esa-sceva/satcom-chunk-collection huggingface dataset:
# GeoBERT: Pre-Training Geospatial Representation Learning on Point-of-Interest
## 3 Materials and Methods
### Basic Geographic Unit  
In previous studies, researchers took traffic analysis zone [3] (TAZ), buffer area [5],or other urban functional zone (UFZ) [42] as the basic unit to build a training corpus of POIs. Although this may have more specific practical physical meaning, it is more time-consuming and less effective in the case of the large-scale corpus. Therefore, we use the level-7 Geohash grid as the base unit of POIs, which is easy to retrieve. Moreover, we use a finer-grained grid division, so it can be used for a wider and more refined range of downstream tasks. Geohash ([[https://en.wikipedia.org/wiki/Geohash](https://en.wikipedia.org/wiki/Geohash)]([https://en.wikipedia.org/wiki/Geohash](https://en.wikipedia.org/wiki/Geohash)), accessed on 30 November 2022) is a public domain geocode system, which encodes a geographic location into a short string of letters and digits. Geohash guarantees that the longer a shared prefix between two geohashes is, the spatially closer together they are. The reverse of this is not guaranteed, as the two points can be very close but have a short or unshared prefix. The area of each level of Geohash is illustrated in Table 2. The max scale is calculated around the equator. In different latitudes, the area will be slightly different. Each level-7 Geohash grid is 153 m \(\times\) 153 m near the equator. An example of level-7 Geohash grids in Shanghai is shown in Figure 2. In the training corpus, 1,582,870 grids are covered.  
\begin{table}
\begin{tabular}{c c c} \hline
**POI Type** & **Number** & **Proportions** \\ \hline accommodation & 930,307 & 5.62\% \\ enterprise and business & 2,513,793 & 15.18\% \\ restaurant & 2,498,107 & 15.09\% \\ shopping & 3,603,615 & 21.76\% \\ transportation & 1,385,916 & 8.37\% \\ life services & 2,515,165 & 15.19\% \\ sport and leisure & 865,208 & 5.23\% \\ science and education & 725,916 & 4.38\% \\ health and medical & 683,560 & 4.13\% \\ government & 664,317 & 4.01\% \\ public facilities & 171,141 & 1.03\% \\ \hline total & 16,557,045 & 100.00\% \\ \hline \end{tabular}
\end{table}
Table 1: Quantitative proportion of each first-level POI type in the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Geohash Length (Level)** & **Cell Length** & **Cell Width** \\ \hline
1 & \(\leq\)5000 km & \(\leq\)5000 km \\
2 & \(\leq\)1250 km & \(\leq\)625 km \\
3 & \(\leq\)156 km & \(\leq\)156 km \\
4 & \(\leq\)39.1 km & \(\leq\)19.5 km \\
5 & \(\leq\)4.89 km & \(\leq\)4.89 km \\
6 & \(\leq\)1.22 km & \(\leq\)0.61 km \\
7 & \(\leq\)153 m & \(\leq\)153 m \\
8 & \(\leq\)19.1 m & \(\leq\)19.1 m \\ \hline \end{tabular}
\end{table}
Table 2: Area of each level Geohash cell.
## 5 ML Intersection with 6G Enabling Technologies
### ML and IRS Convergence for 6G Wireless Networks
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Application** & **ML Model** & **Outcome** \\ \hline
[169] & Phase reconfiguration & DNN & Achieved a reduction in the pilot overhead by 35\% \\ \hline
[170] & Phase reconfiguration & CNN & The results showed that the model yielded near-optimal data rates while utilizing less than 2\% of the total number of Rx locations. \\ \hline
[174] & Channel estimation & CNN & The model improved the overall performance and yielded better NMSE \\  & & & than the LS. \\ \hline
[172] & Phase reconfiguration & DNN & Increased the achievable throughput in all the desired points in the 3D space with lower estimated errors. \\ \hline
[173] & Reflection beamforming & DNN & Achieved near-optimal data rates with negligible training overhead. \\ \hline
[175] & Transmit beamforming & DRL & Obtained the optimal transmit beamforming matrix and phase shifts. \\ \hline
[176] & Transmit beamforming & DRL & Obtained the optimal transmit beamforming matrix and phase shifts. \\ \hline
[178] & Improved spectral efficiency & DL & Could configure real-time phase shift while improving rate performance in low SNRs and providing higher EE. \\ \hline
[179] & Phase shift optimization & TD3 & Reduced the transmit power with lower computation delay. \\ \hline
[170] & Secure beamforming & PDS-PER & Enhanced the secrecy rate and the satisfied QoS probability. \\ \hline
[180] & Phase shift and reflection amplitude optimization & MA-DRL & Significantly improved the secrecy rate and throughput. \\ \hline
[181] & Channel estimation & MLP & Enhanced the SNR and overall performance of the network. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of ML and IRS integration for optimal 6G wireless network performance.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Results and Discussion
#### 4.2.1 IoD Path Planning  
The IoD is composed of multiple drones, each facing unique environmental constraints. As a result, evaluating the entire formation proves more beneficial than planning paths for individual drones. This study assesses the IoD paths using optimization algorithms. Figure 4 presents a visualization of the 2D UAV path-planning results. It illustrates the paths generated by the optimization algorithms, plotted on a 2D map to depict the planned trajectories for the drones within the formation.  
The outcomes of Figure 4 illustrate that all algorithms effectively generate feasible paths for the IoD formation. Moreover, the study highlights that the IPSO algorithm yields smoother and straighter paths compared to others. This characteristic is advantageous in terms of energy efficiency and time optimization.
# Experimental Evaluation of a Takagi-Sugeno Fuzzy Controller for an EV3 Ballbot System +
## 2 Dynamical Model
### DC Motor Dynamics  
The governing equations for the DC motor are given by [31]  
\[L_{a}\frac{di_{a}}{dt}=v-R_{mi}l_{a}-v_{b}, \tag{10}\]  
Figure 2: EV3 BRS’s simplified model.  
where \(L_{a}\) is the armature inductance, \(i_{a}\) is the direct armature current, \(v\) is the input armature voltage, \(R_{m}\) is the armature resistance, and \(v_{b}=k_{b}\dot{q}_{2}\) is the back electromotive force (BEMF) coming from the armature. Assuming \(L_{a}\approx 0\), \(i_{a}=(v-k_{b}\dot{q}_{2})/R_{m}\).  
The DC motor torque is given by  
\[\tau=k_{t}\left(\frac{v-k_{b}\dot{q}_{2}}{R_{m}}\right), \tag{11}\]  
i.e., the input torque is a voltage scaled by \(k_{t}/R_{m}\), with \(k_{t}\) as the torque constant of the motor [32].  
Physical and electrical parametric values corresponding to the platform based on the LEGO Mindstorms EV3 kit are given in Table 1. The physical parameters from the model, such as the mass of the body, the mass of the ball, the radius of the wheel, and the height of the center of mass, were measured directly. Both the body and ball moment of inertia were computed through the relations given in Table 1, which were taken from [4]. The electrical parameters from the EV3's DC motors were taken from [35]. It is worth noting that the NXT kit's gyros are not compatible with the EV3 brick and vice versa.
# Elimination Properties for a Probabilistic Scheduling Problem
## 6 Tabu Search Algorithm
### Moves and Neighborhoods  
In each iteration of an algorithm based on a local search method using the neighborhood (moves) generator, a subset of a set of solutions, neighborhood is determined. If the solutions are permutations, most often the swap-type (_s-move_) and insert (_i-move_) are used. The first move swaps positions of several elements in permutation, and the second moves the element to a different position.  
Let  
\[\mathcal{B}=[B^{1},B^{2},\ldots B^{v}],\]  
be a division of the ordered (\(\mathcal{D}\)-OPT) permutation \(\pi\) into blocks. The \(m\) move made on permutation \(\pi\) is called improving if it generates permutation \(m(\pi)\) with a lower value of criterion function. It follows from Theorem 2 that moves consisting of swapping the order of elements in any block of \(\mathcal{D}\)-OPT permutation are not improving moves. We now consider task \(\pi(j)\) belonging to a certain block from the division \(\mathcal{B}\) of permutation \(\pi\). Moves that can bring improvement consist of swapping the task \(\pi(j)\) (_before_) the first or (_after_) the last task of this block. Let \(\mathcal{M}^{j}_{bf}(\pi)\) and \(\mathcal{M}^{j}_{af}(\pi)\) be sets of these moves. By  
\[\mathcal{M}(\pi)=\bigcup_{j=1}^{n}\mathcal{M}^{j}_{bf}(\pi)\cup\bigcup_{j=1} ^{n}\mathcal{M}^{j}_{af}(\pi), \tag{17}\]  
we designate the set of all moves that can bring improvement, i.e., moves before or after blocks of some \(\pi\) permutation. Since the procedure for splitting permutations into blocks is not unambiguous, hence the set of moves \(\mathcal{M}(\pi)\) is not explicitly defined and depends on the considered split. Formally, in the definition (17) there should be a symbol identifying a specific division. To simplify the description, it is omitted.  
For a fixed split \(\mathcal{D}\)-OPT of permutation \(\pi\in\Phi\) into blocks, the set of solutions  
\[\mathcal{N}(\pi)=\{m(\pi):\;\;m\in\mathcal{M}(\pi)\} \tag{18}\]  
is _subneighborhood_, generated with the use of \"block elimination properties\".  
The procedure for determining subneighborhood (including the elimination of certain moves generating permutations that do not improve the value of the objective function) has the complexity of \(O(n^{2})\). Its use has significantly improved the efficiency of the algorithm solving the PTWT problem.
###### Abstract  
Modern multi-junction solar cell technology offers a pathway to achieving consistent and high photovoltaic conversion efficiencies through enhanced solar spectrum absorption. Indeed, during the last years, the industries of solar cells have focused on optimizing device structures, utilizing both robust and delicate materials to maximize their performances. This paper presents the modeling and optimization of the electrical and structural properties of high-efficiency InGaP/GaAs double-junction solar cells, specifically without employing an anti-reflective coating. This developed structure has been achieved by introducing a buffer layer in the lower layer and incorporating an upper back surface field layer into the investigated cell structure. Furthermore, the optimization conducted in this paper using Silvaco-Atlas software (version 2018) under the AM1.5G spectrum reveals that the proposed InGaP/GaAs tandem cell configuration exhibits significant performance, reaching conversion efficiency of 41.585%. It can be said that this adapted structure yields a short-circuit current density of 21.65 mA/cm\({}^{2}\), an open-circuit voltage of 2.319 V, and a filling factor of 84.001%. Whereas this newly optimized structure demonstrates its effectiveness in enhancing solar cell efficiency performance, presenting highly promising results with potential significance for the devices' optical and electrical properties.  
InGaP/GaAs; anti-reflective coating (ARC); new back surface field (BSF); tandem solar cell; buffer layer; Silvaco ATLAS TCAD modeling 2025153520
## 3 Results
### Comparison between BDS Single-System and Joint POD
#### 3.3.3 SLR Assessment  
The SLR test results of strategies 1 and 3 are compared, and the results are shown in Tables 6 and 10. The tables show that the C01 satellite, as described in the previous section, has a systematic error. Strategy 1 is significantly better than strategy 3 in the case of the IGSO and MEO satellites. The accuracy of C08 and C10 was improved by 63.0% and 60.4%, respectively, and the C11 satellite was improved by 31.3%. These results are consistent with the radial precision of the overlapping arcs.
## 4 Effect Evaluation of Electromagnetic Environment on Equipment
### Effects Mechanism Methods
#### 4.2.2 Evaluation Models
The above effects evaluation models are all based on the experiment on the communication radio station. Is this a unique property of a communication radio station or a property shared by all kinds of equipment? To verify if the above models are also applicable to other equipment, [PERSON] et al. [79] and [PERSON] et al. [80] employed the third- and second-order intermodulation effects evaluation models on a navigation receiver. The effect evaluation error under critical interference is less than 1 dB, which demonstrates the excellent applicability of the third- and second-order intermodulation models. In studies, the multi-frequency interference effects model is also used to evaluate the noise interference effect. Based on the non-intermodulation blocking effect evaluation model [75], [PERSON] et al. [81] found that the model is unsatisfactory on the navigation receiver and that the existing model based on the third-order power series expansion has shortcomings. It is necessary to make further improvements to current models. In the experiment of multi-frequency non-intermodulation electromagnetic radiation effect on radar, [PERSON] et al. [71] adopted peak-level compression of the target echo as the blocking sensitivity criterion to evaluate the electromagnetic environmental effect. They found a new phenomenon named DIE (Dual-frequency Insensitive Effect). Theoretically, the fifth-order term of the nonlinear system is the root reason for the new interference effect. To solve the problem of assessing the electromagnetic environmental effects of equipment with insensitive effects, we built the assessment model [82] of the insensitive blocking effect based on the effect mechanism. The model verification was carried out on an insensitive radar and a sensitive communication radio station. On the one hand, the model's effectiveness is examined by the dual-frequency electromagnetic radiation effect test. On the other hand, the universality evaluation model research of the electromagnetic environmental effect is explored. The analysis of evaluation models based on effect mechanism analysis is shown in Table 7. Each evaluation model is appropriate for each kind of effect phenomenon. Figure 7: The research process for the evaluation models is based on the effect mechanism method. However, all evaluation models are based on the normalized interference level, \(E_{i}/E_{i0}\). In different interference effects, additional parameters will be introduced to adjust the models, such as the blocking interference factor, the normalized factor, and others. The ratio of amplitude wave level to sinusoidal continuous wave level, \(E_{ame}/E_{\text{Sinc}}\), is always adopted to simplify the evaluation model. In brief, the evaluation model based on effect mechanism analysis mainly considers the contribution of each interference component to the total interference with the normalized interference level, which provides evidence for the general model.
## 3 Results
### Effect of Beam Offset from the Flange  
The effect of the beam offset from the flange was studied only with 300 \(\upmu\)m and 600 \(\upmu\)m process fibers. Based on the very narrow bead width produced earlier with the 200 \(\upmu\)m process fiber and an assumption that the weld width largely determines the tolerance limits, the 200 \(\upmu\)m process fiber was not included in the experiments. The laser power needed for full penetration was first calculated using the Power Factor Model developed by [PERSON] et al. (2013) and subsequently determined experimentally. The calculated values exceeded the real power requirement by at least 30% (less than 8 kW vs. 9.7 kW, F\({}_{\text{PP}}\)\(-\)2 mm, beam \(\O\) on surface 1.0 mm; 10 kW vs. 13 kW, F\({}_{\text{PP}}\)\(-\)2 mm, beam \(\O\) on surface 1.6 mm). Macrographs showing beam offsets from 0.5 mm to 2.0 mm are presented in Figure 4.  
As shown in Figure 4, despite similar width of weld bead, there was a significant difference in the geometry of the fusion area. Welds produced with the 300 \(\upmu\)m process fiber had a deep and narrow profile with a slightly wider top typical of high power laser welds. The setup with the 600 \(\upmu\)m process  
Figure 4: Macrographs of weld cross-sections when beam position from the flange was varied: material = AH36, \(t\) = 8 mm, \(v_{w}\) = 1.25 m/min, F\({}_{\text{PP}}\) = \(-\)2 mm, \(\alpha\) = 15\({}^{\circ}\).  
Figure 3: Macrographs of weld samples at different beam inclination angles: AH36, \(t\) = 8 mm, \(P_{L}\) = 6 kW, \(v_{w}\) = 1.25 m/min, beam offset from flange 0.5 mm, F\({}_{\text{PP}}\) = \(-\)2 mm.  
fiber produced weld that were wide throughout the whole fusion area, resulting in a more acceptable weld profile for a T-joint.
## 3 Proposed Method
### Multiple CNNs and Layers  
Since the well known residual network was proposed, for the purpose of fair comparison, the input image size of CNN- or Transformer-based models seems to be the uniform size 224 \(\times\) 224 \(\times\) 3. Let \(\mathbf{x}\in R^{a\times b\times 3}\) be a color plant leaf image; following [20], we first resize \(\mathbf{x}\) to make the shortest edge equal 224, then the center patch of size 224 \(\times\) 224 \(\times\) 3 is cropped, which is also denoted as \(\mathbf{x}\) in this work. We feed it into a CNN model pretrained on the large-scale image dataset imagenet [53] to extract the activated feature maps from several layers. Because there exist abundant complementary elements between low-, middle- and high-level features, we explore combining the feature maps at different layers. In addition, different CNN models distinguish plant images via learning different dominant features, so three pretrained CNN models, VGG16, VGG19 [54] and ResNet50 [55], are regarded as feature extractors with the hope of learning more discriminative cues for leaves. The features map at a specific layer \(L\) is obtained as follows:  
\[\mathbf{F}_{v16}^{(L)}=VGG16(\mathbf{x},L) \tag{1}\]  
\[\mathbf{F}_{v19}^{(L)}=VGG19(\mathbf{x},L) \tag{2}\]  
\[\mathbf{F}_{res}^{(L)}=ResNet(\mathbf{x},L) \tag{3}\]  
In this paper, there are 37, 43 and 175 layers for VGG16, VGG19 and ResNet50 when the network is completely unfolded, including fully connected layers and softmax probability layer; moreover, the correlation between the convolutional features of adjacent layers is relatively strong. Therefore, in order to extract more complementation information, the activated layers of {9, 16, 23, 30} are adopted for VGG16, the activated layers of {9, 18, 27, 36} are employed for VGG19 and the activated layers of {90, 100, 110, 120, 130, 140, 152} are utilized for ResNet50.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 4 Conclusions  
A probabilistic condition monitoring algorithm was introduced and validated based on noisy, real-world acceleration signals from two azimuth thrusters used in an operating drill ship. The algorithm contributes to the general problem of sample selection by including automated procedures for the control of data quality and for the selection of shaft speed areas to be monitored. The automation accelerates the implementation of the method in a large fleet of thrusters.  
The method was tested against reference classifiers and the results were obtained through an unbiased cross-validation approach. The results suggest that the algorithm performed slightly better in a binary classification task relative to the reference classifiers when the bearing features were used in the \"probabilistic threshold\" approach. With the least sensitive feature set (i.e., time domain features), the best reference classifiers were more accurate. The use of the \"two distributions\" approach with two models generally improved the performance over the \"probabilistic threshold\" approach, which applied one model.  
The classification results suggest that the monitoring of individual samples of squared Mahalanobis distance against some threshold value results in various false positives and negatives. This was also confirmed in the monitoring tests. Therefore, the smoothed trend given by the moving median could be a more useful indicator for the practical application.  
The reduction of variation in monitoring should be further pursued. The use of additional information on the thruster operation, such as the momentary steering angle, together with the information on vessel movement could improve the accuracy. Moreover, the contributions of model parameters to the uncertainty in inference require further analysis leading the way towards the online maintenance of the model.  
Conceptualization, R.-P.N. and M.R.; methodology, R.-P.N.; software, R.-P.N.; validation, R.-P.N.; formal analysis, R.-P.N.; investigation, R.-P.N.; resources, L.S.; data curation, R.-P.N.; writing--original draft preparation, R.-P.N.; writing--review and editing, R.-P.N., M.R., J.K.-R., L.S. and F.F.; visualization, R.-P.N.; project administration, J.K.-R.; funding acquisition, J.K.-R. and R.-P.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Business Finland during the Reboot IoT Factory program.  
Not applicable.
## 2 Materials and Methods
### Satellite Data and Processing
To obtain a more detailed post-construction time-lapse, Google Earth images have been also integrated with true-color images from China-Brazil Earth Resources Satellite 4 (CBERS) taken on the 26 June 2020 and 11 July 2020, used and made available by [PERSON] et al. [79]. The first filling process started in July 2020 and in autumn 2020, therefore in late 2020 the reservoir was already well delineated (Figure 5). As detailed in Section 3.2, because a second filling process happened in July 2021, our analysis for site detection undertaken in the course of 2021 falls not only in the PCARA protocol but also in the initial phase of PFRAP, as per the methodological workflow in Figure 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite Type** & **Satellite Name** & **Imaging Mode** & **Spatial Resolution** & **Acquisition Date** & **Season** & **Dam Construction** \\ \hline SAR & COSMO-SkyMed & StripMap & 3 m & 7 July 2013 & Humid & During (early stage) \\  & \multirow{2}{*}{SrippMap} & \multirow{2}{*}{3 m} & 3 October 2016 & Humid & During \\  & & & 5-30 May 2021 & Dry & Post \\  & & & 5, 13, 25, 29 October 2021, & Humid & Post \\  & & & 10 November 2021 & to dry & \\ \hline Multi-spectral & \multirow{4}{*}{Sentinel-2} & \multirow{4}{*}{L1C (TOA)} & 28 August 2016 & Humid & During \\  & & 10 m & 7 October 2016 & Humid & During \\  & & (RGB, NIR bands) & 25 January 2017 & Dry & During \\  & & & 28 August 2018 & Humid & During (late stage) \\ \cline{2-6}  & \multirow{4}{*}{L2A (BOA)} & \multirow{4}{*}{L2A (BOA)} & 29 May 2021 & Dry & Post \\  & & & 10 m & 3, 13 July 2021 & Humid & Post \\  & & (RGB, NIR bands) & 7 August 2021 & Humid & Post \\  & & & 16, 26 October 2021, 5 & Humid & Post \\  & & & November 2021 & to dry & Post \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Archive and newly acquired satellite imagery used for documenting cultural heritage sites in the GERD dam area.