esa-sceva/satcom-chunk-collection huggingface dataset:
# Robot Routing Problem of Last-Mile Delivery in Indoor Environments
## 5 Experimental Results
### Illustrative Example
The derived travel time matrix assuming the normal density scenario (\(\omega\) = _normal_) is presented in Table 5. Next, the IRRP was solved to obtain the delivery route for the two robots, and the obtained routes are listed in Table 6. The total travel time until all the demands were fulfilled by the two robots reached 4561.4 s.  
However, what if the travel time matrix is generated with a simple conventional travel time calculator? Will a difference occur in terms of the total travel time from the IRRP solution? To answer these questions, we generated the travel time matrix simply by dividing the moving distance by the average speed of the robot without considering any acceleration, deceleration, or cornering aspects of the robots. With this simple travel time matrix, the total travel time decreased to 3108 s, revealing a 1381.4 s difference (approximately 32% shorter than the proposed travel time calculator). Although the total travel time decreased with the simple travel matrix, the obtained routes to solve the IRRP can be viewed as naive and unrealistic. The difference in the total travel time should not be ignored when considering the battery power of the robot. Moreover, the naive routes resulting from the simple travel time matrix can cause delivery failure, which can lead to customer dissatisfaction because a much longer time is required for the robots to finish the delivery. Additionally, the accumulation of the differences in the total travel time can cause a serious problem if delivery services in  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Robot \#**} & \multicolumn{1}{c}{**\# of Trips**} & \multicolumn{1}{c}{**Route of Each Trip**} \\ \hline  & & 1 & 0-1-0 \\  & & 2 & 0-2-0 \\  & 1 & 3 & 0-3-8-0 \\  & & 4 & 0-6-0 \\  & & 5 & 0-7-4-0 \\ \hline  & & 1 & 0-5-0 \\
2 & & 2 & 0-9-10-0 \\  & & 3 & 0-12-11-0 \\ \hline  
multiple buildings are required within a given number of hours. For instance, delivery delays are more likely to occur in buildings assigned later in the delivery schedule.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Data Acquisition
#### 2.1.2 Remote Sensing Data: Sentinel-2 Spectral Bands  
Spectral reflectance data were acquired from the Sentinel-2 satellite constellation provided by the European Space Agency (ESA). In particular, surface reflectance values were extracted for the following nine spectral bands, each with a spatial resolution of 20 m, which are useful for monitoring the health of the vegetation:  
* \(B02\) (Blue, 490 nm);
* \(B03\) (Green, 560 nm);
* \(B04\) (Red, 665 nm);
* \(B05\) (Red Edge 1, 705 nm);
* \(B06\) (Red Edge 2, 740 nm);
* \(B07\) (Red Edge 3, 783 nm);
* \(B8A\) (Narrow Near-Infrared, 865 nm);
* \(B11\) (Short-Wave Infrared 1, 1610 nm);
* \(B12\) (Short-Wave Infrared 2, 2190 nm).  
For this study, Sentinel-2 Level-2A (L2A) imagery was utilized, which is already pre-processed for radiometric calibration, atmospheric correction, geometric correction, and terrain correction. Sentinel-2 provides a high temporal resolution of approximately five days, allowing frequent observations of the same location, depending on atmospheric conditions [49]. The selected sensing date, 18 June 2024, corresponded to the mid to late pod filling phase of the lupin crop, a critical phenological stage that follows flowering and precedes pod maturation [50]. During this period, the plant intensively translocates  
Figure 1: Geographical setting of the study area; red marker indicates the location of the experimental lupin fields, while their approximate perimeter is highlighted by the yellow outline.  
assimilates to the developing seeds, a process that strongly influences final yield [44]. Acquiring satellite imagery during this stage is particularly valuable, as spectral responses and VIs derived from the above bands can effectively capture variations in canopy biomass, chlorophyll content, and plant water status, thus, enhancing the predictive power of yield estimation models.
# Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
## 5 Conclusions  
An NMPC scheme is presented in this paper for the problem of path-following of autonomous vehicles with integrated collision avoidance based on ellipse geometry. A bicycle model is used with both acceleration and steering angle as control inputs. Employing a quadratic cost function that penalizes the deviation from the path, a dynamic optimization problem is formulated with linear inequality state and control constraints to achieve comfort driving. Collision avoidance is achieved by adding nonlinear time-varying state constraints based on the separation condition between elliptic disks. To guarantee staying within the road boundaries, implicit Bezier constraints are added to the OCP. The optimization problem is parametrized and solved using ACADO toolkit and is evaluated on MATLAB platform. The NMPC shows good performance for following of the predefined path while avoiding jerky behavior and collision with nearby vehicles.  
Our future aim is to increase the safety levels of the presented scheme by considering robust NMPC techniques for uncertain nonlinear models. The upper bound of the path-following error is parametrized with the aid of an auxilary control law, and then included into the optimization problem as a decision variable to make it as small as possible. Moreover, a maximum permissible error could be added as a reference to the optimization problem.  
Methodology, M.A.; Writing--original draft preparation, M.A. and S.S.; Writing--review and editing, M.A. and S.S.; Supervision, S.S.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Research Foundation (DFG) as a part of the Research Training Group Integrity and Collaboration in dynamic SENSor networks (i.c.sens) [GRK2159].  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Specimen Details and Materials  
The composite ear cups were designed with high resemblance to the ear cups of a commercial earmuff (PeltorTM OptimeTM I H510F, 3M, Maplewood, MN, USA), which was purchased off-the-shelf. The commercial (or reference) earmuff served as the reference to distinguish the acoustical benefits and drawbacks of the composite earmuffs. Apart from the ear cups, the remaining assembly components--headband, cushion pads, and inner foam lining--were kept identical between the reference and the composite earmuffs for consistency. It should be noted that the [PERSON]'s modulus of the reference ear cups--made from acrylonitrile butadiene styrene (ABS)--is proprietary. Hence, the [PERSON]'s modulus of the material was taken as a typical value (2.6 GPa) from a materials handbook [32].  
Compression moulding technique was used to manufacture the composite ear cups. First, fibre fabrics and PP films were placed between a two-piece aluminium mould according to a prescribed stacking sequence. The aluminium mould was designed to replicate the profile of the ear cups from the reference earmuff. The set-up was then placed into the Collin hot press for 15 min under a processing temperature and pressure of 190 \({}^{\circ}\)C and 20 bar, respectively. Thereafter, the set-up was cooled to room temperature at a rate of \(-10\)\({}^{\circ}\)C per minute. Finally, the excess material was removed by cutting to form the composite ear cups. Figure 1 shows the composite ear cups assembled with the inner foam lining and cushion from the reference earmuff.  
Coir-PP ear cups were made from nine layers of coir fibre fabric with a fibre volume fraction of 28%. One layer of PP film was added in between each layer of coir fibre fabric and at the outermost (exposed) layers. Coir/C-PP ear cups were made from six layers of coir fibre fabric, which were sandwiched by one layer of carbon fibre fabric. Similarly, PP films were added in between each layer of fibre fabric. In the hybrid model, the volume fractions of carbon fibre and coir fibre were 8% and 19%, respectively. The estimated Young's moduli of the composites were around 2.0-2.3 GPa. Figure 2 shows the schematic representation of the fibre stacking sequence for the respective composite ear cups. Table 1 shows the mass and the estimated cost of the respective ear cups.  
Figure 1: Closed-up view of the composite ear cups assembled with the inner foam lining and cushion: (**a**) coir fibre-reinforced polypropylene (Coir-PP); and (**b**) coir/carbon fibre-reinforced polypropylene (Coir/C-PP).  
Figure 2: Schematic representation of the fibre stacking sequence for (**a**) the coir fibre-reinforced polypropylene (Coir-PP) ear cups; and (**b**) the coir/carbon fibre-reinforced polypropylene (Coir/C-PP) ear cups.  
The raw materials were provided by different sources, which are as listed in Table 2. Due to manufacturing limitations, only three samples were fabricated for each type of composite ear cups as opposed to five samples as specified in ANSI S12.42 [33]. Nonetheless, such requirement is not specifically indicated in BS EN ISO 4869-3 [34].
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image Matching Scenario** & \begin{tabular}{c} **Total No.** \\ **of Points** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **[pt/m2]** \\ \end{tabular} & \
\begin{tabular}{c} **Avg.** \\ **Dist. [m]** \\ \end{tabular} &
\begin{tabular}{c} **Stand.** \\ **Dev. [m]** \\ \end{tabular} \\ \hline Metashape 2\(\times\) GSD—photogrammetric & 17,544 & 159.5 & \(-\)0.052 & 0.081 \\ Metashape 4\(\times\) GSD—photogrammetric & 4117 & 37.1 & \(-\)0.052 & 0.078 \\ Metashape 2\(\times\) GSD—consumer-grade & 15,978 & 142.7 & \(-\)0.288 & 0.097 \\ Metashape 4\(\times\) GSD—consumer-grade & 4043 & 35.8 & \(-\)0.253 & 0.129 \\ Inpho 1\(\times\) GSD—photogrammetric & 62,523 & 548.4 & \(-\)0.061 & 0.085 \\ Inpho 3\(\times\) GSD—photogrammetric & 10,001 & 87.0 & \(-\)0.063 & 0.079 \\ Inpho 1\(\times\) GSD—consumer-grade & 61,339 & 528.8 & \(-\)0.153 & 0.064 \\ Inpho 3\(\times\) GSD—consumer-grade & 9709 & 83.0 & 0.174 & 0.065 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistics for the cloud-to-cloud analysis of roof 3 (Figure 16). used profiles and point cloud deviations demonstrated that the photogrammetric scanner dataset provide primarily 50% better results than a consumer-grade dataset.
## 3 Fishing Gear Type Identification from Automatic Identification System Trajectory Data
### Preprocessing of Trajectories from AIS Data
Let \(\Delta lat\) and \(\Delta lon\) denote the latitude distance and the longitude distance between the interpolated neighboring positions pp \((t_{i})\) and pp \((t_{i+1})\), respectively. The angle between the movement direction of a ship and a latitude line can be calculated by Equation (2). The estimated course \(Co(t_{i})\) is computed by Equation (3). \[\theta=\tan^{-1}\!\left(\frac{\Delta lat}{\Delta lon}\right) \tag{2}\]  
\[Co(t_{i})=\left\{ \begin{array}{ll}90-\theta&(\Delta lon>0\;\;and\;\;\Delta lat >0)\\ 90+\theta&(\Delta lon>0\;\;and\;\;\Delta lat<0)\\ 270-\theta&(\Delta lon<0\;\;\mathrm{and}\;\Delta lat<0)\\ 270+\theta&(\Delta lon<0\;\;\mathrm{and}\;\Delta lat>0)\end{array} \right. \tag{3}\]  
Figure 2: Real positions and sampled positions on the interpolated path. The estimated speed \(Spd(t_{i})\) is computed by Equation (4) which divides the movement distance by the elapsed time. \[Spd(t_{i})=\frac{\sqrt{\left(\Delta lon\right)^{2}+\left(\Delta lat\right)^{2}}}{ t_{i+1}-t_{i}} \tag{4}\]  
[PERSON] [14] proposed a rule-based model for classifying the fishing activities of long liner ships according to their speed: hauling fishing activity for 2 to 4 knots speed, setting fishing activity for 4 to 6 knots speed, and non-fishing activities for over 6 knots speed. [PERSON] et al. [24] treated the trawl ships of 2 to 4 knots speed as being in fishing activity. From these observations, the proposed method treats fishing ships with speed of more than 6 knots as being in non-fishing activity. Figure 3 shows the ship speed distributions for fishing gear types over morning time, afternoon time, and night time, which are obtained from the data set used in the experiments. In Figure 3, all fishing gear types except traps (e) are in fishing activities at the speed of under 6 knots. Trap ship cast nets at the speed of 6 to 9 knots and haul them at the speed of under 6 knots.
## 3 Mobility Methodology
### KITE of NDN with C-IoT and AI  
KITE technology with cloud operation uses a server called the rendezvous server (RS), which is located between the consumer and the mobility producer that uses IoT devices. The RS can always be traced by an NDN producer known as KITE. As shown in Figure 5, KITE operates between transmission I_packets from the consumer via accessing the producer by storing the location through the RS. A single route prefix is assumed for simplicity. KITE swaps the public keys to create confidence. It is signed by the mobile producer and has a special trace tag in the name, containing AI technology. The RS acknowledges the data interest for the location of the producer and returns signed tracing data to the mobile producer via the same path as the TI. Intermediary forwarders establish or update FIB entries for the mobile producer data prefix. When a mobile producer moves to other locations, it stores all of the prefixes via cloud storage for any interest packet update. Table 2 explains the operation of KITE technology in cloud storage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flow of Transmission** & **Process Status** & **Description** \\ \hline Process 1 & Before handoff & Typically, a consumer sends an I_packet over an NDN router to the network to request data. The NDN router then determines if the material is available; if not, then it sends an I_packet to the NDN network. \\ \hline  & The I_packet’s prefix data name traverses NDN routers to reach the location of the producer. If the data are cached by any router in the network, then the router reacts promptly with the cached data. If not, then the routers, along with the path, store the interest information as entries in the PIT and FIB tables, then forward it until it reaches the producer. The producer then provides the data in
## 2 Related Work
### The Data Fusion Technique  
This section presents related works on the data fusion technique and describes that it can be used to eliminate the data redundancy, thus reducing the energy consumption during data transmissions. [PERSON] et al. presented a data fusion method based on BPNNs, and they put the input layer of the BPNNs in CMNs, and put the hidden and output layers in CHNs [20]. Only the fused data representing the features of the input data are sent to the SN in order to improve energy efficiency. [PERSON] et al. developed a clustering protocol in the light of data fusion scheme by using BPNNs for TWSNs, which adopts a stable election protocol model based on the LEACH protocol to select appropriate CHNs [38]. The selected CHNs fuse the data after receiving them and send the fused data to a destination node. [PERSON] et al. proposed a data fusion scheme by employing an improved radial basis function neural network in mobile TWSNs, which improves the data fusing model so as to reduce the energy consumption [39]. Nevertheless, the underwater environment has not been taken into account in [20; 38; 39]. [PERSON] et al. introduced a fuzzy-based clustering routing protocol combined with the data fusion technique for UWSNs, where the residual energy, the distance, the node density, the load, and the link quality are considered as inputs to the fuzzy logic as a way to select CHNs and determine the cluster size [40]. The selected CHNs fuse the received data and transmit them to a destination node, reducing the energy dissipation and enhancing the network lifespan. The clustering routing protocol with a two-tier data fusion for UWSNs is described in [41], where CMNs reduce the data redundancy before transmitting the data to CHNs. The CHNs adopt a developed K-means method based on an ANOVA model to aggregate the received data, and send the aggregated data to the SN, thereby minimizing the energy consumption. [PERSON] et al. introduced a data fusion technique based on the BPNN, which combines with clustering routing protocols [42]. The scheme optimizes the selection of CHNs, and the selected CHNs extract features from the data and send them to the SN, which can save energy. [PERSON] et al. described a data fusion method on the basis of the rough set theory and the BPNN [43], where the rough set theory is used to reduce redundant data and the reduced useful data are used to train the BPNN. It has been validated that this protocol can enhance the performance of the data fusion system and improve the training speed of the BPNN. [PERSON] et al. introduced a data collection and fusion mechanism that uses a mobile SN to collect the data from collection points [44]. The collection points are selected periodically and the collection points are the places where the data fusion is performed, which is capable of reducing the energy consumption and extending the network lifetime.
# Inertial-Navigation-Aided Single-Satellite Highly Dynamic Positioning Algorithm
## 4 Simulation and Result Analysis
### Comprehensive Measured Environmental-Error Analysis  
In the actual single-satellite positioning system, all types of errors were included. Flight duration was 150 s, satellite-orbit error was less than 10 m, pseudorange error was less than 10 m, satellite-speed error was 0.1 m/s, and height error was 1 m in a typical single-satellite environment. The INS adopted the same simulation environment as described above. Target motion speed was 20 m/s and direction was mainly in the \(y\) axis. Total simulation time was 2700 s. Due to the solution, only 2700 s of results were output. The results are shown in Figures 8 and 9.  
Figure 6: Our algorithm under single-satellite velocity error of \(x,y,z\) axis.  
Figure 7: Performance comparison of different algorithms under single-satellite velocity error.  
Our proposed single-satellite positioning algorithm based on UKF operated smoothly with no evident divergence under the comprehensive environment. Mean value was less than 50 m; positioning convergence was rapid and could be achieved within 10 s of starting output, error was less than 0.1 km and positioning error was less than 100 m. Figure 9 shows the comparison of absolute position error (reported as RMS) under the comprehensive environment for the three single-satellite positioning algorithms. It shows that the mean error between our proposed algorithm and the UKF algorithm was much smaller than that of the least-squares Doppler single-satellite algorithm. The mean error of our proposed algorithm was only 3.9% of the least-squares algorithm and the EKF algorithm was 8.6% of the least-squares algorithm.
###### Abstract  
Germanate tellurite glasses doped with Eu\({}^{3+}\) and Tb\({}^{3+}\) were synthesized by the conventional melt-quenching method. There is no indication of the energy transfer between dopant ions in this host, but the co-dopants exhibit excellent color purity of 100% for Eu\({}^{3+}\) and 80% for Tb\({}^{3+}\). The co-doped glass exhibits yellow luminescence. The quantum yield of the Eu\({}^{3+}\) emission is equal to 23% under 395 nm excitation. The thermal quenching of Eu\({}^{3+}\) and Tb\({}^{3+}\) luminescence occurs at different temperature ranges, which enables the thermal sensing properties of the material. The relative fluorescence intensity ratio (FIR) sensitivity of 0.16% K\({}^{-1}\) was recorded in the wide range of temperatures spanning from \(-193\)\({}^{\circ}\)C up to 0 \({}^{\circ}\)C. The temperature dependence of the decay times was also studied. The lifetime-based temperature sensitivity was determined to be 0.95% K\({}^{-1}\) at 250 \({}^{\circ}\)C for Tb\({}^{3+}\)\({}^{5}\)D\({}_{3}\) level emission and 0.3% K\({}^{-1}\) at 225 \({}^{\circ}\)C for Eu\({}^{3+}\)\({}^{5}\)D\({}_{1}\) level emission.  
glass; phosphor; luminescence; thermometry +
Footnote †: journal: Applied Sciences
## 2 Data and Methods
### GRACE Data  
Time-variable gravity field models developed using GRACE observations enable us to determine the mass loading, including the variations of ocean, atmosphere, land water [39]. The crustal VD can be modeled using GRACE solutions in the form of spherical harmonic coefficients (SHCs) as follows [40]:  
\[\Delta t=R\sum_{l=1}^{N_{\rm max}}\sum_{m=0}^{l}\overline{P}_{l,m}(\cos\theta) \cdot(\Delta C_{lm}\cos(\mathrm{m}\lambda)+\Delta S_{lm}\sin(\mathrm{m} \lambda))\frac{h_{1}^{\prime}}{1+k_{l}^{\prime}} \tag{1}\]  
where \(R\) represents the mean reference radius of the Earth; \(h_{1}^{\prime}\) and \(k_{1}^{\prime}\) are the load Love numbers, \(\theta\) and \(\lambda\) are co-latitude and longitude, respectively, \(\overline{P}_{l,m}\cos(\theta)\) is the fully normalized Legendre function for degree \(l\) and order \(m\), and \(\Delta C_{lm}\) and \(\Delta S_{lm}\) are spherical harmonic coefficients which are relative to mean SHCs computed from the period of 2002-2015 (2002/03-2015/03).  
We adopted the unfiltered GRACE RL06 SHCs monthly gravity field models (up to 60 degrees and orders) provided by the Center for Space Research (CSR) at the University of Texas at Austin [41]. Due to the simple satellite formation type of GRACE and the high-frequency noise in the monthly solutions, the GRACE Stokes coefficients are dominated by larger uncertainties at high degrees. In order to reduce the effects of those errors on the deformation estimates, a de-correlation filter (P\({}_{3}\)M\({}_{6}\) method) and Gaussian averaging kernel with a radius of 400 km at degree and order directions were applied together to restrain the north-south stripe noise and obtain the GRACE surface mass loading series [42; 43]. The C20 SHCs with poor stability in GRACE solutions were replaced with the values computed from satellite laser ranging (SLR) data. Meanwhile, the monthly estimates of degree-1 (geocenter) gravity coefficients from GRACE RL06 solutions were added into the GRACE GSM solutions for the reason that the satellite gravimetry is insensitive to the geocenter motion [44].  
Considering the effect of the non-tidal atmospheric and oceanic mass field remaining in the GNSS data, the high frequency non-tidal signals (Atmosphere and Ocean De-aliasing Level-1B (AOD-1B) products) were added back to the filtered level-2 GRACE-estimated gravity fields [45]. Before compared with the GNSS time series, the secular trends in GRACE-derived deformations were also removed with a linear model.