esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Evaluation of the Data Quality of the Proposed Microservice with Multi-Model Polyglot Persistence
_Consistency_--a microservice with multi-model polyglot persistence provides strong data consistency and uses three methods to ensure consistency: eventual, immediate, and OneShard (highly available, fault-tolerant deployment mode with ACID semantics) while mainframe monolith data persistence only uses an immediate method to ensure consistency. In addition to database supported consistency methods, the business layer of microservice ensures that consumers operate only with consistent data models. Consumers, through REST API, can only manipulate data at the domain level as they are not aware of the database schema details and do not have access rights to access it directly. 2. _Understandability_--a new data model with five collections instead of 35 tables that were used in the mainframe application is simpler and easier to understand. The relations between entities are represented as a graph, which is a great help in improving the readability. The AQL query language used to query polyglot persistence is considered as a human-readable query language and increases understandability compared to the SQL query language used in mainframe application. 3. _Availability_--the biggest advantage of microservice with polyglot persistence in terms of availability is that it supports many resilient deployment modes to meet the different needs of a different project. Active failover deployment is used for smaller projects with fast asynchronous replication from the leading node to passive replicas. OneShard deployment is used for multi-node clusters with synchronous replication from the leading node it provides. A synchronously-replicating cluster technology allows it to scale elastically with the applications and all data models. The last but not least feature of multi-model polyglot persistence is the support for datacenter to datacenter replication. 4. _Portability_--while the mainframe requires a very specific infrastructure to run an application, a microservice with multi-model polyglot persistence can be installed on all main operating systems (Linux, Windows and macOS) and can be deployed to a private or public cloud. It can be summarized that by using the proposed migration approach, it is possible to execute the migration from the monolith mainframe persistence model to the multi-model polyglot persistence model without losing data quality. Eleven of fifteen ISO/IEC 25012:2008 standard quality attributes were the same for both models and four were even better for the multi-model polyglot persistence model. It must also be noted that the results could be different for different monolith applications.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 2 One Way Unclonable Functions with Ternary States
### One Way Unclonable Functions
#### 2.1.2 Individual Digital Access Instructions (_IDAceress_):  
* The individual digital access _IDAceress_ is used to retrieve the set of instructions \"I\" needed to generate **K** from the set of addresses **A**. To enhance security, **I** can be XORed with the message digest \"MD\" of the XOF as shown in Equation (3): \[\text{I}\leftarrow\text{Hash ({IDAceress} \oplus\text{ MD})}\] (3)
* With this protection, _IDAceress_ can be freely disclosed through insecure communication channels.
* The set of instructions **I** can incorporate is a ternary representation that reduces the bit error rates (BER) of the output stream **K**, and it can offer additional protection. When an opponent tests the one-way function without knowing the position of the ternary states, \(\overline{\textbf{\tau}}\) generates streams with high BER and potentially damages the structure permanently.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Tool Comparison
#### 5.2.13. Great Expectations  
Great Expectations [71] is a tool for data validation and data profiling. It can be easily plugged and extended with many frameworks. It offers integration support for many data sources including MySQL, SQLAlchemy, and others. The main purpose of this tool is to do data quality assessment through automated testing, but it is limited to tabular data only.
## 3 Results and Discussion  
Structural and Morphological Properties of Fe\({}_{65}\)Ni\({}_{35}\) and Fe\({}_{65}\)Mn\({}_{35}\) Alloys  
The room temperature XRD patterns for Fe\({}_{65}\)Ni\({}_{35}\) and Fe\({}_{65}\)Mn\({}_{35}\) are displayed in Figure 1a,b, respectively. Five Braggs peaks (111), (110), (200), (200), and (220) are observed in the diffraction pattern. Among the above peaks, two are corresponding to the \(\alpha\)-phase and three denote the \(\gamma\)-phase of large crystals of Fe-Ni and Fe-Mn alloys, as confirmed by the literature report [26; 39] and JCPDS data (as describes in Figure 1). It can be inferred that both alloys crystallize in the cubic structure with two different space groups: Fm-3m (\(\gamma\)-phase, fcc structure) and Im-3m (\(\alpha\)-phase, bcc structure). In both compounds, two structural phases coexist, which are confirmed by the presence of different diffraction planes (as identified in Figure 1). The formation of the \(\gamma\)-phase in both compounds is promoted by the diffusion of Ni/Mn into the Fe site during the ball milling. The diffraction peak (110) has a higher intensity than the (111) peak, which means the \(\alpha\)-phase is dominated as compared to the \(\gamma\)-phase; such behavior commonly occurs in as-prepared Fe-based alloys [26; 36; 40]. From the phase diagram study, generally Fe-Ni based systems have contained an \(\alpha\)-phase up to 30% Ni composition and from the \(\alpha+\gamma\), mixed phase to the \(\gamma\) phase as the Ni composition increased [39; 43; 44; 45; 46]. Similar behavior also occurred in the Fe-Mn compound.  
The powder morphology of the Fe\({}_{65}\)Ni\({}_{35}\) and Fe\({}_{65}\)Mn\({}_{35}\) alloys are studied by HR-FE-SEM. Figure 2a,b displays the morphological micrograph of both the studied alloys. The as-milled alloy evidently shows the irregular distribution of the bulk alloys, which may arise due to the presence of two crystallographic phases and/or the presence of different Fe-Ni and Fe-Mn clusters. These results are analogous to the XRD results where two crystallographic phases coexist.
# Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations
## 3 Methods
### Training  
**Scheduled Sampling.** RNNs are difficult to train, because early mistakes in the sequence prediction can be accumulated over the remaining prediction process. The issue is studied in Scheduled Sampling [53] which proposes to _randomly_ sample either the current prediction or the ground-truth as input to the model during training. This makes the model robust to prediction mistakes during inference, as the model is gradually trained to correct such mistakes [36].  
We use Scheduled Sampling to train HRPAE model as follows. We initialize the sampling probability to 0, and gradually increase the probability by constant step \(\delta\) after each iteration during training. In the prediction process of Figure 1b, we randomly select either the ground-truth or generated video frames according to the probability, and feed it as input to HRPAE. As a result, HRPAE gradually learns how to recover from early mistakes in predictions with the increasing sampling probability, and is able to generate robust predictions during testing.  
**Loss function.** We not only want to generate realistic frames, but also wish the generated frames to be continuous and capture the motion of objects fluently. To that end, we minimize the Mean Squared Error (MSE) reconstruction loss denoted by \(L_{2}\) to enforce the generated frames to be close to the ground truth frames in the \(L_{2}\) sense:  
\[L_{2}(G)=\frac{1}{2}\sum_{i=2}^{T+K}\|\mathbf{x}_{i}-\hat{\mathbf{x}}_{i}\|^{2} \tag{1}\]  
where we sum up the pixel-wise \(L_{2}\) frame loss over the predicted frames. Experimental results show that by training HRPAE with respect to Equation (1), our model can generate coherent video frames without an additional loss regularizer. The experimental results show that, by using the MSE loss, our model is able to generate sharp and coherent frames.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 3 Connected Vehicles and Data Availability
### Vehicular Data Representation  
Vehicles are getting more and more intelligent. An average car today contains more than 20,000 components, including about 40 microprocessors and an important set of embedded sensors that can number up to 200 sensors per vehicle in 2020 [25]. Modern vehicles hinge on these considerable sets of sensors in order to generate and exchange vehicle motion and status data. Thereby, in a connected vehicle scenario, a rich data source is the vehicle itself. Nevertheless, most data that are generated  
Figure 2: Connected Vehicles applications.  
by a vehicle are primarily of a technical nature; differ from carmaker to carmaker, and even within carmakers, from model to model.  
When considering the connected vehicles' technology, which aims at sharing some of these data with third parties, a variety of data representation known as messages sets have been proposed to support interoperability and enable data exchanges among connected vehicles network. The Society of Automotive Engineers (SAE) has developed the J2735 standard, which specifies a Message Set Dictionary, explicitly to support interoperability among applications based on the DSRC [26]. The SAE standard J2735 defines approximately 150 standard data elements and 70 standard data frames and describes 15 types of application data messages sets listed in Table 1.  
A message is a combination between two structures named data frame and data element. The data frame is a complex data structure that contains one or more data elements and even other data frames. As stated in [27], among the fifteen messages described in the J2735, the BSM is considered to be the more important.
## 2 Materials and Methods
### Sample Preparation  
To standardize the sample, the teeth underwent sectioning at the cementoenamel junction, perpendicular to the long axis of the tooth. This was achieved using a diamond cylindrical bur (Coltene/Whaledent AG, Altistatten, Switzerland) mounted in a turbine and cooled with water. The result of this process was the acquisition of root segments, each 17 mm in length. In cases where the pulp chamber was not exposed, an access cavity was created using a spherical bur mounted in a turbine to gain access to the canal.  
In order to simulate the characteristics of an immature tooth, the apical portion (2 mm) of the root was removed using a cylindrical diamond bur (Coltene/Whaledent AG, Altistatten, Switzerland) mounted in a turbine. Subsequently, both the coronal and root surfaces of the segments were polished with a carborundum disc (Dentorium Products Co., Inc., Farmingdale, NY, USA), resulting in segments with a final length of 15 mm.  
The chemical-mechanical preparation of the root canals commenced with the initial exploration and permeabilization using manual ISO k15 files measuring 25 mm (DentsplyMaillefer, Ballaigues, Switzerland). Establishing a working length of 15 mm involved inserting a manual ISO #10 K-file ([PERSON], Ballaigues, Switzerland) until it became visible at the apical foramen. Orthgrade mechanical canal preparation began with manual instrumentation with K-files #10, #15, #20 and #25 ([PERSON], Ballaigues, Switzerland). Subsequently, mechanized instrumentation was carried out using the SX file from the Protaper Next system ([PERSON], Ballaigues, Switzerland) up to the established working length of 15 mm. This process was executed at a constant speed of 250 rpm and a torque control of 1.2 N/cm, using an X-SMARTTM electric motor ([PERSON], Ballaigues, Switzerland). Between each instrument, irrigation was conducted using 2.5% sodium hypochlorite with a 27G closed-end side-vented needle.  
To replicate an open apex, a retrograde divergent preparation of the canal was carried out, involving the apical foramen of each tooth. This was achieved using the mechanized ProFile Orifice Shaper(r) files (Dentsply Maillefer, Ballaigues, Switzerland) OS#1 (06/20), OS#2 (06/25) and OS#3 (06/30) sequentially introduced 5 mm apically with the X-SMARTTM electric motor. The instrumentation speed was set at 250 rpm and torque control at 1.2 N/cm. The irrigation protocol mirrored that recommended during orthgrade instrumentation.  
The final irrigation protocol involved using 1 mL of 17% ethylenediaminetetraacetic acid (EDTA) (Magnum Dental AS, Tartu, Estonia) for 1 min, followed by neutralization with 2 mL of 0.9% saline solution (NaCl). The canals were then dried with paper points (Dentsply, Johnson City, TN, USA), and the integrity of the canal was assessed through an optical microscope (Leica(r) M320, Wetzlar, Germany).
# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 2 Literature Review
A network where information enters the LSTM can be judged by rules. Only the information that accords with the authentication will be left behind, and the discrepant information is forgotten by the forget gate [61]. In addition to ANNs, many other approaches are frequently used in weather forecasting, such as multiple regression, SVM, decision trees, and the k-Nearest Neighbor model. The disadvantage of MLP and deep learning models over other methods is that they are time-consuming during the training process. Although SVM requires intensive training and experimentation on different kernel functions as well as other parameters, it is significantly faster compared to MLP and deep learning models [62]. The SVM algorithm was developed by [PERSON] and is based on statistical learning theory [63]. SVM includes efficient algorithms for a wide range of regression problems because they not only take into account the approximation of the error to the data but also provide a generalization of the model--that is, its ability to improve the prediction of the data when a new data set is evaluated by it [39]. ANNs, including Multilayer Perceptron, deep ANNs, and other machine learning models, are constantly being improved and widely used for the forecasting of air temperature [64], rainfall [36,37,65], cloudiness [66], and wind speed [67], which proves that these are forward-looking models that are worthy of constant research and improvement for forecasting purposes. To summarize, the most frequently forecasted condition in the above works was temperature; several studies have similarly found the use of models for wind speed prediction, and the most frequently used machine learning model for this purpose was a multilayer Artificial Neural Network. Other models chosen by researchers include  
* Support Vector Machines and
* Linear Regression. The most commonly used learning algorithm for neural networks is the backpropagation algorithm [2]. Datasets are usually large and contain data from many previous years--optimally 10 years. Before starting the training and prediction, the data are normalized. The authors typically verify the accuracy of the model predictions using measures, such as MAE and MSE. The research results presented in the analyzed papers motivated us to implement a Multilayer Perceptron as the prognostic model and to investigate its effectiveness using, among others, MAE and the Mean Squared Error (MSE) as measures of the model effective ness. As the network learning algorithm, the backpropagation algorithm was chosen. We decided that the dataset would contain data from 10 years. the maximum and minimum temperatures, atmospheric pressure, wind speed, and daily precipitation for the next day were planned as the forecast conditions.
## 3 Results and Discussion
### Results of E-Nose  
In order to gain a better understanding of the differences in the response signals of Liupao tea, processed using various fermentation methods, to different odors, a radar map was created using the average stable values of the response signals of 10 sensors (Figure 1b). The results indicated that the odor fingerprints of Liupao tea varied depending on the processing method employed. Specifically, sensors R2, R6, R7, and R8 showed higher response values. Additionally, the response values differed among Liupao tea samples processed with different techniques, with sensors R6, R7, and R8 exhibiting significant differences. Conversely, sensors R1, R2, R3, R4, R5, R9, and R10 demonstrated minimal variations, indicating that these sensors were not sensitive to the odor of the four Liupao tea samples. Overall, all four sample groups exhibited peak response values at sensor R7, indicating a prominent sulfide odor in the samples. A previous study on the modeling of the \"stale\" aroma of Liupao tea using an E-nose found that sensors W1S (methyls), W1W (inorganic sulfides), and W2S (alcohols, ketones) contributed more to the response of aged Liupao tea, which is consistent with the results of this study [35].
# Effects of Propanol on the Performance and Emissions of a Dual-Fuel Industrial Diesel Engine
## 3 Results and Discussion
### Emission Characteristics
#### 3.3.2 Hydrocarbons  
Unburned hydrocarbons are the product of the incomplete combustion of the fuel in the engine cylinder where not all the fuel particles are involved in the oxidation reaction with limited contact with oxygen. Increased HC emissions are favored by local oxygen deficiencies in the combustion chamber during the combustion of a rich mixture. The places where the accumulated fuel may remain unoxidized are the piston ring gaps and the boundary layer zones. Hydrocarbons are also formed in large amounts when a very lean mixture is burned, due to the prolonged oxidation process caused by the slow combustion rate. Figure 9 shows the changes in the concentration of hydrocarbons in the exhaust gas of the test engine powered by diesel and propanol, compared to the engine powered by D100. It is evident that the co-combustion of diesel fuel with propanol, up to 50%, contributed to the decrease in HC emissions. For DP15, DP30, and DP50, the decrease was 56 ppm (26.5%), 47 (22.3%) ppm, and 39 ppm (18.5%), respectively. The increase in alcohol content caused increasing amounts of fuel injected into the intake manifold, where it formed a homogeneous alcohol-air mixture, and then delivered it to the cylinder during the intake stroke. In this case, the amount of diesel directly injected into the cylinder decreased, which no longer accumulated as much in the gaps and on the walls of the combustion chamber, and had a limited possibility of creating fuel-rich zones. This led to a reduction in the number of hydrocarbons produced in the test engine cylinder. In addition, supplying the cylinder with alcohol fuel delayed auto-ignition, extending the time to create a homogeneous mixture in the engine cylinder. It also reduced the fuel-rich areas that emit unburned hydrocarbons. A sharp increase in HC concentration in the exhaust gas was recorded for the highest alcohol content (DP65), which amounted to 161 ppm (76.3%). It was caused by a decrease in combustion temperature and heat release rate in the engine cylinder due to a significant ignition delay.
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 2 HV-PLC Channel
### PLC-Channel Frequency Response  
Power systems 1 and 2 of Figure 1 are included in the PLC-channel analysis by means of their one-port nodal representation through a frequency-dependent matrix \(\mathbf{Y}_{BUS}\):  
\[\mathbf{I}_{1}=\mathbf{Y}_{BUS_{1}}\mathbf{V}_{1}\qquad\text{and}\qquad \mathbf{I}_{2}=\mathbf{Y}_{BUS_{2}}\mathbf{V}_{2}\]  
Further inclusion in (12) of coupling equipment yields the relation between voltages \(v_{s_{1}}\) and \(v_{s_{2}}\) at the communication equipment 1 and 2 in Figure 1:  
\[v_{s_{2}}=Hv_{s_{1}} \tag{13}\]  
where \(H\) is a scalar transfer function. Figure 4 provides typical diagrams for the PLC coupling equipment being considered here: the wave trap and the line tuners. For details on the inclusion in (12) of the coupling equipment the reader is referred to [14]. Supposing \(v_{s_{1}}\) is the transmitted signal and \(v_{s_{2}}\) the received one, the PLC-channel attenuation is:  
\[A=20 log_{10}\left|\frac{v_{s_{1}}}{v_{s_{2}}}\right|=-20\log_{10}\left|H\right| \tag{14}\]  
Mention is made here that the attenuation given by (14) is frequency dependent and that its variations are due to the multiconductor line propagation features, to the choice of the line coupling scheme and to the values of the coupling-device elements.  
Figure 4: Coupling equipment. (**a**) Wave trap. (**b**) Receiving-end line tuner. (**c**) Transmitting-end line tuner.  
The coupling schemes at both sides of the power line depicted in Figure 1 involve two line conductors. This is referred to as a phase-to-phase coupling [1]. In practice, the coupling is often made to one conductor only and to the ground, and this is called a phase-to-ground coupling [1]. When designing a PLC system, one has to select the best coupling scheme. On a three-phase line, there are six possible coupling schemes for the phase-to-ground modality, as well as six other alternatives for the phase-to-phase coupling configuration.  
One last remark about PLC coupling is that PLC signals travel in multiple line modes. There are \(n\) modes for an \(n\)-conductor line. Certain couplings present the problem known as modal cancellation [14] consisting of two modes arriving with opposite phase angles and canceling each other, with the consequent loss of the communication signal. The proper design of a PLC system involves the selection of a coupling scheme immune to modal cancellation.