esa-sceva/satcom-chunk-collection huggingface dataset:
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 3 Workflow of DevOps and MLOps
### Operations in ML  
Building an ML model is a role designated for a data scientist with the support of a Domain expert, and it does not intersect with how the business value is produced with that model. In a traditional ML development life cycle, the operations team is responsible for deploying, monitoring, and managing the model in production. Once the data scientist implements the suitable model, it is handed over to the operations team.  
There are different techniques in which a trained model is deployed. The two most common techniques are \"Model as a Service\" and \"Embedded model\" [30]. In \"Model as a Service\", the model is exposed as Representational state transfer (REST) API endpoints, i.e., deploying the model on a web server so that it can interact through REST API and any application can obtain predictions by transferring the input data through an API call. The web server could run locally or in the cloud. On the other hand, in the \"Embedded model\" case, the model is packaged into an application, which is then published. This use case is practical when the model is deployed on an edge device. Note that how an ML model should be deployed is wholly based on the final user's interaction with the output generated by the model.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 4 Construction and Operation of SOUNET
### Phase 2: Network Maintenance
#### 4.2.2 Link Hole Recovery  
The link hole occurs when a sensor node is lost due to battery discharge or physical damage such that the branches connected to the lost node are disconnected. Since the link hole causes data loss, it is essential to recover the link hole by creating a detour or by replacing the lost node immediately with a new one. However, the problem lies in the way the sensor nodes recognize the occurrence of link holeswithout centralized notification. In SOUNET, the sensor nodes count the number of failures in _ACK_ reception, \(n_{ack\_fail}\), from the parent and if the value reaches the threshold, \(N_{MAX\_ACK\_FAIL}\), then they may regard that parent node as a link hole. The \(n_{ack\_fail}\) becomes zero when an _ACK_ or any type of packet is received, indicating that the parent is alive.  
On recognizing a link hole, a sensor node starts the link hole recovery by deleting the current parent and searching for a new parent in the neighbor table. The sensor node randomly selects the new parent node from _among the highest-leveled neighbors who are neither dead ends nor child nodes_. Regarding whether the neighbor is a dead end or not, the node easily obtains the _DE_ status of neighbors without extra signaling by overhearing _DATA_ from neighbors since the _DATA_ contains the sender's _DE_ in the packet header. Figure 3 shows an example of link hole recovery where eight nodes (from 1 to 8) are located in levels from \(i\) to \(i+3\), and the node 2 is lost (dotted circle in Figure 3b) and accordingly the neighbor tables of associated nodes 4 and 5 are updated. Nodes 4 and 5 end up detouring via new parents 5 and 3, respectively.  
If no valid parent candidate exists, the sensor nodes inevitably select a new parent from _among their child nodes_. Figure 4 shows such an example where node 3 is lost and then node 5 selects node 6  
Figure 4: Additional example of link hole recovery: (**a**) Before link hole occurrence; (**b**) After link hole recovery.  
Figure 3: Example of link hole recovery: (**a**) Before link hole occurrence; (**b**) After link hole recovery.  
as a new parent because node 7 is a dead end. In this case, node 5 also becomes a dead end because no alternative candidate for parent exists. Node 6 realizes that it is selected back as a parent by the current parent, node 5, on receiving a _DATA_ destined for itself from node 5. In addition, node 6 learns that node 5 became a dead end by reading the _DE_ field of the _DATA_ sent by node 5 and it also becomes a dead end. After that, node 6 updates the neighbor table accordingly and selects node 4 as the new parent. Later, nodes 4 and 2 also become dead ends.
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 2 Radar Digital Video Coding Depth Division Algorithm Based on Deep Learning
### Characteristics of Radar Images
The continuous updating of the coding scheme enables the radar video with a lower bit rate while maintaining the same definition. SCC has further improved the performance of coded radar video by introducing new IBC and PLT modes. Although SCC was proposed on the basis of HEVC, there are obvious differences in CTU division when using SCC and HEVC to encode the same video image, and SCC coding is of higher complexity. This is shown in Figure 11(a), which is an example of CTU division of a frame of a radar image on the Y component using HEVC encoding. We mark the ordinary CU division mode with a dark blue solid line. As shown in Figure 11(b), which is an example of CTU division on the Y component using SCC to encode the same frame of the radar image, in order to intuitively judge the difference between the two coding schemes in the CU segmentation results, we mark the case of CU using IBC mode with a red solid line, and the case of CU using PLT mode with a yellow solid line. It can be seen that the two coding schemes differ greatly in the selection of CTU division mode when encoding shipborne radar digital video. As can be seen from Figure 17, when coding a CTU with a more complex texture, in order to ensure the quality of the details of the image, HEVC will divide the CTU into smaller CUs, whereas SCC will divide the CTU into larger CUs using IBC and PLT modes. This is also one of the reasons why SCC reduces the bit rate by more than HEVC. The intra-coding processes of HEVC and SCC are shown in 17, in which it can be noted that the SCC reduces the code rate by increasing the RD-cost of the IBC/PLT mode in the process of recursively calculating the RD-cost of the CU. Nonetheless, this further division method also results in more calculations. At present, HEVC fast coding algorithms based on deep learning have mainly been proposed for the texture features of the CTU itself, ignoring the relationship with the adjacent coded CUs. Therefore, a fast CTU division algorithm suitable for HEVC cannot accurately match the CTU segmentation result of SCC, which not only increases the code rate, but also affects the encoding effect.
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 8 Conclusions  
This paper proposed a new triple mixed Gibbs sampling algorithmic approach to perform detection in large MIMO and adaptively modulated MIMO systems. The latter systems appear as the most natural type of cell-based MIMO uplink when multiple MT terminals exist and a massive MIMO BS is absent. The paper identifies the existing trade-offs in the choice of the temperature parameter, and capitalizing on those, a suitable selection for both mixing times and mixing ratios was presented. Numerical results show that the suggested variant outperforms the existing ones in the literature for symmetric large MIMO, whilst requiring a lower number of iterations. Further, exploiting the delay-less multiple restarts feature, the proposed variant is shown to obtain near-optimal performance. These results were obtained for large MIMO with full-loaded cells, rather then with loads \(0.75<\frac{N}{M}<1\) as in the recent work [27]; even so, the attained performance by the proposed T-MGS is comparable to that one, when fairly comparing the performances at the same number of iterations.  
Secondly, the paper shows how the proposed MIMO detection technique can be naturally extended to AM-MIMO, allowing us to detect in each MIMO stream a different alphabet, which is a feature that is not as straightforward to implement in other types of receivers, that often require a complicated control of the lattice boundaries. Because GS receivers do not operate in the lattice domain, but rather directly in the constellation domain, these uneven alphabets are simpler to track in the different streams.  
A natural extension of this research should be the adaptation of the T-MGS detector to soft-metrics and to coded MIMO.  
Conceptualization, F.R. and F.A.M.; methodology, F.R. and F.A.M.; software, F.R.; validation, F.R. and F.A.M.; formal analysis, F.R. and F.A.M.; investigation, F.R.; resources, F.A.M.; data curation, F.R.; writing--original draft preparation, F.R.; writing--review and editing, F.A.M.; visualization, F.R.; supervision, F.A.M.; project administration, F.A.M.; funding acquisition, F.A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FCT/MCTES (Portugal) through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Figure 12: BER performance as a function of the SNR of T-MGS detection for AM-MIMO systems in a case with \(M=4\) terminals, each of which using the modulations listed in the labels.  
**Acknowledgments:** The authors are grateful to [PERSON] from Instituto de Telecomunicacoes and Instituto Superior Tecnico, and to [PERSON] from Instituto de Telecomunicacoes, for technical discussions that contributed to improving this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Synthesis of Pure Zn-Fe LDH  
The Zn-Fe LDH photocatalyst was synthesized by a simple co-precipitation method [15; 17]. Briefly, 3.57 g of zinc nitrate hexahydrate (0.3 M, 297.5 g/mol) was dissolved into 40 mL of deionized water, and 1.616 g of iron nitrate nonahydrate (0.1 M, 404 g/mol) was dissolved in a separate beaker containing 40 mL of deionized water and magnetically stirred for proper dissolving [18]. The two solutions were then mixed in a 250 mL beaker under continuous stirring. Sodium hydroxide (2 M, 40 g/mol), used as a precipitating agent, was then added dropwise into the mixed solution under vigorous magnetic stirring until a dark orange-colored colloidal solution was obtained. The resulting orange-colored colloidal solution was filtered using vacuum filtration, and the filtered cake was washed twice to eliminate the impurities. The washed filter cake was dried in an air oven at 60 \({}^{\circ}\)C overnight, followed by grinding to finally obtain a fine powder of the Zn-Fe LDH photocatalyst. The concentration of Zn and Fe was measured by flame atomic absorption spectroscopy (AAS, Agilent 280 FS AA) and was found negligible, i.e., Zn concentration was 0.0128 \(\upmu\)g/L, and Fe concentration was 0.0095 \(\upmu\)g/L. For Zn detection, the wavelength was 307.6 nm, whereas for Fe detection, the wavelength was 372 nm. The flame consisted of air/acetylene gas.
# An End-to-End Mutually Interactive Emotion-Cause Pair Extractor via Soft Sharing
## 4 Results
### Overall Performance  
Table 3 presents the experimental performance in the ECPE task. Compared to a similar end-to-end approach E2E-PExtEUR(tm) [13], the F1 scores of our model Emiec-LSTM improved by 1.28%, 1.72% and 2.4% in the emotion extraction, cause extraction and emotion-cause pair extraction tasks, respectively. This is a strong indication that the way of using the intrinsic connection between emotion and cause to extract match pairs is correct, and the related ablation experiments will be placed in Section 5. Compared to the traditional two-step approach ECPE [12], our Emiec model improves the performance of the emotion-cause pair extraction task by an impressive 8.9%. In addition, our method has better results than the current methods ECPE-2D(BERT) [14] and ECPE-MLL(ISML-6) [15] on the most dominant prediction matching pair task, even though the number of parameters is much smaller than theirs.  
Through the case study in Figure 5, we can also visualize that the Emiec method performs well in relatively complex short texts.  
Figure 4: Repeated experiments on the determination of the value of \(\lambda_{sf}\) in different metric. A higher percentage value and a smaller variance indicate better results. It is not appropriate to use weights that are too high or too low.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Emotion Extraction**} & \multicolumn{2}{c}{**Cause Extraction**} & \multicolumn{2}{c}{**Pair Extraction**} \\  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline ECPE [12] & 0.6741 & 0.7160 & 0.6940 & 0.6039 & 0.4734 & 0.5301 & 0.4694 & 0.4102 & 0.4367 \\ ECPE-2D(BERT) [14] & 0.7435 & 0.6968 & 0.7189 & 0.6491 & 0.5353 & 0.5855 & 0.6049 & 0.4384 & 0.5073 \\ ECPE-MLL(ISML-6) [15] & 0.7546 & 0.6996 & 0.7255 & 0.6350 & **0.5919** & 0.6110 & 0.5926 & 0.4530 & 0.5121 \\ E2E-PExtEUR(tm) & 0.7163 & 0.6749 & 0.6943 & 0.6636 & 0.4375 & 0.5226 & 0.5134 & 0.4929 & 0.5017 \\ \hline Emiec-LSTM (Ours) & 0.7702 & 0.6550 & 0.7071 & 0.7010 & 0.4413 & 0.5398 & 0.5693 & 0.4903 & 0.5257 \\ E2E-BERT (Ours) & **0.8263** & **0.7441** & **0.7830** & **0.7135** & 0.5522 & **0.6225** & **0.6833** & **0.5325** & **0.5985** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best results of our model and previous experimental methods with existing metrics after hyper-parameter tuning. Bold fonts indicate the best results for the method, and underlining stands for the second best results. The top half is the performance of pipelines, and the bottom half is the one of our model.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 5 Conclusions  
To address the problem of cross-view geo-localization of UAV and satellite images, a method utilizing the Vision Transformer network structure was proposed in this paper to realize cross-view image retrieval. A color transfer module was introduced to reduce the difference between drone and satellite images, which caused the network to learn the correct global information adequately. A novel piecewise loss function was designed to avoid the model parameters being trapped in suboptimal sets. Data augmentation methods, targeting drone and satellite images, were developed to solve the image disproportion. Our method achieved the results of SOTA on the open dataset University-1652.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **R@1** & **R@5** & **R@10** & **AP** \\ \hline Ours & **91.57** & **99.29** & **99.68** & **93.31** \\ Ours w/o data augmentation & 86.51 & 97.13 & 98.55 & 88.90 \\ Ours w random color & 81.40 & 94.49 & 96.52 & 84.33 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study of data augmentation techniques. (Bold numbers mean the best results).  
Moreover, some innovations of this study deserve further attention. First, a single-stage training method was proposed, which can simultaneously extract cross-view feature information and complete image retrieval different from existing methods. Second, the developed piecewise loss function can increase the constraint on the positive sample and avoid the model converging to the local optimum.  
In future work, more accurate localization methods are worth studying. For example, methods to estimate the longitude and latitude of drones based on satellite image reference and methods to estimate the position and orientation of drones. Due to the influence of the self-attention mechanism, the feature extraction result mainly focused on the building areas in the image, which may cause the limitation of the proposed method. The lightweight model, which has a less computational burden, deserves further attention to be more widely deployed on UAVs.  
Methodology, Z.C. and X.W.; Software, Z.C.; Validation, Z.C.; Writing--original draft, Z.C. and P.Z.; Writing--review & editing, Z.Z., Y.L., H.L. and Y.Z.; Supervision, H.L.; Funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NSFC 62088101 Autonomous Intelligent Unmanned Systems, in part by STI 2030-Major Projects 2021 ZD0201403.  
All codes will be publicized to promote reproducibility. [[https://github.com/zfcui33/CCA](https://github.com/zfcui33/CCA)]([https://github.com/zfcui33/CCA](https://github.com/zfcui33/CCA)) accessed on 14 September 2023.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Development and Characterization of a DC-Driven Thermal Oscillator Using Acrylate-Based Composites
## 2 Working Principle and Device Design  
In this study, we implemented a PTC-based thermal oscillator by employing the characteristic of the reversible rapid resistivity change of PTC composites due to temperature variation. Figure 1a shows a schematic and the exploded view of the proposed oscillator device. The device consists of a microheater, a PTC composite temperature sensor, and a C-shaped planar heat sink. All three components are implemented on a glass substrate.  
An electrothermal microheater made of a Ti/Au layer acts as a heat source, which heats the PTC composite by heat conduction. The PTC composite film, which is laid on the interdigital electrodes, senses the changes in resistance in response to temperature changes. In addition, the glass substrate provides the main heat conduction path between the microheater and the PTC temperature sensor. The C-shaped heat sink surrounding the sensor is used to improve the temperature uniformity around the PTC composite film for speeding up the cooling process.  
Figure 1b shows the typical resistance-temperature characteristic of acrylate PTC materials. The temperature dependence before the melting point (\(T_{m}\)) is weak. However, a strong increase of a few orders of magnitude in resistivity is observed around \(T_{m}\). This behavior is repetitive, and the resistivity returns to almost the same value after a heating/cooling cycle. In addition, the hysteresis effect of the acrylate-based PTC material is essentially insignificant [19].  
Figure 1c shows an equivalent circuit diagram of the DC-driven thermal oscillator using the PTC material. \(V_{s}\) is the total voltage across the PTC temperature sensor (\(R_{PTC}\)) and the divider resistor  
(\(R_{div}\)).\(V_{h}\) is a fixed DC voltage applied to the microheater (\(R_{h}\)). A MOSFET (IRF3205) was used to switch the microheater on and off.  
Figure 1d illustrates the temperature oscillation induced by PTC composite because of its rapid resistivity change due to temperature.  
As the heater turns on, the temperature of the PTC composite increases. Additionally, \(R_{PTC}\) increases with the elevated temperature, which results in a decrease in \(V_{div}\), which is the gate-to-source voltage of the MOSFET. Once the \(V_{div}\) drops below the threshold voltage of the MOSFET, the MOSFET switches to the cut-off region. Consequently, the microheater turns off, and the system therefore cools down. As the temperature of the PTC composite decreases, its resistance also decreases significantly, which, in turn, increases \(V_{div}\). The heating process starts again as the MOSFET switches to the saturation region because the voltage across \(R_{div}\) (i.e., \(V_{div}\)) exceeds the threshold voltage of the MOSFET.
## 3 Results and Discussion
The influence of the percentage substitution on the relative compressive strengths(blended mortar/OPC mortar) of the standardized mortars at 2 and 28 days of hydration is represented in Figure 7. The progressive increase in strength may be clearly seen in the first 48 h of the reaction, increasingly so when the percentage substitution was higher for the case of the APS pozzolan, although with substitutions of 10% the compressive strength was slightly lower than the reference mortar [42]. However, the activated carbon waste showed a significant gain in compressive strength with substitutions of 10%, falling behind the reference mortar with incorporations of 20%. With a longer hydration time of 28 days, the APS pozzolan increased in strength in both proportions with respect to the OPC mortar; however, the ACW pozzolan never reached the reference values, decreasing \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Oxides (\%)**} & **C-S-H Gels** & **C-S-H Gels** & **C-S-H Gels** & **C-S-H Gels** & **C-S-H Gels** \\  & **(at 28 Days) in** & **(at 28 Days) in** & **(at 28 Days) in** & **(at 28 Days) in** & **(at 28 Days) in** \\  & **OPC** & **OPC** 10\% APS** & **OPC** 20\% APS** & **OPC** 10\% ACW** & **OPC** 20\% ACW** \\ \hline Na\({}_{2}\)O & n.d. & n.d. & n.d. & 0.90 \(\pm\) 0.31 & 0.98 \(\pm\) 0.52 \\ \hline MgO & n.d. & n.d. & n.d.- & 0.90 \(\pm\) 0.28 & 0.95 \(\pm\) 0.33 \\ \hline Al\({}_{2}\)O\({}_{3}\) & 10.60 \(\pm\) 1.21 & 16.12 \(\pm\) 1.32 & 18.74 \(\pm\) 1.36 & 1.57 \(\pm\) 0.64 & 2.12 \(\pm\) 0.33 \\ \hline SiO\({}_{2}\) & 20.91 \(\pm\) 1.49 & 28.26 \(\pm\) 1.74 & 25.63 \(\pm\) 2.45 & 31.25 \(\pm\) 1.37 & 28.15 \(\pm\) 2.16 \\ \hline SO\({}_{3}\) & n.d. & n.d. & n.d. & 2.10 \(\pm\) 0.62 & 2.007 \(\pm\) 0.40 \\ \hline K\({}_{2}\)O & n.d. & n.d. & n.d. & 1.84 \(\pm\) 0.35 & 1.93 \(\pm\) 0.42 \\ \hline CaO & 68.49 \(\pm\) 2.25 & 52.38 \(\pm\) 2.37 & 47.87 \(\pm\) 2.39 & 60.31 \(\pm\) 1.16 & 60.15 \(\pm\) 1.19 \\ \hline Fe\({}_{2}\)O\({}_{3}\) & n.d. & n.d. & n.d. & 1.05 \(\pm\) 0.11 & 1.11 \(\pm\) 0.23 \\ \hline Ca/Si & 3.28 & 1.85 & 1.88 & 1.93 & 2.14 \\ \hline Si/Al & 1.97 & 1.75 & 1.37 & 19.90 & 13.28 \\ \hline Al/Ca & 0.15 & 0.31 & 0.39 & 0.03 & 0.03 \\ \hline Number of analyses & 10 & 10 & 10 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis by EDX from the C-S-H gels. with the increase in the percentage substitution. This development of compressive strengths would be directly related with the evolution of the fixed lime, as commented earlier (Figure 3). Figure 6: Morphologies of the C-S-H gels at 28 days of cure time. Figure 7: Evolution of the resistances relative to compression (CS) with the hydration time.
## 4 Dynamic Reconfiguration of Distribution Network
### Solution Algorithm  
The dynamic reconfiguration of distribution network is a complex non-convex, mixed-integer programming and typical non-linear problem, which appropriately involves the use of the evolutionary algorithms such as genetic algorithm (GA). Figure 3 shows the flowchart of reconfiguration model. To preserve the radial structure of the network, each loop can only have one switch open at a time. Thus, a gene in the chromosome represents the ID of opened switch in a loop. Based on the total number of loops in the network, the decimal coding is used to represent the chromosome by sequencing the opened switches of all the loops [30].