esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### System Design and Test  
#### 2.2.1 Design Workflow  
Design workflows can identify suitable parameter values of the solution architecture [27]. A design workflow model established procedures for design, such as those given in norms, standards, and guidelines in an executable form in the MBSE system model. The design workflow is modeled as an activity diagram for this purpose. The design workflow of a pump impeller is shown as an example in Figure 5. It is divided into three sections: _get_, _calculate_ and _set_.  
The design of the pump impeller requires the selected values of the design variables to perform calculations. Actions in the get section, therefore, read the associated values from the MBSE system model and make them available for further actions. The following five design variables (cf. get section Figure 5) are set by the systems engineer and read from the MBSE system model:  
* Rotational speed \(n_{Opt}\), flow rate \(Q_{Opt}\) and head \(H_{opt}\) in the best efficiency design point indexed as Opt
* Impeller vane exit angle \(\beta_{2}\)
* Number of impeller blades \(z_{U}\)  
The calculate section contains actions that use methods to calculate parameter values of the principle solution. These actions can represent simple analytical relationships (as in _calculatePumpInstallationSpace_) or trigger extensive calculations stored in external tools (as in _calculatePumpDimensions_). Although the design of the pump is based on principles of hydrodynamics, the calculation in _calculatePumpDimensions_ differs from the model of the  
Figure 4: Architecture of the hydrodynamic pump as a principle solution of the function _apply mechanical energy to fluid_.  
physical effect in Figure 4. The physical effect models the in- (rotational speed and pressure difference) and output (torque and flow rate) behavior of the pump. In contrast, the design workflow determines the necessary parameters of the physical effect. After the calculations in the workflow, the parameter values must be transferred back to the MBSE system model. The values can be used by further calculations or checked for requirement satisfaction. The following eleven parameter values (cf. set section in Figure 5) are calculated from the five design variables and written back to the MBSE system model:  
* Parameter values of the pump characteristic curve \(a,\ b,\ c,\ d\)
* Head coefficient \(\psi_{Th}\)
* Impeller diameter \(D_{2}\) and width \(b_{2}\)
* Pump installation space \(V_{Pump}\)
* Shaft power at zero net flow rate \(P_{0}\)
* Exchange flow rate at zero net flow rate \(Q_{a}\)
* Characteristic pump rotational speed \(n_{q}\)
# A Novel Methodology for Planning Urban Road Safety Interventions
## 2 Literature Review
### Predictive Approaches  
Predictive methods use statistical models, such as regression analysis, to estimate the likelihood of future road accidents. By combining historical accident data with mathematical modeling, these methods offer a robust foundation for targeted and efficient road safety planning [16]. A key example is the Empirical Bayesian (EB) method, employed by the HSM [6]. The HSM integrates:  
* Safety Performance Functions (SPF), which predict expected accidents under base conditions;
* Crash Modification Factors (CMF), which adjust predictions to reflect site-specific conditions;
* Calibration Factors (C\({}_{\text{x}}\)), which tailor models for different geographic contexts.  
This approach identifies high-risk road segments, prioritizes maintenance, and evaluates intervention impacts. Studies in Finland [17] and Norway [18] highlight the accuracy and reliability of EB-based predictions, particularly for planning safety improvements and assessing intervention benefits. Predictive methods provide valuable insights, even with fluctuating accident data, making them a vital tool for proactive road safety management.
## 4 Implementation and Results
### Communication  
The communication protocol used is Modbus TCP/IP, and its configuration is shown in the class diagram in Figure 3. First, the Arduino encodes and decodes the data sent, for which the Ethernet and Modbus libraries are installed, and then the variables for sending and receiving data are created. Then the IP network, MAC, Gateway, and Subnet are configured, and the registers are started to send the data bi-directionally between the PLC and Unity.  
Subsequently, the TIA Portal software is programmed with the configuration of the Modbus network parameters to send and receive data between PLC S71200 and Arduino. The programming in the TIA Portal software is performed in Lader language, while in Arduino, the reading and writing of the registers are performed through Modbus communication. The data blocks (DB) are configured for sending and receiving data in  
Figure 3: Communication class diagram.  
Figure 2: General Scheme of the DT.  
each direction, as shown in Figure 4a. Finally, serial communication between Unity and the embedded system is established. To achieve this, programming is performed in Visual Studio to develop serial communication, COM port assignment, and speed. The variables to transmit and store data are also created, and the digital and physical environment movements supported by augmented reality are interpreted. Figure 4b shows the class diagram of the configuration in the TIA Portal.
# Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
## 3 Proposed Method
### Transfer Learning as Post-Training  
Our proposed method aims at transferring the knowledge from a high-resource language \(L_{S}\) to a low-resource language \(L_{T}\). Transfer learning in the context of multilingual PLM has mostly revolved around zero-shot learning, and the small number of existing few-shot and supervised learning approaches limit the performance in \(L_{T}\) by forcing the model to maintain its ability in \(L_{S}\). This is based on the assumption that there are none or few labeled examples in \(L_{T}\) while unlabeled data is abundant. However, it has been suggested that this assumption is neither realistic nor practical [14; 15].  
Instead of this limited transfer learning approach, we assume that both unlabeled and labeled data are available in \(L_{T}\) and formulate this as a pretraining and post-training problem. Post-training refers to the process of performing additional unsupervised training to a PLM such as BERT using unlabeled domain-specific data, prior to fine-tuning. It has been shown that this leads to improved performance by helping the PLM to adapt to the target domain [46; 47; 48; 49]. We start with a monolingual PLM in \(L_{S}\) and completely adapt it to \(L_{T}\).  
Figure 2: Illustration of the proposed approach and previous approaches. (**a**) Cross-lingual post-training (ours). (**b**) Multilingual language model pretraining. (**c**) Monolingual transfer learning.  
Another key advantage of this approach is that this makes it possible to completely skip the training in \(L_{S}\). This is because most recent publications in PLM literature make the trained model checkpoint publicly available, and the model architecture and training objectives in \(L_{S}\) are inherited to \(L_{T}\) when post-training. Beside the cost saving, this also enables the use of this method when the pretraining data in \(L_{S}\) is not available in certain scenarios (e.g., privacy concerns, licensing, etc.).
## 2 Materials and Methods
### DSC Oxidative Stability Test  
Oxidation induction time (OIT) was determined by the differential scanning calorimetry (DSC) following the ISO 11357-6 (ISO, 2018) [22]. Samples of lipid fraction were analyzed in a DSC 7 Perkin Elmer along with an Intracooler II, operated with Pyris software 10.1. The instrument was calibrated using indium (m.p. 156.6 \({}^{\circ}\)C, \(\Delta\)Hf = 28.45 J/g) and n-dodecane (m.p. \(-\)9.65 \({}^{\circ}\)C, \(\Delta\)Hf = 216.73 J/g), 99.99% pure nitrogen was used as the purge gas. Oil samples of approximately 6-7 mg were weighed into 50 \(\mu\)L open aluminum pans (Perkin Elmer, No. 02190041) and placed in the equipment's sample chamber. The reference was the same open and empty aluminum pan. Isothermal protocol was followed to determine the oxidative stability characteristics of the oils at a constant temperature of 100 and 120 \({}^{\circ}\)C with an oxygen flow of 20 mL/min (purity 99.995%). Based on the oxidation curves obtained, parameters denoted as oxidation induction time (OIT) were determined after curve normalization as the intersection of the extrapolated baseline and the tangent line to the descending exotherm. Oils samples were analyzed in two replications for all DSC measurements.
## 3 Experiments and Discussion
### Experimental Settings  
The proposed architecture consisted of three modules. The number of layers in the spectral dimension reduction module, graph convolution branch, and convolution branch were all set to two. The spectral dimension reduction modules started with two \(1\times 1\) convolution layers (128 filters and 128 filters). The size of the convolution kernels in the CNN branch was set to 3 \(\times\) 3 (128 and 64 filters), and the sample output dimensions in the GCN branch were set to 128 and 64. We used the Adam optimizer to train our model with a learning rate of 0.001, and the training epoch was set to 500. The number of superpixels for each dataset was set to 1/100 of the number of pixels.  
The proposed algorithm is implemented in Python 3.8.12 and Pytorch1.1.0 using Python language. The hardware used for training is an i7-10750H CPU and a NVIDIA GeForce RTX 2060s GPU.
# Enhanced Message Replication Technique for DTN Routing Protocols
## 2 Related Works
### Flooding Protocols
Based on the above analysis, the issue with flooding-based protocols is the high consumption of network resources such as bandwidth, buffer, and energy. Therefore, to mitigate the waste in the network resources issue, quota-based protocols should be investigated.
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 4 Method  
We propose an online learning approach based on DRL to solve the LA task. This work takes the first step in evaluating the effectiveness of using DRL for LA with a large and less compromised observation space. The goal of high throughput is achieved based on the surveillance of each individual subcarrier state.  
Figure 1: Actor-critic reinforcement learning approach with used toolkits.  
As shown in Figure 2, the agent observes the state \(S\) of the system on the receiver side and proposes an MCS recommendation to the transmitter. The transmitter then uses the MCS while the resulting PER on the receiver side determines the reward \(R\). The agent uses the reward \(R\) to adjust its decision-making process, modeled as a neural network.  
The agent implements an actor-critic (AC) RL algorithm, while GNU Radio encapsulates the RL environment, as displayed in Figure 3. The environment provides an observable state in form of estimated \(SINR\) values corresponding to the \(n\) equidistant data carrying subcarriers of the previous transmission (i.e., without the guardbands and DC carrier).  
\[s=\begin{bmatrix}SINR_{SC_{0}}\\ SINR_{SC_{1}}\\ \vdots\\ SINR_{SC_{n}}\end{bmatrix} \tag{1}\]  
The action space consists of the available \(MCSs\) defined by the given radio transmission standard. For this work, our actions space consists of eight actions, as defined by the IEEE 802.11p standard [12].  
\[a\in\{MCS0,MCS1,\dots,MCS7\} \tag{2}\]  
The packet success rate \(PSR=(1-PER)\) with \(PER\) indicating the packet error rate, is then used to calculate the effective data rate \(\mathcal{R}_{effective}\) that is provided as reward \(R\) to the agent, where \(\mathcal{R}_{a,theoretical}\) is defined as the coded data rate of the chosen \(MCS\).  
\[\mathcal{R}_{effective}=(1-PER)\cdot\mathcal{R}_{a,theoretical} \tag{3}\]  
The \(PSR\) can be used for the reward calculation during the training process. Since we do not have access to the receiver's \(PER\) in a real-world scenario, we propose using a single metric or a combination of several metrics that are available on the transmitter once the training process has terminated successfully, e.g., cyclic redundancy check (CRC).  
Figure 2: The basic concept of the presented approach. An agent observes the state and recommends an action. Finally, the environment provides a reward, depending on the success of the previous transmission.
# BERT Models for Arabic Text Classification: A Systematic Review
## 4 Discussion
### How Effective Are They for Classifying Arabic Text?
The first two models were pretrained so they can support over 100 languages each and therefore, as has been explained earlier, had small Arabic pretraining corpuses which affected their performance. The same applies to Arabic ALBERT, which was pretrained on a slightly smaller corpus compared to the high-performing models. Regarding GigaBERT, it was not designed for text classification but rather as a bilingual BERT for zero-shot transfer learning from English to Arabic. The high-performing models include AraBERT, MARBERT, ArabicBERT, ARBERT, and QARiB. A common feature that these high-performing models share is that they were all pretrained on large Arabic corpora. By looking at the number of times each one of them outperformed the other models, it can be said that MARBERT has the best performance of the other four. It is followed by QARiB then ARBERT. In fourth place comes AraBERT, and lastly, ArabicBERT. MARBERT and QARiB are the only models that were pretrained on both Modern Standard Arabic and dialectical Arabic corpus. This seems to have had a positive impact on their performance. The other three models, i.e., ARBERT, AraBERT, and ArabicBERT, were only pretrained on Modern Standard Arabic corpus. Therefore, when dealing with the testing datasets in which the majority of them contained dialectical text, MARBERT and QARiB were able to perform slightly better than ARBERT, AraBERT, and ArabicBERT. A final point worth mentioning is that a large pretraining corpus does not always lead to a better performance. Version 1 of AraBERT, which was pretrained on around 23 gigabytes of text, showed better performance than ArabicBERT, which was pretrained on 95 gigabytes of text. Out of the six comparisons that included both models, AraBERT was able to outperform ArabicBERT in five comparisons (83%). Therefore, it can be said that the quality of the pretraining data has a significant impact on the performance of the BERT model.
# Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect
## 2 Related Work
The problem is defined as, given a target user w, finding the top-k most influential nodes for the user (i.e., local optima). [PERSON] et al. [27] design an independent cascade-based model for influence maximization, called IMIC-OC, to calculate positive influence. To the best of our knowledge, no existing work on influence maximization considers network dynamic properties such as social reinforcements which are common phenomenon in social network and viral marketing. There are many different types of reinforcement, but when it comes to human beings, one of the most common is the naturally occurring social rein forcers that we encounter all around us every day. Social reinforcement refers to reinforces such as smiles, acceptance, praise, acclaim, and attention from other people. In some cases, simply being in the presence of other people can serve as a natural social reinforcement. Researchers have found that social reinforcement can play a vital role in a variety of areas, including health. The influence of people in our social networks can influence the type of health choices and decisions that we make. Although this simplifies the problem, it results in the disagreement between actual and theoretical situations and discounts the practical value of the theoretical research. [PERSON] et al. [28]. discuss the temporal nature of viral marketing in practice on big data with a set of visual accompaniments and novel methodology. Some researchers also view social reinforcement as a type of affinity [29], which develops a dynamical information diffusion model referred the affinity of people; however, the research of reinforcement in the reference [29] is still different from the research in our research, because this method in the reference [29] only considers three kinds of nodes, and the method in this paper consider the spread reasons. This paper proposes a new propagation model to capture the dynamic changes of memory effect and social reinforcement effect, and improves this model. Experimental results show that the improved model can capture some important features of viral marketing in social networks and explain some phenomena that cannot be explained by the existing definition of impact maximization problem. What's more, the improved model also has lower time complexity.
## 3 Results and Discussion
### Estimation of Proton Energies Incident on the Second CR-39 Detector
Thus, considering a number of 1000 protons incident on the front side of the detector stack, we calculated the number of protons stopped in each slice of CR-39 detector material as a function of the proton incident energy (see Figure 6b). For proton energies ranging from 2 to 15 MeV, the graph presented in Figure 6b exhibits some discontinuities at 2.2, 5, 8, 10 and respectively, 12 MeV. The SRIM code calculates the number of protons stopped into the CR-39 slab considering its chemical composition (polyallyl-diglycol-carbonate (PADC), C\({}_{12}\)H\({}_{18}\)O\({}_{7}\)) and a value of the detector density of about 1.3 g/cm\({}^{3}\) (given by the Track Analysis System Ltd). So, we suppose that these discontinuities could be specific to the material physical and chemical properties. \begin{table}
\begin{tabular}{c c} \hline \hline
**Proton Incident Energy on Al+2 CR-39 Detectors** & **Mean Energy of the Protons Incident on the Second CR-39** \\
**Stack Calculated with SRIM 2013** & **Detector from the Stack Calculated with SRIM 2013** \\ \hline
10.5 & 1.6 \\
11 & 3.2 \\
11.5 & 4.4 \\
12 & 5.46 \\
12.5 & 6.43 \\
13 & 7.25 \\
13.5 & 8 \\
13.8 & 8.4 \\
14 & 8.77 \\
14.5 & 9.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correspondence between the incident proton energy on the first and second CR-39 detector. Figure 6: (**a**) Dependence of proton transmitted energy through 1 mm thick CR-39 detector on incident proton energy within the 10–15 MeV range; (**b**) Counted number of protons stopped in 2 mm CR-39 detector as function of incident proton energy.
## 3 Research Methodology
### Classification Using Squirrel Search-Optimized Gradient Boosted Decision Tree
To determine data \(X\), GBDT employs \(M\) additive functions as shown in the following equation to obtain the desired result:  
\[\hat{y}_{GBCT}=\sum_{i=1}^{M}\eta m^{b}(x_{k,1};\gamma). \tag{9}\]  
In a GBDT model, each successive tree predicts the pseudo-residuals of the previous trees, given an arbitrary differentiable loss function. The user defines both the loss function and the function that calculates the associated negative gradient. The loss function is minimized by combining the predictions and training additional trees. The number of trees is a critical parameter in gradient boosting, as too few or too many trees can result in underfitting or overfitting, respectively. Figure 2: Example of GBDT. The pseudocode of the proposed squirrel search-Optimized Gradient Boosted Decision Tree (SS-GBDT) is presented in Algorithm 2. ```
1. Initialize variables: Iterations (\(Iter\)), Population size (\(N_{P}\)), Decision variables (\(N_{D}\)), Likelihood of predator presence (\(P_{PP}\)), Scaling factor (\(F_{s}\)), Gliding constant (\(C_{g}\)), Upper and lower bounds for decision variables (\(U_{D}\), \(L_{D}\))
2. Initialize Flying Squirrels (FLS) randomly: for \(i\) in range \((1,\ N_{P}+1)\): for \(j\) in range \((1,N_{D}+1)\): \(FLS_{id}=L_{D}+rand()*(U_{D}-L_{D})\)
3. Evaluate Fitness (FV) for each Flying Squirrel (FLS): for \(i\) in range \((1,\ N_{P}+1)\): \(f_{i}=f_{i}(FLS_{i,1}FLS_{i,2},\ldots\ldots FS_{i,n})\)
4. Sort FLS positions based on their FV: \(\left[\mathit{sorted}_{f},\mathit{sort}_{\mathit{index}}\right]=sort(f)\)
5. Create new positions by aerodynamic gliding or random walk: for each FLS: if \(R_{1}\geq P_{PP}\): \(FLS_{at}^{new}=FLS_{at}^{old}+D_{g}C_{g}\left(FLS_{ht}^{old}-FLS_{at}^{old}\right)\) else: \(FLS_{at}^{new}=\) random location Limit the new positions to the lower and upper bounds
6. Repeat steps 2-5 for the maximum number of iterations (\(Iter\))
7. Initialize GBDT model with the optimized parameters obtained from squirrel search
8. Train GBDT model with training data: Stage 1: Set the initial constant value for the model Stage 2: For \(m\) in range \((1,\ M+1)\): 2.1: Determine step size and minimal loss reduction for averaging the weights of different trees 2.2: Update the model Stage 3: Return the final model \(F_{m}(x)\)
9. Use GBDT model for prediction and classification
```  
**Algorithm 2**Squirrel Search-Optimized Gradient Boosted Decision Tree (SS-GBDT)
## 2 Related Works
In this existing study, a hybrid genetic algorithm with hill climbing (GAHC) was used for selecting the optimal route. Initially, an improved fuzzy c-means approach was constructed on density peaks and the cluster heads were selected in an estimated way depending on direct, indirect, and recent trust values. Here, the computation also depends upon the trust threshold worth nodes obtained. Even cluster heads were obtained in the multi-hop routing process, and it was the combination of all routes. The features utilized in this existing work for obtaining optimal routing were latency, throughput, and connection. The developed protocol afforded optimal performance compared to other comparable protocols. [PERSON] et al. [27] introduced an efficient video-transmission process using clustering and optimization approaches in a MANET environment. This existing study used discrete wavelet transform (DWT) to the video frames for decomposing the input frames into several sub-bands. For transmitting the videos, bit streams were utilized with the stream control transmission protocol (SCTP) multi-streaming. Here, clustering was carried out by the enhanced fuzzy c-means algorithm (EFCM). Then, the optimal path selection was performed by the enhanced cuckoo search (ECS) optimization algorithm for sending video streams. Through the inverse operation of the video-sending part, frame reconstruction was enabled at the receiver side. The simulation analysis reveals the effectiveness of this existing study by achieving less performance in delivery ratio, delay, energy consumption, throughput, and overhead by changing the number of nodes and rates.Sharma et al. [28] utilized improvised OSLR with an optimized multi-protocol router (MPR) and DYDOG for affording secure video transmission. Here, the developed hybrid approach can ensure secure video streaming in MANETs by optimizing factors such as energy utilization and delay in the OLSR. The developed protocol enhanced the node's mobility and promoted security by analyzing varied malicious attack nodes such as packet replication, black holes, and wormholes. This study used encryption methods such as digital signature and AES to secure the frames against malicious attacks. The simulation results prove the efficacy of the developed approaches.
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
## 5 Measurement Result
### Hamming Weight  
The Hamming weight illustrates the distribution of '1's and '0's in the PUF response [29]. Figure 7a presents the Hamming weight distribution across 40 PUF instances with a specific challenge. The results demonstrate a mean of 50.30% with a standard deviation of 4.14%, indicating that it closely approaches the ideal value of 50%. This measurement result is much better than the simulation value due to the integration of more process variations from an array of 144 PUF cells, whereas the simulation only considers a single PUF cell. Additionally, this measurement result is the best value that we achieved with various challenge inputs.