonal Histogram_ algorithm on the polygon shapefiles. The results obtained were then checked with Planet satellite images for the year 2018, and with Sentinel-2 or Landsat images for any previous year not covered by the assigned Planet quota. To provide reproducibility, two QGIS models and one R script (.rsx) to use in QGIS were created; moreover, the analysis was also reproduced in R, and both the R code and the QGIS models and code are linked in the Supplementary Materials.
# High-Durability Concrete Using Eco-Friendly Slag-Pozzolanic Cements and Recycled Aggregate
## 3 Concrete Properties Using Slag-Pozzolanic Cement
### Mechanical Properties of Concrete Using Slag-Pozzolanic Cement Types  
With regard to hardened concrete, compressive strength, bending tensile strength, and modulus of elasticity were evaluated for a basic characterization of the different cement types.  
Compressive strength was tested on 150 mm concrete cubes after 2, 28, 90, and 365 days of water storage at 20 \(\pm\) 1 \({}^{\circ}\)C to present the varying strength development of the different cement or rather concrete types (Figure 8). As expected, the slag-pozzolanic cement types start at a low early strength level. CEM V/A cement types reach the strength level of the reference cement CEM I after 28 days and show a strength increase even between 90 and 365 days of concrete age. CEM V/B cements lag significantly behind, reaching a significantly lower strength level; strength development runs nearly flat after the age of 90 days.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Cement Type**} & **Fresh Concrete Density** & **Flow Spread Consistency [cm] and** & \multirow{2}{*}{**Air Content [Vol.\%]**} \\  & **[kg/m\({}^{3}\)]** & **(Consistency Class) According to [58]** & \\ \hline CEM 132.5 R & 2283 & 50 (F4) & 3.6 \\ (1) CEM V/A (S-V) 32.5 R & 2322 & 40 (F2) & 2.0 \\ (2) CEM V/B (S-V) 32.5 N & 2306 & 45 (F3) & 2.3 \\ (3) CEM V/A (S-V) 32.5 R & 2303 & 40 (F2) & 2.6 \\ (4) CEM V/B (S-P) 32.5 N & 2190 & 50 (F4) & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fresh concrete properties by using different slag-pozzolanic cement types.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compound** & **Amount [kg/m\({}^{3}\)]** \\ \hline Cement & 360 \\ Water & 176 \\ Aggregate 0/4 mm & 710 \\ Aggregate 4/8 mm & 420 \\ Aggregate 8/16 mm & 695 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Concrete standard mixture for comparison of produced cement types.  
The modulus of elasticity was determined as the secant under compression (the test specimen is loaded under axial compression, stresses and strains are recorded, and the slope of the secant to the stress-strain curve is determined) according to [58]. Test specimens (concrete prisms with dimensions of \(150\times 150\times 600\) mm) were water-stored at \(20\pm 1\)\({}^{\circ}\)C (the age-dependent pathway is displayed in Figure 9). The modulus of elasticity for the reference cement CEM I starts at approx. 30 GPa at the highest level and shows a stronger increase with time than the concrete types made with slag-pozzolanic cement. All CEM V cement types reach a similar level of approx. 36 GPa at a curing time of 365 days under water (see [49]).
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Sidelobes  
The main lobe may not serve drones properly since the antennas are not omnidirectionally vertical, as illustrated in Figure 7. Although research efforts have been made, the issue remains unresolved. It has been shown in [87] that the effect of sidelobes is mitigated at high altitudes when drones are exposed to free-space propagation conditions. Sidelobes are part of the far field pattern of a directional antenna, transmitting undesirable radiation in directions other than the main one. Since sidelobes have a lower field intensity than the main lobe, terrestrial users connect to the main lobe by tilting their antennas downwards. Drones may be prone to unwanted sidelobes since they fly at high altitudes. The sky may not be fully covered by the sidelobes of BSs, resulting in no sky coverage and subsequent link failure. This also contributes to further increasing the handover probability.  
Figure 6: Interference level with connected drones.
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 5 Conclusions  
In this study, attention is drawn to the importance of smart and mechanical systems for the efficient use of natural resources such as water and nutrients, which are vital in human life. At the same time, it is not enough to just suggest smart systems or methods, it is also important to develop sustainable approaches. Therefore, we focused on an application that can meet many needs in agriculture, from planting to harvesting. Moreover, the health of the farmers can be protected and their heavy workload on the agricultural lands will be reduced. In addition, accident rates will automatically decrease. Therefore, the occupational health and safety rate can be increased. In addition, crop abundance can be experienced due to the mechanized and sustainable nature of the proposed model, and therefore possible forced migration events will also decrease.  
The proposed mechanism consisted of three phases. In the first phase, an SCSO-based approach was presented to assign the cultivation task to the relevant autonomous robots, eliminating the human resource factor. It should be noted that in this study, we did not focus on the robotics field, but only on the development of an algorithmic-based model and the architecture of the simulation environment. In the second phase, we used IoT devices to instantly check the status of the crops planted by robots. The result from this phase was taken as the input for the third phase (harvesting). In the third stage, a new method was proposed, inspired by the working mechanism of the ACO algorithm. This method tries to get the maximum profit from the crops to be collected. In addition, it attempts to generate paths for the harvesting robots in a way that would consume the least power and time. According to the results, our proposed algorithms performed better than other methods. In this study, the tomato field was considered as an example, but the suggested mechanism could be used for different purposes in many agricultural lands.  
Conceptualization, F.K., A.S., S.N. and F.A.A.; methodology, F.K., A.S., S.N. and I.Y.; software, A.S. and I.Y.; validation, A.S., M.Z., G.R., S.L., A.M. and I.Y.; formal analysis, A.S. and I.Y.; investigation, A.S., F.A.A., F.E., I.Y., S.N. and F.K.; resources, F.K., F.E., I.Y., A.M. and M.Z.; data curation, M.Z. and I.Y.; writing--original draft preparation, F.K., F.E., S.N., F.A.A., G.R., S.L. and A.M.; writing--review and editing, F.K., F.A.A., I.Y., G.R., S.L. and A.M.; visualization, A.S., F.E., G.R., S.L. and A.M.; supervision, F.K., A.M. and I.Y.; project administration, F.K. and I.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Available on request.  
The authors declare no conflict of interest
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 5 Use Cases and Applications
### Logistics and Supply Chains  
Symbiotic technology enhances communication and operational efficiency in logistics and supply chain management. This frequently entails the incorporation of RFID tags into products, enabling efficient tracking and streamlined management for improved outcomes.  
This kind of solution could be valuable for RT asset location and tracking and inventory management within warehouses, utilizing existing WiFi APs to identify and oversee inventory for indoor applications or outdoor real-time shipment tracking using the cellular network [91].