esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 5 Experiments
### Experimental Results  
All the experimental results for the three datasets are listed in Table 3. Our experiments were conducted five times and the results presented are the average performances of all domain adaptation tasks. Figures 2 and 3 show the results for the ImageNet and Leaves datasets, respectively. The following conclusions are drawn from the experimental results:  
* All the domain adaptation methods significantly and consistently outperformed the standard SVM classifier, demonstrating the advantages of the feature-representation method in a broader set of scenarios.
* Compared to shallow learning methods, such as TCA, autoencoder-based methods, such as TLDA, TLMRA, and SSRLDA, all achieved superior results in unsupervised domain adaptation, indicating the superiority of deep-learning-based methods in learning transferable and discriminative features across domains. Notably, mSDA achieved comparable performance to TCA, demonstrating that the traditional structure of the autoencoder cannot learn sufficient features. This is why other autoencoder-based methods require improvements in architecture.
* In comparison with mSDA, our SCSA achieved better performance in all tasks for three different datasets, demonstrating the superiority of our framework for exploring different domains compared to autoencoder-based domain adaptation methods.
* By comparison to other autoencoder-based deep methods, such as TLDA and TLMRA, our proposed SCSA achieved better performance for overall tasks in the same target domains and for the same problems. These methods rely on the classical structure of autoencoders (i.e., TLMRA) or the integration of regularization terms into the objective function (i.e., TLDA). The results confirm that our SCSA can explore abstract and distinctive features for domain adaptation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Sets** & \multicolumn{2}{c}{**Configurations**} \\ \hline \multirow{3}{*}{Corel Data Set} & Kernel Size & \(11\times 11\times 3\) \\ \cline{2-3}  & Maps Number & 1000 \\ \cline{2-3}  & Pool Type & max \\ \cline{2-3}  & Pool Size & \(12\times 12\) \\ \hline \multirow{3}{*}{ImageNet Dataset} & Kernel Size & \(10\times 10\times 3\) \\ \cline{2-3}  & Maps Number & 500 \\ \cline{2-3}  & Pool Type & max \\ \cline{2-3}  & Pool Size & \(24\times 24\) \\ \hline \multirow{3}{*}{Leaves Dataset} & Kernel Size & \(6\times 6\times 3\) \\ \cline{2-3}  & Maps Number & 800 \\ \cline{1-1} \cline{2-3}  & Pool Type & mean \\ \cline{1-1} \cline{2-3}  & Pool Size & \(3\times 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main configurations of SCSA on Datasets.
## 3 Numerical Model and Calculation Scheme
### PFC Simulation Results Analysis
#### 3.3.3 Overburden Stress Field Evolution Analysis
After the main roof is broken, the load capacity is greatly reduced, and the overlying strata load gradually acts on the rock strata above the main roof. The strong chain transfers to the rock stratum above the main roof, and the arch height further increases. At this time, the maximum overburden vertical stress peak is about 38.5 MPa. Figure 22: Whole force chain. Figure 23: _Cont._  
When the working face advances to 70 m and 80 m, the span of the powerful chain arch further increases with the advance of the working face, and the load borne by the rock strata above the main roof gradually increases. At this time, the maximum overburden vertical stress peak is about 40 Mpa. When the working face advances to 90 m, the load between the slabs exceeds its strength limit, fracture instability occurs, and the strong chain between the slabs breaks accordingly. At this time, the overlying strata of the goaf are arched as a whole, and the load capacity of each rock stratum in the arch is greatly reduced. The strong chain is transferred to the overlying strata of the arch to form a new strong chain arch, and the load is transferred from the side of the arch to the deep rock mass. At this time, the vertical stress peak of overburden rock is about 42 Mpa. In summary, it can be seen that the load borne by the overlying strata in the mining process of the deep high-stress slope is the macroscopic embodiment of the contact force chain, and there is a positive correlation between the strength of the force chain and the load. After coal seam mining, the load of overlying strata is transmitted to the lower strata and coal wall through the force chain to form the force chain arch. The force chain arch is mainly composed of a large number of strong chains, forming an obvious load transfer path in the main direction. With the continuous advancement of the working face, the overburden force chain of the slope is constantly changing. The overlying strata have experienced the dynamic process of the generation of the strong chain arch, the stability of the strong chain arch, the increasing span of the strong chain arch, and the breaking of the strong chain arch. The transmission direction of the strong chain reflects the process of the load transfer path of the overlying strata from the affected area to the separation area and then to the compaction area. It can be seen that in the deep high-stress slope, the instability and fracture of the strong chain with the advancement of the working face is the root cause of various dynamic load phenomena.
extra electricity during the night, when the SHS battery is discharged. Such activities create the need for a virtual marketplace to be able to accommodate and facilitate two-way transactions, purchases and sales.  
Blockchain, according to Reference [13], can be defined as \"an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way\". It gained mainstream attention in 2009 through its application to BitCoin, which was the first open-source, peer-to-peer, digital, decentralized cryptocurrency. The applications, though, of blockchain surpass digital currency and go beyond affecting different realms of our daily lives [14]. The applications in the energy field have been gaining attention, starting from research [15; 16] leading to demonstration projects by start-ups [17], which have caught the attention of traditional electrical industry actors [18]. A distinction has to be made between permissioned or private blockchains and permissionless or public blockchains, and it has to be mentioned that the private blockchains are usually called \"distributed ledgers\" [19]. A comparison of permissioned and permissionless blockchains is made in Table 1.  
Blockchain, in principle, could provide a solution for the creation of a virtual marketplace in rural areas of the developing world, since it can cover the scenarios analyzed previously. While many blockchain applications have been proposed for grid-connected systems, there is a lack of applications for off-grid systems. Most current blockchain applications need uninterrupted access to the Internet. While this can be taken for granted in grid-connected scenarios, most locations in the developing world have either no Internet access or the access through mobile telephone networks is intermittent, characterized by very low connection speeds and low-quality connections. In theory, a blockchain could be realized in a local network, but a minimum number of computers have to exist in order for decentralized verification of transactions to take place. Processing power in rural areas of the developing world is scarce, and consists mainly of smartphones. New research has to take place in order to decrease the decentralized processing power needed by a blockchain in such a reality. New private blockchain verification algorithms, with the use of pre-selected trusted validators, could be one of the ways forward. Moreover, increasing interest is drawn to the combination of the Internet of Things with blockchain applications [20]. Potentially compatible sensors, actuators and devices in microgrids could be used as nodes of a blockchain.  
Digital technology applications, such as blockchains, can facilitate the deployment of microgrids in the developing world and contribute to achieving the 2030 SDG of universal energy access. The future looks promising.
## 1 Introduction  
Wireless sensor networks (WSNs) consist of many hosts called sensor nodes (SNs). A wireless sensor device is a simple autonomous host device. It can sense a phenomenon, convert the sensed information into a form of data, process the data and then transmit the data to a sink or a base-station (BS) for further usage or analysis. The sensor host is very limited in terms of storage, cache memory, processing and computing power, communication capabilities and battery lifetime [1, 2, 3, 4]. There are many different applications adopting sensor nodes. However, this work focus on monitoring and tracking applications where sensor nodes monitor a certain area and track the presence of a certain object of interest such as an animal in the wildlife, a patient or a doctor in a hospital, or a fellow soldier or a vehicle in the battlefield. When the sensor node senses the object, it reports data to the sink (or to multiple sinks) either directly or through other neighboring sensors. One of the most common applications discussed in source location privacy (SLP) literature is the _panda monitoring game_[4, 5]. When a sensor node detects a panda in a certain area, it should report via a message transmitted through intermediate nodes to the sink. In order to protect the panda from hunters or adversaries (ADVs), we need to implement in place an efficient source location privacy scheme (SLP). In such a scenario, location privacy is much more important than the confidentiality of the sensed data itself. Source location privacy is even more important in military, homeland security, and law enforcement, in addition to many other civilian applications [6]. In addition, base-station location privacy (BLP) is very crucial for every WSN since it aggregates all the data.
## 3 Proposed ESM Block-DNN Detector  
In this section, we elucidate the working principle of the proposed detection mechanism. Figure 2 illustrates the framework of the ESM detector when one antenna is activated and sends QPSK signal. Regarding the secondary constellation, the constellation part only needs to be adjusted to two antennas sending the same BPSK0 or BPSK1 signal simultaneously. In order to enhance the precision of the B-DNN detector, we train separate neural networks for each case of ESM-2 TX presented in Table 1. The main parameters are given in Table 2. Comparing with traditional DNN, we use a smaller B-DNN to detect the number of antennas activated by each case and the corresponding constellation symbols. In order to build the B-DNN, three distinct steps are required: data pre-processing, training and prediction.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Tx1** \\ \hline Input nodes & \(2(N_{r}N_{p}+N_{r})\) \\ Learning rate & 0.005 \\ Hidden layer & 3 \\ Number of training set & 15,000,000 \\ Output nodes & M \\ Number of validation set & 5,000,000 \\ Hidden layer activation & ReLu \\ Epoch & 50 \\ Output layer activation & Softmax \\ Loss function & Cross-entropy \\ QPSK hidden nodes & 256-128-64 \\ BPSK0 hidden nodes & 256-128-64 \\ BPSK1 hidden nodes & 256-128-64 \\ Optimizer & SGD \\ \hline \hline \end{tabular}
\end{table}
Table 2: Significant Parameters in DNN.  
Figure 2: Diagram of ESM communication system with DNN signal detection.
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 4 Discussion
### Common Mode Estimation in GNSS
**Funding:** Partial funding for this study was obtained from CNES (Centre National d'Etudes Spatiales). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** We thank all the different agencies, networks, etc., for providing the daily RINEX GNSS data. All the GPS measurements used in this study were obtained from IGN/RGP (Institut Geographique National/Reseau GNSS permanent, [[http://rgp.ign.fr](http://rgp.ign.fr)]([http://rgp.ign.fr](http://rgp.ign.fr)), (accessed on 7 November 2021)), RENAG (Reseau National GNSS, [[http://webrenag.unice.fr](http://webrenag.unice.fr)]([http://webrenag.unice.fr](http://webrenag.unice.fr)), (accessed on 7 November 2021)), SONEL (Systeme d'Observation des variations du Niveau de la mEr a Long terme, [[https://www.sonel.org](https://www.sonel.org)]([https://www.sonel.org](https://www.sonel.org)), (accessed on 7 November 2021)), EUREF (EUREF Permanent GNSS Network, [[https://www.epncb.oma.be](https://www.epncb.oma.be)]([https://www.epncb.oma.be](https://www.epncb.oma.be)), (accessed on 7 November 2021)), RING (Rete Integrata. Nazionale GPS, [[http://ring.gmg.ingit](http://ring.gmg.ingit)]([http://ring.gmg.ingit](http://ring.gmg.ingit)), (accessed on 7 November 2021)), BIGF (British Isles continuous GNSS Facility, [[http://www.bigi.ac.uk](http://www.bigi.ac.uk)]([http://www.bigi.ac.uk](http://www.bigi.ac.uk)), (accessed on 7 November 2021)), CDDIS (Crustal Dynamics Data Information System, [[https://cddis.nasa.gov](https://cddis.nasa.gov)]([https://cddis.nasa.gov](https://cddis.nasa.gov)), (accessed on 7 November 2021)), NOA (National Observatory of Athens, [[http://www.gini.noa.gr](http://www.gini.noa.gr)]([http://www.gini.noa.gr](http://www.gini.noa.gr)), (accessed on 7 November 2021)), NIFE (National Institute for Earth Physics, [[http://gps.infp.ro](http://gps.infp.ro)]([http://gps.infp.ro](http://gps.infp.ro)), (accessed on 7 November 2021)), DGPA (Dutch Permanent GNSS Array, [[http://gnss1.tudelft.nl/dpga/](http://gnss1.tudelft.nl/dpga/)]([http://gnss1.tudelft.nl/dpga/](http://gnss1.tudelft.nl/dpga/)), (accessed on 7 November 2021)), ERGNS (Red Geodesica Nacional de Estaciones de Referencia GNSS, [[https://www.ign.es/](https://www.ign.es/)]([https://www.ign.es/](https://www.ign.es/)), (accessed on 7 November 2021)), and ITACYL (Red de estaciones GNSS de Castilla y Leon, [[http://gnss.itacy.les](http://gnss.itacy.les)]([http://gnss.itacy.les](http://gnss.itacy.les)), (accessed on 7 November 2021)). We also thank anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript. **Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
This is one of the first papers to study the ionospheric effects of two solar eclipses that occurred in South America and Antarctica under geomagnetic activity in different seasons (summer and autumn) and their impact on the equatorial ionization anomaly (EIA). The changes in total electron content (TEC) during the 15 February 2018 and 30 April 2022 partial solar eclipses will be analyzed. The study is based on more than 390 GPS stations, Swarm-A, and DMSP F18 satellite measurements, such as TEC, electron density, and electron temperature. The ionospheric behaviors over the two-fifth days on both sides of each eclipse were used as a reference for estimating TEC changes. Regional TEC maps were created for the analysis. Background TEC levels were significantly higher during the 2022 eclipse than during the 2018 eclipse because ionospheric levels depend on solar index parameters. On the days of the 2018 and 2022 eclipses, the ionospheric enhancement was noticeable due to levels of geomagnetic activity. Although geomagnetic forcing impacted the ionosphere, both eclipses had evident depletions under the penumbra, wherein differential vertical TEC (DVTEC) reached values \(<\)\(-\)40%. The duration of the ionospheric effects persisted after 24 UT. Also, while a noticeable TEC depletion (DVTEC \(\sim\)\(-\)50%) of the southern EIA crest was observed during the 2018 eclipse (hemisphere summer), an evident TEC enhancement (DVTEC \(>\) 30%) at the same crest was seen during the eclipse of 2022 (hemisphere autumn). Swarm-A and DMSP F18 satellite measurements and analysis of other solar eclipses in the sector under quiet conditions supported the ionospheric behavior.  
ionosphere; solar eclipse; global positioning system (GPS); satellite measurements; total electron content (TEC); in situ measurements; equatorial ionization anomaly ()
# Off-Axis Diffractive Optics for Compact Terahertz Detection Setup
## 1 Introduction  
The terahertz radiation spectrum has been at the center of researchers' attention for the last few decades [1] due to its specific features. These features comprise non-invasiveness [2; 3; 4] and non-ionization as well as high absorption and dispersion in water [5] which is a major component of biological tissues. THz radiation is starting to play an increasingly significant role in many biomedical applications [6] including detection of e.g., skin cancer [7; 8]. Our ambition is to design, manufacture and verify thin diffractive optical elements (DOEs) which would allow the development of compact and more applicable THz skin scanners. Using off-axis DOEs is advantageous due to the possibility of sustaining a small size of the focal point while significantly reducing a minimal focusing distance (unlike on-axis diffractive lenses and refractive lenses [9]). In the recent publication [10] we have already presented in-reflection inspection setup with plane wave-to-point \"emitter lens\" and point-to-point \"detector lens\". The whole setup has been designed for the frequency \(\
u=520\) GHz. The choice is connected with the distinguishability of healthy and tumorous skin tissues, which is the best in the 0.35-0.55 THz frequency range [11; 12]. Further work aims at optimization of inspection setup by applying point-to-point lenses on both sides and reducing focal lengths further. In this article the theoretical and experimental comparison of off-axis diffractive structures for the THz spectrumis discussed. For the design wavelength (520 GHz), different 3D printing methods were used to manufacture structures. First, more advanced powder-based technique [13] enabled the creation of simple structures [14; 15], but not all small features of the structure could be properly resolved in the case of higher frequencies. Next, a much simpler and more cost-efficient extrusion-based method and a different optical design have been used. The reduction of the focal length is a key feature to setup minimization. At the same time, the diameter of the structure should remain as large as possible to ensure sufficient signal intensity. These two assumptions lead to the off-axis lenses with small numerical apertures (_NA_ which is also related to \(\frac{f}{d}\)). The shorter the focal lengths, the stronger the influence of the aberrations on the focal spot, especially for the off-axis focusing. These effects can be partially reduced with the proposed iterative design algorithm. Moreover, for the frequency 520 GHz the attenuation of optical materials starts to play significant role. Thus, the application of paraffin is proposed. It has very low attenuation coefficient even up to 1 THz, while most of the 3D printable materials are characterized by higher values of absorption. To be able to manufacture such paraffin lenses, another way of coding phase delay maps has been chosen--high-order kinoform (HOK).
# Predictive Scheduling with Markov Chains and ARIMA Models
## 4 Experimental Verification of the Proposed Solution
### Production Process Modelling and Scheduling  
Before the obtained prediction results could be subjected to further processing, nominal schedules are generated from the real data. Let us assume that the product is manufactured in batches of 50 pieces and the objective function of the schedule is to minimize the makespan (\(C_{\max}\)).  
The schedules are developed using LiSA (A Library of Scheduling Algorithms) software [49], for the analysis of scheduling problems in various environments. The production data serve to represent the production system: a set of machines \(M\) and jobs \(J\), the technology matrix \(MO\) and the matrix of processing times \(PT\). To test the alternative versions of scheduling, the choice of the next operation is determined by two dispatching rules [50]:  
* LPT (longest processing time)
* SPT (shortest processing time)  
The robustness of the production schedules is to be provided by the inclusion of the results from the predictions of failure parameters using the data describing the states of production shifts set \(S\) and predicted repair times \(rt_{t}\). As a result, we have managed to determine the elements from the set of predicted machine failure times \(FT_{Ml}\) and the service time buffer set \(TB_{Ml}\). As noted in the introduction, the data obtained for strategic machines are analyzed from the perspective of executed production processes, hence the discrepancies in the designations in the technology records and the schedule. All the data that serve to generate the robust schedule are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Machine No.**} & \multirow{2}{*}{**ARIMA Model**} & \multicolumn{6}{c}{**Predicted Repair Times [min]**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** & **5** \\ \hline \(M_{1}\) & ARIMA(1,0,0) & 38.77 & 42.11 & 41.90 & 41.91 & 41.91 \\ \(M_{2}\) & ARIMA(0,0,0) & 39.79 & 39.79 & 39.79 & 39.79 & 39.79 \\ \(M_{3}\) & ARIMA(1,1,2) & 36.78 & 40.80 & 40.02 & 40.16 & 40.14 \\ \(M_{6}\) & ARIMA(2,0,1) & 48.12 & 37.57 & 43.20 & 37.78 & 42.13 \\ \(M_{7}\) & ARIMA(1,0,1) & 54.80 & 54.20 & 53.72 & 53.35 & 53.06 \\ \(M_{8}\) & ARIMA(0,0,1) & 51.83 & 49.85 & 49.85 & 49.85 & 49.85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Predicted machine repair times.  
Since it is built on the data above, the obtained schedule is robust to machine failure disturbances. The procedure for generating the robust schedule is rather straightforward: service time buffers \(TB_{Ml}\) are implemented into the nominal schedule in the slots indicated by the set of machine failure times \(FT_{Ml}\). The time-to-failure is counted only for the machines processing jobs (idle time was disregarded). In the case when a service time buffer is required during the operation, any interfering operation was shifted right in the order of jobs.