esa-sceva/satcom-chunk-collection huggingface dataset:
# Supply Chain Data Analytics for Digital Twins:
## 2 Theoretical Background
### Digital Twin Approaches  
Despite their widespread adoption, digital twins are often ambiguously defined and can vary in functionality and integration levels. The first standardized definition comes from [PERSON] who categorized them as Digital Model, Digital Shadow, and Digital Twin, depending on the extent of data integration, where the digital model only contains information from the physical, the digital shadow includes real-time data and the digital twin, in addition, can provide real-time feedback to the physical system [7]. DTs use real-time data to model the physical asset and analyze its status.  
The rise of AI proved valuable to be incorporated in DTs to model physical assets and led to their improvement being renamed Cognitive Digital Twins (CDT). CDTs encapsulate AI models and services, allowing them to reason in specific situations, predict the impact of different scenarios, and adapt their behavior accordingly [8]. These predictions and adaptations can come from the incorporation of optimizers, recommender systems, anomaly detection models, or any other type of AI-based models. Thus, CDT can be used not only for monitoring but can also be part of the decision-making process.  
In the context of supply chains where multiple actors exchange goods to manufacture a product, there is a collaboration between them. In the case of a collaborative scenario, different actors can agree to develop a CDT that exchanges the required information between them. In this form, the individual use of a CDT can be expanded into the concept of Supply Chain Cognitive Digital Twin (SC-CDT). Thus, the SC-CDT is a network of interrelated CDTs involving all actors and assets. The SC-CDT governance framework encompasses business collaboration terms, data, and AI models at the CDT level, with sustainability being a crucial factor driving decisions [9].
# _atmosphere_
## 4 Discussion  
### Environmental Controls on CO\({}_{2}\) Fluxes  
The variations in growing season CO\({}_{2}\) fluxes were mainly determined by atmospheric water vapor in the two alpine wetlands (Figures 3 and 7), which is inconsistent with the long-standing view that thermal conditions predominantly control CO\({}_{2}\) fluxes in these temperature-limited wetlands [14; 15; 40]. There are two potential explanations for this finding from the perspectives of the atmospheric water vapor itself and plant physiology. First, atmospheric water vapor is an integral indicator of hydrothermal status and can exert a substantial influence on land carbon uptake variability through its indirect effects from soil moisture-atmosphere feedbacks [41; 42]; this is indeed exponentially related to \(T_{\rm a}\) (atmospheric water vapor = \(3.9e^{0.087A}\), \(R^{2}=0.97\), \(p<0.001\)) and \(T_{\rm s}\) (atmospheric water vapor = \(2.6e^{0.117\rm s}\), \(R^{2}=0.83\), \(p<0.001\)) and would, thus, be a reasonable proxy of thermal conditions. Second, the optimum temperature of plant physiological activity is closely related to the growing season air temperature due to long-term plant evolutionary adaptation [43]. In other words, current thermal conditions should be comparable for analogous plant photosynthesis and growth within a certain cold site [44; 45]. Additionally, the effects of thermal conditions on CO\({}_{2}\) fluxes are markedly moisture-dependent, based on reports from warming experiments in alpine regions [45; 46]. Thus, atmospheric water vapor should be a reliable metric of hydrothermal conditions controlling growing season CO\({}_{2}\) fluxes in alpine wetlands [12; 35]. The nongrowing season CO\({}_{2}\) fluxes were jointly controlled by site attributes and \(T_{\rm s}\) (Figures 3 and 8). This stems from the fact that ecosystem respiration consists of root autotrophic respiration and microbial heterotrophic respiration, which are closely related to substrate availability and soil temperatures in alpine regions [9; 44]. Therefore, atmospheric water vapor, more than air/soil temperatures, regulated the variability in growing season CO\({}_{2}\) fluxes, while site attributes and soil temperatures jointly controlled nongrowing season CO\({}_{2}\) emissions in our wetlands. These findings are consistent with previous studies [4; 12; 18]. Given that the nongrowing season temperature is projected to further increase across the QTP [47], these alpine wetlands will likely release more carbon and positively feedback to climate change [5]; this prediction is different from the model simulations of a single site where little attention was paid to nongrowing season CO\({}_{2}\) dynamics [13; 26].
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
## 6 Discussion  
It should also be noted that the maximum number of iterations is improved over previous designs in 5G-NR and 4G-LTE. Model 1 can be compared to the design in [11] because it has the same decoder parameters. In particular, our design significantly reduces latency with six iterations of improvements and up to a 0.2 dB (for BER = 2.10\({}^{-3}\)) SNR increase (see Figure 7a). The rate 2/3 was not simulated for the maximum expansion factor in previous studies, and therefore we are providing an estimated comparison with references [12; 14]. In [12], a rate of 2/3 was used with an expansion factor of Z = 104, while in [14], they used a rate of 1/2 which was expected to perform better than 2/3. Despite this, our design achieved an SNR performance gain of up to 0.2 dB compared to [14] and 0.6 dB compared to [12] for BER = 2.10\({}^{-2}\) (see Figure 7b). Furthermore, we have achieved a remarkable reduction in the number of iterations, bringing it down to 6, as opposed to 15 and 20 iterations found in references [12; 14], respectively (see Table 1). It is noteworthy that our study has simulated the maximum attainable codeword length for a rate of 2/3.  
The previously discussed decoder simulation results were obtained using the MS algorithm. The AMS and OMS algorithms are known to outperform the MS algorithm because they tend to overestimate LLR values. However, this issue was not a significant challenge in our work, and due to the small number of iterations, the correction process was achieved before the overestimation became a concern.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**This Study**} & \multirow{2}{*}{[11]} & \multirow{2}{*}{[12]} & \multirow{2}{*}{[13]} & \multirow{2}{*}{[14]} & \multirow{2}{*}{[39]} & \multirow{2}{*}{[43]} \\  & & & & & & & \\ \hline Expansion factor & 384 & 384 & 384 & 104 & 32 & 384 & 384 & - \\ \hline Rate & 1/3 & 2/3 & 1/3 & 2/3 & 4/5 & 1/2 & 1/3 & 1/2 \\ \hline SNR (dB) & 1.7 dB & 2.9 dB & - & - & 4 dB & - & 2.4 dB & - \\ \hline Codeword length & 26,112 & 13,440 & 26,112 & 3432 & 1280 & 16,128 & 26,112 & 12 \\ \hline Information length & 8448 & 8448 & 8448 & 2288 & - & 8448 & 8448 & - \\ \hline Number of iterations & 4 & 6 & 10 & 15 & 20 & 20 & 15 & 10 \\ \hline Decoding algorithm & MS & MS & OMS & AMS & SMS & MS & MS \\ \hline Matrix size & 46 \(\times\) 68 & 13 \(\times\) 35 & 46 \(\times\) 68 & 13 \(\times\) 35 & 22 \(\times\) 44 & - \\ \hline Decoding parallelism & Fully & Fully & Fully & Partially & Partially & Partially \\ \hline Standard & 5G-NR & 5G-NR & 5G-NR & 5G-NR & 5G-NR & 4G-LTE \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed 5G-NR LDPC decoder with the state-of-the-art.  
Figure 7: Comparison of the proposed decoder’s SNR performance with other designs, (**a**) [11] based on the rate 1/3 and (**b**) (AMS [12], MS [14]) based on the rate 2/3.
## 3 Results
### The Influence of Human Factors, Geography, Climate, and Vegetation on Captures
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **N** & **Minimum** & **Maximum** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Ner & 30 & 1 & 100 & 33.830 & 28.122 \\ A & 30 & 1199.360 & 1692.380 & 1465.333 & 109.575 \\ AI & 30 & 1.091 & 1.420 & 1.291 & 0.096 \\ DR & 30 & 0.385 & 14.266 & 6.271 & 3.986 \\ E & 30 & 16.674 & 848.089 & 281.473 & 191.900 \\ MP & 30 & 1315.680 & 1446.880 & 1387.384 & 35.542 \\ MPS & 30 & 47.962 & 51.773 & 50.017 & 1.057 \\ MT & 30 & 116.157 & 140.953 & 132.790 & 7.398 \\ MTS & 30 & 3616.450 & 4668.230 & 4100.121 & 347.627 \\ NDVI & 30 & 147.664 & 195.797 & 180.142 & 10.265 \\ PD & 30 & 0.047 & 665.646 & 107.748 & 131.344 \\ S & 30 & 2.075 & 20.159 & 12.176 & 4.303 \\ WS & 30 & 3.922 & 7.549 & 5.704 & 0.912 \\ \hline \end{tabular} Note: Ner—number of beehives, A—Albedo, All—Aridity Index, DR—distance to a main river, E—elevation, MP—mean annual precipitation, MPS—mean annual precipitation seasonality, MT—mean annual temperature, MTS—mean annual temperature seasonality, NDVI—normalised difference vegetation index, PD—population density, S—slope, WS—wind speed.  
\end{table} 
Table 3: Descriptive statistics of the independent variables. with caution, as the minimum and maximum values in the sample are 3.922 m/s and 7.549 m/s, respectively. Some of the independent variables included in the models are correlated (see Table 4), and the effects on captures described may overshadow one another. Some easily explained correlations correspond to the increase in albedo with population density as in built environments the lack of vegetation conduces to an increased reflection of sunlight. The positive correlation between elevation and precipitation is also explained by increased condensation leading to precipitation, and the negative correlation with temperature is common knowledge. The negative correlation between elevation and population density is also evident as villages and cities in the region are in lowland areas. The negative correlation between precipitation and temperature is also explained by the fact that precipitation increases with elevation and, therefore, with decreasing temperatures.
# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 3 The Proposed Algorithm  
In Figure 3, an overview of our algorithm is shown. Our approach consists of an image preprocessing step, in which input images are filtered and some primitive information such as image gradients \((g_{x},g_{y},d)\) are extracted. Then, in a second step, relevant patterns such as edge segments \(s\) are extracted and described using our directional pixel voting descriptor. Finally, the input image is compared with a target (template), which can be an object or a scene, to determine whether it can be recognized or not.
# Compact Single-Frequency MOPA Using a Silica Fiber Highly Doped with Yb\({}^{3+}\)
## 4 Conclusions  
We have reported a single-frequency polarization-maintaining fiber MOPA utilizing a highly Yb-doped fiber with a relatively large NA. This combination efficiently suppressed SBS while allowing the fiber to be coiled on a very small mandrel (2 cm radius), thus producing a compact packaged device. Its performance shows that the silica fiber technology is now at a stage where fibers with sufficiently high Yb concentrations can be fabricated for compact, simple MOPA amplifiers outputting tens of watts and offering a good trade-off between SBS suppression, beam quality, thermal load, and photodarkening. Future work should focus on further improving the core-glass composition to further reduce the reduction in power due to the residual photodarkening loss. Possible avenues include increasing the Ce concentration and offsetting the related increase in core index by increasing the fluorine concentration or with a pedestal index profile.  
Conceptualization, E.B. and M.E.; methodology, E.B.; investigation, E.B., M.E. and R.S.C.; resources, G.E. and M.E.; data curation, E.B.; writing--original draft preparation, E.B. and M.J.F.D.; writing--review and editing, E.B., M.J.F.D., M.E., H.-E.N., R.S.C. and G.E.; supervision, M.J.F.D., M.E. and H.-E.N.; funding acquisition, M.E. All authors have read and agreed to the published version of the manuscript.  
This research was funded by EU Regional funds: 135790; the Knowledge Foundation (KKS): 0868895.  
Not applicable.  
Data Availability Statement: Data is available on request.  
Cobolt: a Hubner Group Company (Sweden), Monocrome (Spain), and ITF Technologies (Canada) are gratefully acknowledged for their support.  
The authors declare no conflict of interest.  
Figure 4: Measured output power of the MOPA using fiber I showing a slow decrease over time as a result of residual photodarkening loss.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### System Model
#### 3.3.1 Coordination Transformation  
The positions of the sensor nodes in the image plane are computed with the coordination transformation. Since we assume a moving receiver drone, the relative position between \((x_{i},y_{i},z_{i})\) and the origin \((x_{c}(t),y_{c}(t),z_{c}(t))\) is computed. The position of the \(i\)th sensor node in the camera coordination system is formulated as  
\[\left[\begin{array}{c}X_{i}(t)\\ Y_{i}(t)\\ Z_{i}(t)\end{array}\right]=\left[\begin{array}{ccc}1&0&0\\ 0&\cos(-\theta)&-\sin(-\theta)\\ 0&\sin(-\theta)&\cos(-\theta)\end{array}\right]\left[\begin{array}{c}x_{i}-x_ {c}(t)\\ y_{i}-y_{c}(t)\\ z_{i}-z_{c}(t)\end{array}\right] \tag{1}\]  
which consists of the parallel displacement and the rotation by the elevation angle.  
Then, the position of the \(i\)th sensor node in the image plane is computed with the perspective transformation as  
\[(u_{i}(t),v_{i}(t))=\left(\begin{array}{cc}\frac{l_{h}}{2\tan\left(\frac{a_{ h}}{2}\right)}&0\\ 0&\frac{l_{v}}{2\tan\left(\frac{a_{h}}{2}\right)}\end{array}\right)\left( \begin{array}{c}\frac{X_{i}(t)}{Y_{i}(t)}\\ \frac{Z_{i}(t)}{Y_{i}(t)}\\ \frac{Z_{i}(t)}{Y_{i}(t)}\end{array}\right)+\left(\begin{array}{c}\frac{l_{ h}}{2}\\ \frac{l_{v}}{2}\end{array}\right). \tag{2}\]  
The perspective transformation is depicted in Figure 3. Let \(p_{i}\) denote the size of a sensor node in the image plane. It is calculated with the radius of the projected circle as  
\[p_{i}=\frac{r_{i}}{\rho Y_{i}(t)}f. \tag{3}\]  
\begin{table}
\begin{tabular}{c l} \hline
**Variable** & **Definition** \\ \hline \(\mathcal{I}\) & Set of sensor nodes \\ \(i,j\) & Sensor node identifier in \(\mathcal{I}\) \\ \(r_{i}\) & Radius of \(i\)th sensor node \\ \((x_{i},y_{i},z_{i})\) & Position of \(i\)th sensor node in global \\  & coordinate system \\ \((x_{c},y_{c},z_{c})\) & Position of camera in global coordinate system \\ \((u_{i},v_{i})\) & Position of \(i\)th sensor node in image plane \\ \(l_{h}\) & Horizontal resolution of image plane \\ \(l_{v}\) & Vertical resolution of image plane \\ \(\phi_{h}\) & Horizontal angle of view \\ \(\phi_{v}\) & Vertical angle of view \\ \(\theta\) & Elevation angle of camera \\ \(f\) & Focal length of camera \\ \(\rho\) & Image sensor size \\ \(R_{i}\) & Data rate \\ \(S_{i}\) & Spatial multiplicity \\ \(D\) & Symbol rate \\ \(N_{i}\) & Symbol number for CSK \\ \(M_{i}\) & Maximum data size \\ \(T_{i}\) & Transmission time \\ \(a\) & Top length of camera coverage trapezoid \\ \(b\) & Bottom length of camera coverage trapezoid \\ \(c\) & Height of camera coverage trapezoid \\ \(h\) & Altitude of receiver camera \\ \(\mathcal{A}\) & Ground coverage of receiver camera \\ \hline \end{tabular}
\end{table}
Table 1: Variables.  
The conditional expression that the \(i\)th sensor node is filmed by the receiver drone is formulated as  
\[\left\{\begin{array}{l}0\leq u_{i}(t)-p_{i}\wedge u_{i}(t)+p_{i}\leq l_{h}\\ 0\leq v_{i}(t)-p_{i}\wedge v_{i}(t)+p_{i}\leq l_{v}.\end{array}\right. \tag{4}\]
## 2 Problem Description
### Assumptions of the Problem  
* The study area is divided into several regions. Regions are subsets of a larger area called zones, which are covered by hubs.
* In this study, three levels of hubs are considered: level w hubs, which have the lowest capacity level and provide primary services. Level c hubs provide complementary services. Finally, level h hubs provide the most advanced services and also provide level c hub services.
* Each demand point must be covered by one level w hub, one level c hub, and one level h hub. However, level h hubs can also provide level c hub services. Therefore, if a demand point is covered by a level h hub, there is no need for it to be covered by a level c hub.
* In each region, there must be at least one level w hub.
* In a region, it is not possible to have both level c and level h hubs simultaneously.
* In a zone, there cannot be more than one level h hub.
* Demand is considered to be point-wise.  
In Figure 1, different scenarios of demand coverage based on the assumptions are depicted. In the first scenario, the demand point is allocated to all three available levels of hubs. In this case, the demand for each level is assigned to hubs of the same level. In the second scenario, the demand point is allocated to two hubs at levels w and h. In this case, as described in the assumptions, the demand for level c is covered by the level h hub because level h hubs can provide services for level c hubs as well.  
Figure 1 illustrates the architectural framework for the BTS site selection model, emphasizing its integration of sustainability and dynamic demand patterns. This figure supports the conceptual understanding of how different parameters influence the optimization process.
### Types of Antenna Systems Used in Underground Mining
#### 1.2.4 Radio Frequency Identification (RFID) Antennas  
RFID antennas are intended to broadcast and receive radio waves in order to communicate with RFID tags. When these tags come within range, they include miniature microchips that may store data and transmit it back to the antenna. RFID antennas are normally built of metal or conductive material and are available in a variety of shapes and sizes [43; 44]. [PERSON] et al. [45] designed an RFID tag antenna at 915 MHz for underground localization applications. A miniaturization of 31% in antenna size was achieved, and they analyzed the substrate selection for antenna efficiency. [PERSON] and [PERSON] [46] proposed a CP antenna for a RFID tag to be implemented in an underground localization system. The fully covered ground plane makes the tag robust due to less interaction with the attached medium. The extended metal plate provides a wide bandwidth of 37 MHz and axial ratio bandwidth of 6.4 MHz with a reading range of 2.2 m.
# Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
## 3 Results and Discussions
### U-Net and DeepLabV3+ Training Results
#### 3.1.3 Quantitative Comparison  
Table 1 shows the final measurement of every evaluation metric. The F1 and mIoU for U-Net were 0.7199 and 0.5993 respectively, while the F1 and mIoU for DeepLabV3+ were 0.6478 and 0.5174 respectively. Based on these results, U-Net clearly outperformed DeepLabV3+. The only difference between U-Net and DeepLabV3+ is that U-Net tends to have better detection rate for the spalling class than for the crack class, while DeepLabV3+ tends to have better detection rate for the crack class than for the spalling class. The void class had the lowest values of F1 and mIoU for both models. This is arguably due to the large number of false negatives, caused by extensive labeling of small voids in the training data. Both models typically cannot detect voids that are smaller than four pixels in diameter. These results also suggest that the background class needs less attention than the other classes. Therefore, weighting 0.1 for background to the loss function is sufficient.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 4 Antennas for mmWave Industry 4.0 and beyond Communication
### LTCC-Based Antennas  
LTCC technology has emerged in recent years for the miniaturization of portable electronics devices. It is a multilayer technology and eludes many disadvantages of PCB-based designs. LTCC is a multilayer technology that has been used for packaging ICs and has been applied to sensors, actuators, and integrated microsystems with relatively low cost and high productivity [106; 109; 135]. LTCC-based antennas can be employed with efficient characteristics in industrial environments as well. Various circularly polarized (CP) and linearly polarized (LP) LTCC-based antennas are reported [104; 107; 110; 111; 112]. Since the electric field of the wave only changes direction in a circular fashion in circularly polarized antennas, the signal strength remains constant. Thus, high gain CP antennas can be more useful for effective short-range communication in a rich scattering industrial environment. An LTCC-based phased array antenna at 60 GHz integrated with a 28 nm CMOS transceiver at 60 GHz was reported in [136].  
In order to mitigate the high cost of large arrays at the 60 GHz band for beamforming or MIMO, a metamaterial-based LTCC compressed Luneburg lens antenna is reported at the 60 GHz band [113]. An assembly of stacked multilayer PCB was used to realize a high gain LTCC-based Luneburg lens antenna. A comprehensive review of various LTCC-based antenna designs at 60 GHz is presented in [106]. In the future, LTTC-based antenna designs can be employed in Industry 4.0 and beyond at 60 GHz as a low-cost and compact solution.
# A New Chaotic-Based Approach for Multi-Objective Optimization
## The Tornado Chaotic Search Algorithm
### Tornado Principle
#### 2.2.3 Chaotic Fine Search (CFS)
```
1:Input:\(f\), \(\omega\), \(L\), \(U\), \(Z\), \(N_{f}\), \(N_{p}\)
2:Output:\(X_{l}\): _the best solution among local chaotic points_
3:\(\mathcal{R}=\frac{1}{2}(U-L)\);
4:\(X=\omega\);
5:\(X_{l}=\omega\); \(Y=f(\omega)\);
6:for\(\eta=0\) to \(N_{l}-1\)do
7: Compute the \(\eta-\)error \(\widetilde{\epsilon_{\eta}}\) and then evaluate \(\widetilde{R}_{\eta}\) using Equations (27)\(-\)(29)
8: Generate two symmetrical chaotic variables \(X_{1},\ X_{2}\) according to (23)
9:for\(i=1\) to \(2\)
10: Choose randomly p in \(\{1,\cdots,n\}\) and decompose\(X_{i}\) using (19)
11: Generate \(N_{p}\) symmetrical points \(X_{i,j}\) according to (20)
12:for\(j=1\) a \(N_{p}\)
13:if\(Y>f(X_{i,j})\)then
14:\(X_{l}=X_{i,j};Y=f(X_{i,j})\);
15:endif
16:endfor
17:endfor
18:endfor
```  
**Algorithm 4** Chaotic Fine Search (CFS). ```
1:Given :\(f\), \(L\), \(U\), \(Z\), \(M\), \(M_{l}\), \(N_{c}\), \(N_{l}\), \(N_{f}\), \(N_{p}\)
2:Output :\(X,Y\)
3:\(k=1\); \(Y=+\infty\);
4:while\(k\leqslant M\)do
5:\(X_{c}=\mathbf{CGS}\)\((f,L,U,Z_{k},N_{c})\)
6:if\(Y>f(X_{c})\)then
7:\(X=X_{c}\); \(Y=f(X_{c})\);
8:endif
9:\(s=1\);
10:while\(s\leqslant M_{l}\)do
11:\(X_{l}=\mathbf{CLS}\)\((f,X,L,U,Z_{s+k},N_{l},N_{p})\)
12:if\(Y>f(X_{l})\)do
13:\(X=X_{l}\); \(Y=f(X_{l})\);
14:endif
15:\(X_{f}=\mathbf{CFS}\)\((f,X,L,U,Z_{s+k},N_{f},N_{p})\)
16:if\(Y>f(X_{l})\)do
17:\(X=X_{f}\); \(Y=f(X_{f})\);
18:endif
19:\(s=s+1\);
20:endwhile
21:\(k=k+1\);
22:endwhile
```  
**Algorithm 5** Tornado Pseudo-Code.