esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Use Cases and Requirements
### Parliament Use Case  
Parliament libraries provide Members of Parliament (MP) and their assistants, as well as journalists, political analysts and researchers, information and documentation regarding parliamentary issues. Besides traditional publications, the Web and the Social Web play an increasingly important role as an information source, since they provide important and crucial background information, like reactions to political events and comments made by the general public. It is in the interest of the parliaments to create a platform for preserving, managing, mining and analyzing all of the information provided in social media.  
Through ARCOMEM, the Greek and Austrian parliaments aspire to transform their flat digital content archives into historical and community memories. In particular, one of the selected case studies concerns the Greek financial crisis. ARCOMEM opens the road for answering questions like:  
* What is the public opinion on crucial social events?
* How has the public opinion on a key person evolved?
* Who are the opinion leaders?
* What is their impact and influence?  
The parliament use case exhibits notable differences compared to the broadcaster use case. First, parliaments have multimedia archives with content partly produced by the parliamentary procedures. The focus is on associating this information with the events and entities involved and subsequently enriching it with relevant user content from the Social Web. Second, crawls may last longer than in the broadcaster case. Political events may have a long aftermath, in contrast to news stories, which are usually more temporally focused. Another difference is that a broad range of people use the parliament archives and may have varying requirements when retrieving information, making it difficult to cover everybody's needs.
## 3 Results and Discussion
### Mixed Time Partitioning Methods vs. Sub-Cycling and Partitioning Methods  
We study the total run time required to simulate the solidification and cooling of the casting over 450 s. In our analysis, we consider four cases of numerical simulation:  
1. traditional methods--when the same time integration schemes and the same time steps are used in each sub-domains (Table 3),
2. sub-cycles methods--when the same time integration schemes and the different time steps are used (Table 4),
3. partitioning methods--when the different time integration schemes and the same time steps are used (Table 5),
4. mixed time partitioning methods--when the different time integration schemes and the different time steps are used (Table 6).  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Symbol** & **Description** & \(\mathbf{t}\)**[s]** \\ \hline \(I-I^{m}\) & Implicit in cast domain \(-\) Implicit in other domains & 3841.51 \\ \(E-E^{m}\) & Explicit in cast domain \(-\) Explicit in other domains & 2340.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A total time \(t\) of computations obtained for the traditional methods (\(m=1\)). The same integration methods and the same time step.  
Figure 7: Temperature field obtained for selected point in casting domain after 136.5 s of solidification process (red color denotes the highest temperature and yellow color denotes the lowest temperature in the cast). Four combination of the time integration schemes (**A**) \(I-I^{m}\), (**B**) \(E-E^{m}\), (**C**) \(E-I^{m}\), (**D**) \(I-E^{m}\) for multiplication factor \(m=1\).  
We have gathered data for four cases listed in Table 1. We measured run time includes the assembly of the system, the applying boundary conditions and solving the system of equations. We report \(E-E\) combination to be the fastest while \(I-I\) to be the slowest case (because of many iterations per one time step). The major difference comes from the protocol the system of equation is assembled and solved. In \(I-I^{m}\) case, both the boundary condition and solving system of equations are iterative processes and require more time to be completed. We note that in the above cases the multiplication factor \(m=1\) and the mixed time partitioning methods are very similar to the conventional methods. However, we decided to include these results to establish the baseline for sub-cycles time steppers, which we discuss in the next subsection.
## 1 Introduction
### 3D-Printed Sockets  
Three-dimensionally printed prosthetics provide an alternative for users who cannot afford conventional prosthetics or their associated costs [13]. Many initiatives, such as the e-NABLE project [14], embrace open-source principles to conceive low-cost 3D-printed upper limb prostheses. Three-dimensionally printed prosthetics allow for a high degree of customization, fast and efficient manufacturing, design flexibility and a wide quantity of materials with different properties. Regarding materials, organizations dedicated to making 3D-printed prosthetics explicitly state that their designs should not be considered fully functional prosthetic devices [15]. Instead, they should be considered as tools that assist in simple tasks to prevent prosthetic uses that could lead to material failure. Catastrophic failures of 3D-printed lower-limb sockets have been documented [16], and standardized safety and durability tests are available [17]. In contrast, no such standard exists for upper limb sockets [18], which makes it difficult to introduce new materials for these prosthetics.  
Another challenge of 3D-printed upper limb prosthetics is the design of the socket part, which is typically conceived as a modular design of an adjustable socket using straps or a BOA(r) dial tensiometer (BOA Technology Inc., Denver, CO, USA) to fit all users. However, traditional adjustable sockets do not allow the applied tension to be controlled.  
In order to overcome this situation, recent studies have proposed and assessed the manufacturing of personalized upper limb sockets using 3D printing and 3D scanning techniques [19; 20; 21; 22]. These works aim to improve stump morphology measurements to propose 3D-printed socket designs that fit users' anatomies and converge full 3D designs and manufacturing. Three-dimensional scanners allow the volume and geometry of users' residual limbs to be accurately measured [23] toward a more reproducible and less invasive customization process than the traditional casting process. Nevertheless, as the tissue of an amputation-generated stump may undergo a loss of compactness compared to a stump that results from hand agenesis, it may be necessary to apply a certain compression to the stump before performing 3D scanning.  
Traditional socket manufacturing mainly involves molding, shaping and lamination, which require a lot of materials, time and manual labor. Moreover, the obtained socket fit commonly requires several modifications [13]. This entire manufacturing and adjustment process might not be affordable, particularly in low-income countries or in pediatric prosthetics, where frequent adjustments may be required. In this regard, 3D scanning offers a high potential for accurate morphology measurements, and it is especially interesting inthe 3D-printed prosthetics realm as it facilitates a full 3D process with a very high potential for traditional manufacturing methods.
# UAV Image Multi-Labeling with Data-Efficient Transformers
## 4 Experimental Results
### Evaluation Metrics  
To quantitatively validate the proposed methodology and compare our results to other state-of-the-art methods, we calculated the following metrics: specificity (Sp), recall (Re), precision (Pr), average (Avg), F1-score (F1), F2-score (F2), mean average precision (mAP), ranking loss (RL), and Hamming loss (HL). The definitions of these metrics are given as below:  
* \(Specificity=\frac{TN}{TN+FP}\), \(Recall=\frac{TP}{TP+FN}\), \(Precision=\frac{TP}{TP+FP}\), where TP, FP, TN, FN are the number of true positives, false positives, true negatives, and false negatives;
* Average (Avg): is the average of specificity and recall;
* \(Fscore=\frac{\left(1+\beta^{2}\right)\left(Precision\times Recall\right)}{ \beta^{2}\left(Precision+Recall\right)}\), where \(\beta\) has the value of 1 for F1-score and 2 for the F2-score;
* Mean average precision (mAP): is the mean of average precisions for each class;
* Ranking loss (RL): is the average number of incorrectly ordered pairs of labels;
* Hamming loss (HL) is the fraction of incorrectly predicted labels to the total number of labels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **No. of** & **No. of** & **Spatial** & **Spectral** & **Image Size** \\  & **Classes** & **Images** & **Resolution (cm)** & **Resolution** & **Image Size** \\ \hline Trento & 13 & 3415 & 2 & RGB & 224 \(\times\) 224 \\ Civezzano & 14 & 3415 & 2 & RGB & 224 \(\times\) 224 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about multi-label UAV datasets.  
Figure 3: Sample images. (**a**) Trento and (**b**) Civezzano multi-label datasets (green color indicates the presence of the class while red color indicates the absence of the class in the image).
## 4 Results and Discussion  
### Experimental Results and Training Data Preprocessing  
As Figure 2(a) shows, the 12 N.m assembly torque group has the best performance over the entire load current range, indicating the optimized assembly torque of the presented fuel cell stack. At load currents lower than 550 A, the 14 N.m assembly torque group performs similarly to the 15 N.m assembly torque group. However, the voltages of the 14 N.m group show the lowest values while the load current is higher than 550 A. This deviation keeps increasing with the ascending load current.  
From the results presented in Figure 3b, we can conclude that the air pressure difference grows larger if the assembly torque is higher. This difference is also affected by the load current: the difference is bordered while the load current is increased. The gas consumption increases as the load current grows under the given reactants' stoichiometric ratios.  
The cell voltage varies under different assembling torques of 12 N.m, 14 N.m, and 15 N.m, with the load current increasing at the step of a constant 0.1 A/cm\({}^{2}\). The performance in the eighth cell is shown in Figure 4. The load current decreases from 1.8 A/cm\({}^{2}\) to 0.5 A/cm\({}^{2}\) while the cell voltage increases from \(\sim\)0.56 V to \(\sim\)0.78 V under the assembling torques of 12 N.m and 14 N.m and from \(\sim\)0.6 V to \(\sim\)0.81 V at 15 N.m. For the training samples, the cell voltage experiment data of the dynamic overshooting process during the load current descending are removed because the data deviate significantly from the steady-state results.  
The experiment results are divided into three data sets: 70% are the data-for-training set, 15% are the validation set, and 15% are the test set. The training set is used to train the network model, the validation set is used to validate the network parameters, and the test set is used to test the prediction performance of the model. The model performance is considered based on the parameters of \(R^{2}\), \(MSE\), and _training time_, as shown in Table 2. The computer, for the model computation using the MATLAB neural network toolbox, has the specifications of an Intel(r) Core(tm) [EMAIL_ADDRESS] GHz processor and a single-channel 8 GB@2133 MHz memory.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ANN Structure** & \(\mathbf{R^{2}}\) & _MSE_ & _Training Time_ \\ \hline D1 & 6-8-5-5 & 0.99992 & 5.2523 \(\times\) 10\({}^{-7}\) & 6 s \\ D2 & 6-8-5-10 & 0.99992 & 5.9548 \(\times\) 10\({}^{-7}\) & 23 s \\ D3 & 6-8-5-20 & 0.99992 & 6.8463 \(\times\) 10\({}^{-7}\) & 17 s \\ D4 & 6-8-5-50 & 0.99991 & 6.3335 \(\times\) 10\({}^{-7}\) & 302 s \\ D5 & 6-8-5-100 & 0.99990 & 7.6788 \(\times\) 10\({}^{-7}\) & 977 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(MSE\), \(R^{2}\), _Training Time_, and the ANN structure in each group.  
Figure 3: (a) Curve of the relation between the load current and average cell voltage; (b) the current- and air-pressure-difference curve.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Definitions of Key Terms
Examples of incentive-based programs may include direct load control (DLC) and interruptible programs that allow a utility to control customer demand or limit access to electricity during periods of high grid stress (e.g., heat waves). Examples of price-based DR include time-of-use (TOU) pricing, critical peak pricing (CPP), or real-time pricing (RTP), which are utility tariffs that encourage customers to voluntarily decrease demand during high energy prices that are typically associated with peak hours. The key difference between the two types of DR programs is that incentive-based programs involve infrequent service interruptions, while price-based programs enable voluntary, ongoing load shaping to support the grid. Other notable terms defined throughout the literature include flexible loads [43], flexumer [44], building clusters [35], demand-side services [43], and drivers of price responsiveness [45]. These are not broadly used terms and have been used in specific applications. It should be noted that many papers did not define any terms and instead, cited the benefits of and applications for DR or other forms of DSM without presenting any definitions.
# Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
## 2 Review of the Algorithmic Concepts
### Singular Value Decomposition  
Singular value decomposition (SVD) [10] based on the linear algebra concept is a diagonalization of a rectangular matrix, and it is a very useful and powerful multimediatool, particularly in the contexts of data analysis, satellite data, image dimension reduction, etc. It transforms correlated variables into a collection of uncorrelated variables to reveal the relationships in the original data more clearly. Several SVD-based watermarking schemes have been proposed due to the stability of singular values, which may withstand modest image processing disruptions [10,15]. A rectangular matrix '\(A\)' of order \(m\)\(\times\)\(m\) can be partitioned into the product of three matrices, namely orthogonal matrix U, diagonal matrix S, and the transpose of orthogonal matrix V, in accordance with the theory. It can be theoretically represented by its component matrices as follows:  
\[A=\begin{bmatrix}a_{1,1}&a_{1,2}&\ldots&a_{1,m}\\ a_{2,1}&a_{2,2}&\ldots\\ \vdots&&\ddots&\\ a_{m,1}&&&a_{m,m}\end{bmatrix}=US\,V^{T}=\begin{bmatrix}u_{1,1}&u_{1,2}& \ldots&u_{1,m}\\ u_{2,1}&u_{2,2}&\ldots\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&0&\sigma_{m}\end{bmatrix}\times\begin{bmatrix}v_{1,1}&v_{1,2}&\ldots&v_{1, m}\\ v_{2,1}&v_{2,2}&\ldots\\ \vdots&\ddots&\\ v_{m,1}&&&v_{m,m}\end{bmatrix}^{T} \tag{1}\]  
where \(UUU^{T}=I_{m}\) and \(VV^{T}=I_{m}\), and \(I_{m}\) is the identity matrix of order \(m\). The elements of the diagonal matrix S are in decreasing order, satisfying Relation (2), where \(r\) is the rank of the matrix.  
\[\sigma_{1}\geq\sigma_{2}\geq\ldots\geq\sigma_{r}>\sigma_{r+1}=\sigma_{r+2} \ldots=\sigma_{m}=0 \tag{2}\]
# Effect of Bioaugmentation on Biogas Yields and Kinetics in Anaerobic Digestion of Sewage Sludge
## 2 Materials and Methods  
### Material Characteristics  
Sewage sludge that included two-source residues (originated from primary and secondary clarifiers) were obtained from the Pulawy municipal wastewater treatment plant (WWTP), Poland. Sludge was sampled once a week in the WWTP. Under laboratory conditions, the sludge was mixed at the volume ratio of 60:40 (primary: waste sludge), then homogenized, manually screened through a 3 mm screen and partitioned. The sludge samples were stored at 4 \({}^{\circ}\)C in a laboratory fridge for no longer than one week. Sludge prepared in this manner was fed to the digester as mixed sewage sludge (SS). The main characteristics of SS during the entire cycle of experiments was as follows (the average value and standard deviation are given): chemical oxygen demand (COD)--43.00 \(\pm\) 5.49 g dm\({}^{-3}\), volatile fatty acids (VFA)--1179 \(\pm\) 733 mg dm\({}^{-3}\), total solids (TS)--37.0 \(\pm\) 2.9 g kg\({}^{-1}\), volatile solids (VS)--28.6 \(\pm\) 2.11 g kg\({}^{-1}\), pH--6.75 \(\pm\) 0.33, and alkalinity--846 \(\pm\) 268 mg CaCO\({}_{3}\) dm\({}^{-3}\).  
A liquid solution containing a mixture of Bacteria and Archaea was prepared in a continuous mode throughout the experiment. A nylon pouch filled with a powdery substrate (ArcheaSolutions Inc., Evansville, IN, USA) was placed inside a generator. The microbial composition of the powdery substrate after 1 day of cultivation at 37 \({}^{\circ}\)C under constant mixing conditions in distilled water showed that about 36% of microorganisms belonged to Archaea with _Methanosaeta_ as s predominant genus and that about 59% of microorganisms belonged to Bacteria with _Exigubobacterium_, _Janthinobacterium_, _Acinetobacter_, and _Stenotrophomonas_ as the most abundant genera (Figure 1).  
The analysis of the substrate was performed as described in _Molecular analysis_, sequences were placed in NCBI (BioProject PRJNA431048). The substrate was packed in a vinyl alcohol coating, which dissolved upon contact with dechorlariated pipe water flowing through the device. The release of an appropriate microbial content of 1.08 g L\({}^{-1}\) h \({}^{-1}\) required a continuous flow of water at a level of about 0.5 L min\({}^{-1}\). After 30 days, the pouch containing a mixture of microorganisms was replaced by a new one. The generator was linked to two serially-connected storage tanks with a total active volume of 320 L. The liquor prepared in this manner was stored at room temperature. The average total solids (TS) of the liquor differed slightly during the experiments and reached 0.48 and 0.45 g kg\({}^{-1}\) in phase 1 and phase 2, respectively, while the average volatile solids (VS) oscillated around 0.04 g kg\({}^{-1}\). The COD values were 22 g\(\cdot\)m\({}^{-3}\) in phase 1 and 26 g\(\cdot\)m\({}^{-3}\) in phase 2. Similarly, the VFA concentrations were 21 and 24 g\(\cdot\)m\({}^{-3}\), respectively. The alkalinity of 330 g CaCO\({}_{3}\cdot\)m\({}^{-3}\) and pH value of 7.16 were obtained for both phases.
# Reinforcement-Learning-Based Trajectory Learning in Frenet Frame for Autonomous Driving
## 6 Conclusions  
In this paper, we proposed a reinforcement-learning-based method for learning trajectories for obstacle avoidance on curved roads with continuous obstacles. Our method overcomes the limitations of modeling real-world kinematics and distorting object shapes when generating trajectories in the Frenet frame and demonstrated a 100% success rate in the trained environment. RLTF has the limitation of requiring a reference line. However, since RLTF has demonstrated significant success in most cases, we believe it can be applied to driving in various urban environments, such as narrow city streets, when collision prevention is applied along the output trajectory.  
conceptualization, K.L. and S.C.; methodology, K.L.; software, S.Y. and J.R.; validation, S.Y. and J.R.; formal analysis, S.Y. and J.R.; investigation, S.Y., Y.K. and J.R.; resources, K.L.; data curation, S.Y., Y.K. and J.R.; writing--original draft preparation, S.Y. and K.L.; writing--review and editing, S.K. and K.L.; project administration, S.C. and K.L.; funding acquisition, S.C. and K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Autonomous Driving Center, R&D Division, Hyundai Motor Company and the Chung-Ang University Research Grants in 2023.  
Not applicable.  
The data presented in this study are not available due to privacy.  
[PERSON] has received research grants from Hyundai Motor Company. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
IT-MPC-NNInformation-theoretic model predictive control with neural network  
DDPGDeep deterministic policy gradient  
DDQNDouble deep Qnetwork  
MPPIModel predictive path integral  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **RLTF** & **IT-MPC-NN**[29] & **Supervised BC** & **TD3+BC** \\ \hline Average reward & 410.2 & 400.3 & 331.48 & 220.5 \\ Average success rate & 99.6 & 95 & 91 & 79 \\ Average speed & 19.32 & 16.73 & 15.07 & 14.51 \\ Average deviation & 0.01 & 0.10 & 0.22 & 0.16 \\ Average throttle value & 0.15 & 0.13 & 0.11 & 0.11 \\ Average steering angle & 0.34 & 0.36 & 0.20 & 0.18 \\ Average acceleration & 0.85 & 0.71 & 0.46 & 0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Driving results on unseen roads.  
Figure 9: Simulation driving trajectory results of RLTF and TD3+BC.
# Smart Electric Three-Wheeled Unit for the Manufacturing Industry
## 4 Discussion  
_Testing the Tricycle in the Operation of the Manufacturing System_  
The electric tricycle was tested in the real operation of the manufacturing system at the Technical University in Kosice (Figure 20) for the transportation of semi-finished products from storage spaces directly into the production process. Smart devices were tested, such as tracking transportation routes using GPS technology, transmitting camera footage within a shared image, for example, during the repair or maintenance of a machine. Through the built-in tablet and Bluetooth technology, information about the inventories of transported material and their technical information were shared.  
During the testing of the smart electric tricycle in a manufacturing environment, data were collected and analysed to confirm its usability and its proposed technical specifications. The data tested included measurements of the 750 W motor's performance, encompassing the maximum power, torque, and motor efficiency under various loads. These measurements were conducted under standard operating temperatures and conditions to determine the motor's reliability and the degree of overheating. The battery endurance was tested under different load levels, with and without a trailer, where the number of kilometres travelled was within the estimated tolerances. The transmission of live camera footage and the connectivity of the tablet with attached devices such as cameras, safety, and strain sensors were also monitored.  
Figure 20: Testing the tricycle at the Technical University of Kosice.  
Strain tensometric sensors for the transported material were tested (Figure 21). The tricycle demonstrated very good manoeuvring capabilities, such as a small turning radius of 1.5 m, easy operation, and the pulling power provided by the 750 W electric motor. The range on a single charge, depending on the tricycle's load, is approximately 30 km.  
Given that it is a functional prototype, further development focused on design, ergonomics, and optimization of the user properties of the three-wheeled unit is still required. Moreover, it is essential to gather feedback from users and analyse it to identify areas that need improvement or modification to achieve maximum efficiency and user satisfaction with the product.  
The intelligent features of the smart electric tricycle include connectivity to the Internet of Things (IoT), enabling it to receive and share essential information in real time. This allows for continuous monitoring of the production environment through integrated front and rear cameras. Side-mounted safety sensors help prevent potential collisions by detecting obstacles during material transport and movement within the production area, with incidents displayed directly on the front screen. Furthermore, the tricycle is equipped with a 10-inch tablet featuring WiFi, Bluetooth, and a SIM card slot, facilitating communication through specific enterprise applications that can be installed directly onto the device due to its open system architecture.