esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Examples
### Interconversions  
#### 3.3.1 Dynamic Modulus to Relaxation Modulus  
Regarding to the procedures of interconversion from dynamic modulus to relaxation modulus, storage modulus is needed to be calculated based on Equation (12). In the equation, relaxation times \(\rho_{i}\) is expected to be identified first. According to reference [3], the distribution of \(\rho_{i}\) could be estimated through the collocation method. Moreover, in general terms, the integrated curves could be smoother with smaller relaxation time interval. The logarithmic time interval lower than 0.5 is recommended [2]. However, the curve oscillation has already reached an acceptable range when the logarithmic time interval is less than 1 [15]. Therefore, the logarithmic time interval 1 will be employed in the following computation. Another important issue is the number of Maxwell elements, more elements yields to higher accuracy in general, but leading to more complexity at the same time [31]. Usually, 10 elements are enough for the transformation [32]. Therefore, to span enough time range of the data, 11 elements will be taken, meaning that \(\rho_{i}=2\times 10^{i-7}s,\ i=1,2,...,11\). Consequently, the Prony series coefficients converted by dynamic modulus are listed in Table 2.
# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 3 The Proposed Method and Its Applications on Downstream Tasks
### The Sentiment-Enhanced Word Embedding Method
#### 3.1.4 Loss Function  
The loss function measures the difference between true sentiment orientation labels in a sentiment lexicon and predicted sentiment orientation labels of words at the final module of the S-EWE method. We use the cross-entropy function  
\[\mathcal{L}=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\text{log}(\hat{y}_{i})+(1-y_{i}) \text{log}(1-\hat{y}_{i})], \tag{5}\]  
to calculate the loss, where \(N\) is the total number of samples.  
In order to better learn the mapping relationship between original word embeddings and their true sentiment orientation labels in the S-EWE method, we freeze the word embedding layer. All word embeddings and true sentiment orientation labels are fixed in this case. The parameters \(W\), \(b\), and sentiment mapping matrix are trainable. By backpropagation, the method can better learn the sentiment mapping matrix and find the mapping relationship between original word embeddings and their corresponding sentiment orientations. After training, we can obtain the well-trained sentiment mapping matrix, denoted as \(\mathbf{\mathcal{R}}^{t}=\{\mathbf{\
abla}_{1}^{t},\mathbf{\
abla}_{2}^{t},\ldots,\mathbf{ \
abla}_{n}^{t}\}\). Finally, the well-trained sentiment-enhanced word embeddings, denoted by \(\mathbf{E}^{t}=\{\mathbf{e}_{1}^{t},\mathbf{e}_{2}^{t},\cdots,\mathbf{e}_{n}^{t}\}\), are obtained as follows.  
\[\mathbf{E}^{t}=\mathbf{w}+\mathbf{\mathcal{R}}^{t}. \tag{6}\]  
The detailed running procedure of the S-EWE method is described in Algorithm 1.  
```
1:Set of words in a sentiment lexicon \(w=\{w_{1},w_{2},\ldots,w_{n}\}\), set of sentiment orientations in the sentiment lexicon \(Y\), number of epochs denoted by \(epochs\), batches denoted by \(batch\)
2:Well-trained sentiment-enhanced word embeddings \(\mathbf{E}^{t}\)
3:Initialization \(\mathbf{\mathcal{R}}\leftarrow\) xavier Gaussian initialization
4:\(\mathbf{w}\leftarrow\) embed(\(w\)) // Equation (1)
5:\(i\gets 0\)
6:while\(i<epochs\)do
7:for\((\mathbf{w},\mathbf{y})\in batch\)do
8:\(\mathbf{e}_{i}=\mathbf{w}_{i}+\mathbf{\
abla}_{i}\) // Equation (2)
9:\(\mathbf{\hat{y}}\leftarrow\) Softmax(\(\mathbf{w}\mathbf{e}_{i}+\mathbf{b}\)) // Equation (4)
10:\(\mathcal{L}\leftarrow-\frac{1}{\text{len}\{batch\}}\sum_{i=1}^{\text{len}\{ batch\}}\left[y_{i}\text{log}(\hat{y}_{i})+(1-y_{i})\text{log}(1-\hat{y}_{i})\right]\) // Equation (5)
11: Backpropagation
12: Optimize parameters
13:endfor
14:\(i\gets i+1\)
15: Obtain well-trained sentiment-enhanced word embeddings \(\mathbf{\mathcal{R}}^{t}\)
16:endwhile
17:return well-trained sentiment mapping matrix \(\mathbf{E}^{t}=\mathbf{w}+\mathbf{\mathcal{R}}^{t}\) //Equation (6)
```  
**Algorithm 1** The S-EWE algorithm.
## 5 Numerical Experiments  
The experiments include case studies using three algorithms: DP, DP with action reduction (ar-DP), and PPO. They aim to compare the algorithms' performances. In addition, a sensitivity analysis of the problem using DP was conducted.  
These experiments aim to address the following questions:  
1. How does action reduction affect computation time?
2. How does PPO perform compared to DP in terms of computation time and total profit?
3. How does a change in price sensitivity impact costs?  
For the experiments addressing questions 1 and 2, we compared the performance of DP, ar-DP, and PPO in terms of computation time and total profit. Different parameters of the inventory management problem were used to evaluate the algorithms. For the experiment addressing question 3, we conducted a sensitivity analysis using DP. Case studies were conducted using varying price sensitivity, and comparisons were made in terms of total profit and the breakdown of various costs.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 5 Simulation and Results
### Network Configuration  
As shown in Figure 9, there are 12 streets and nine intersections in the simulation area. The nodes in this area are placed randomly at the beginning, and they move to a random destination with a speed not exceeding 15 m/s.  
The simulation experiments were implemented on ns-3.27. For sparse traffic scenarios, the number of vehicles is set to 30, 40, 50, 60, 70 and nodes with a maximum node speed of 15 m/s. The maximum communication radius of the vehicle was set to 250 m. Accurate vehicle position information can be provided by the positioning system, in other words, the error of GPS is not considered. The data traffic is considered as Constant Bit Rate(CBR). And the experiment of each scene was run for 150 s. Other parameter values are assigned on the basis of the values used in [28]. Table 3 summarizes the key parameters in the simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **GPSR** & **RTEIT** \\ \hline Greedy forwarding & \(n^{2}\) & \(n^{2}\) \\ Recovery forwarding & \(n^{3}\) & \(n^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity comparison of GPSR and real-time effective information traffic routing (RTEIT).  
Figure 9: Simulation area.
## 4 Result
### Feature Importance Analysis Using XGBoost and Comparative Review with SHAP
#### 4.2.1 Feature Importance Analysis for AI Literacy in the Class Preparation Phase
Features with substantially lower SHAP influence relative to their XGBoost importance (for example, the perceived importance of AI Literacy in the results-utilization phase) likely have more context-dependent effects, contributing less uniformly across all teachers. In contrast, features that exhibited higher SHAP values than expected from the XGBoost ranking (such as teachers' emphasis on AI Literacy skills) seem to exert their influence mainly under particular conditions, which the tree-based metric may understate. Additionally, SHAP dependence plots highlight interaction effects between key factors. For instance, the positive impact of a teacher's AI Literacy skills on predicted class preparation performance is markedly amplified when that teacher also assigns a high importance to integrating AI in the preparation phase; this synergy appears in SHAP plots as significantly larger SHAP values for the skills feature when both features are high. Similarly, teachers who rate both their own and their students' need for AI Literacy learning as high tend to show amplified SHAP contributions for these need-related features, implying that a broad awareness of AI Literacy needs (encompassing both oneself and one's students) can reinforce the influence of other variables on preparation-phase performance. These interaction patterns underscore that a teacher's commitment to AI integration and recognition of learning needs to work synergistically with their skillset, ultimately shaping their effectiveness in the Class Preparation Phase.
###### Abstract  
To reduce congestion, numerous routing solutions have been proposed for backbone networks, but how to select paths that stay consistently optimal for a long time in extremely congested situations, avoiding the unnecessary path reroutings, has not yet been investigated much. To solve that issue, a model that can measure the consistency of path latency difference is needed. In this paper, we make a humble step towards a consistent differential path latency model and by predicting base on that model, a metric _Path Suap Indicator (PSI)_ is proposed. By learning the history latency of all optional paths, _PSI_ is able to predict the onset of an obvious and steady channel deterioration and make the decision to switch paths. The effect of _PSI_ is evaluated from the following aspects: (1) the consistency of the path selected, by measuring the time interval between _PSI_ changes; (2) the accuracy of the channel congestion situation prediction; and (3) the improvement of the congestion situation. Experiments were carried out on a testbed using real-life Abilene traffic datasets collected at different times and locations. Results show that the proposed _PSI_ can stay consistent for over 1000 s on average, and more than 3000 s at the longest in our experiment, while at the same time achieving a congestion situation improvement of more than 300% on average, and more than 200% at the least. It is evident that the proposed _PSI_ metric is able to provide a consistent channel congestion prediction with satisfiable channel improvement at the same time. The results also demonstrate how different parameter values impact the result, both in terms of prediction consistency and the congestion improvement.  
Keywords:consistent routing; network fluctuation; traffic engineering; latency prediction; backbone networks +
Footnote †: journal:
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 2 Materials and Methods
### Transducer  
In view of a future use of the system not only as a general purpose ultrasound research platform but with special focus on prostate therapy monitoring by tracking of radiation-sensitive ultrasound contrast agents [16; 17], in the context of the AMPHORA project, a transducer with optimized footprint for transperineal imaging was developed. This transducer was used for a first characterization of the multichannel electronics. Typical matrix transducers show small footprints to fulfill the \(\lambda/2\) criterion for optimized beam-steering capability, which leads to low penetration depths. For the application of therapy monitoring through the perineal window, a high penetration and imaging depth is necessary while accepting smaller beam-steering angles.  
Therefore, the perineal window and the number of available channels define the maximum rectangular footprint of the aperture. With the target dimension of 30 mm \(\times\) 20 mm given by the planned perineal application and 1024 dedicated channels, a pitch of 770 \(\upmu\)m, corresponding to 2\(\uplambda\) at a center frequency of 4 MHz, results in 26 elements in the elevation direction and 39 elements in the lateral direction. Sound field simulations with our proprietary point source synthesis simulation software \"SCALP\" were performed to optimize the array geometry. For optimized oscillation modes, FEM simulations suggested a 3 \(\times\) 3 sub dicing on each element. For further enhanced energy transfer, the acoustic stack was processed on a syntactic foam backing with flex printed circuits to connect through it. To get full advantage of multi-channel capabilities each transducer element is connected to an individual electronics channel via four separated 256-core micro coax cables. Two FEM optimized matching layers were applied for improved bandwidth and energy transfer, which is crucial in the foreseen therapy monitoring application.  
Beyond the targeted clinical application, a commercial matrix array probe made by the French company Vermon with 32 \(\times\) 32 elements was also used to characterize the volumetric ultrasound platform. The transducer has a center frequency of 2.8 MHz and a pitch of 300 \(\upmu\)m. The aperture has a footprint size of 9.6 mm \(\times\) 9.6 mm.
## 2 Proposed Architecture for Mars Exploration with IoT
In the case of gateways, LoRa can be used on rovers or probes on the surface of Mars. It can provide coverage in open spaces of up to 15 km [37]. LPWAN protocols are considered an alternative for the integration of IoT technologies for both terrestrial and space networks [17]. In satellite IoT communications, LoRa/LoRaWAN and Narrowband IoT (NB-IoT) are prominent options for connecting long-range with reduced costs and low energy consumption [38]. Recent research shows the feasibility of establishing direct links with satellites by implementing these protocols [17], enabling direct communication between IoT nodes and satellites in orbit. This can be applied to other planets after redesigning the communication architectures. Between the two options, studies position LoRa/LoRaWAN as an IoT connectivity protocol suitable for exploring Mars or the Moon [11] due to its energy efficiency [14; 39; 40] and greater robustness against the Doppler effect [14]. Its adaptation to the architecture proposed in Figure 2 focuses on guaranteeing communications between the IoT nodes and the satellites, for which it is necessary to use a LoRaWAN network server (LNS) to manage and administer the communications located physically on Earth. This server collects device data, contextualizes them, and prepares them for application use [14]. Since there is no regulation on the frequencies that can be used on Mars, the use of any terrestrial frequencies used by this technology can be proposed. Therefore, 868 MHz and 433 MHz have been used in this study.
## 2 Materials and Methods
### Impulsive Manoeuvres
#### 2.2.1. Hohmann Transfer  
[PERSON], a German engineer, is best known for his discovery of the orbital transfer manoeuvre, known as the Hohmann Transfer, which is illustrated in Figure 2. It consists of using an elliptic transfer orbit, which is tangential to two circles representing the internal and external orbit. The distance from the central body to the periapsis of the transfer ellipse matches the radius of the internal circular orbit, while the distance to the apoapsis matches the radius of the external circular orbit. During the manoeuvre, the spacecraft moves only along half of the transfer ellipse, and the movement may take place in two directions. Hohmann transfer is considered to be the most efficient in terms of fuel consumption, as it only requires two impulses--two short burns of the engine at theoretically defined points of the orbit. For example, this process may include the initial transfer of the spacecraft to the low altitude \"parking orbit\", and then to a higher, circular orbit with the use of an elliptic transfer orbit, which is tangential both to the parking orbit and to the target circular orbit.  
Figure 2: Hohmann transfer, a diagram. Created based on [7]. (1) Central body; (2) Internal circular orbit of radius r\({}_{1}\); (3) External circular orbit of radius r\({}_{2}\); Points A and B represent the periapsis and apoapsis of the transfer ellipse.  
Calculating \(\Delta\)v  
If the manoeuvre starts at point A, situated on the internal orbit, then a velocity increase \(\Delta\)v\({}_{\rm A}\) in the direction of movement is required to transfer the spacecraft to the elliptical orbit with higher energy. After the satellite has moved from point A to point B, located on the external orbit, a further increase in velocity \(\Delta\)v\({}_{\rm B}\) is required to enable the spacecraft to move to the circular orbit of even higher energy. It is worth noting that without the second increase in velocity, the spacecraft would remain on the elliptical Hohmann transfer orbit, and, as a result, it would return to the starting point A. The total fuel consumption necessary for this manoeuvre may be calculated from the equation below [7]:  
\[\Delta v_{total}= \Delta v_{A}+\Delta v_{B}, \tag{3}\]  
The same value of \(\Delta\)v\({}_{\rm total}\) is required when the manoeuvre starts at point B, situated on the external circular orbit. In this case, the transfer to an internal orbit of lower energy requires a reduction in the energy of the spacecraft. To accomplish this, \(\Delta\)v\({}_{\rm A}\) and \(\Delta\)v\({}_{\rm B}\) must be achieved with the use of so-called retrofires. In practice, this means that the thrust of the rocket engines is directed against the direction of the spacecraft's motion, which enables the use of the thrust force as a brake. As a result, the orbital speed of the spacecraft is reduced, which enables it to move to the lower-energy orbit [7].
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 5 Evaluation of the RA Calculation Method and Verification of Usefulness of RA
### Usefulness of RA as a QoE Metric
The upside is that a video is less likely to become severely corrupted because any unstable connection state is judged to be a disconnected state. In contrast, OLSR performs route updates using TC messages, which are sent only periodically. Thus, it may frequently fail to detect disconnections and consequently fail to stop the video being played. In such a case, it continues to play a video even though its video quality is degraded by block noise. In the experiment, some videos continued to be played even though their connections were unstable. These videos were degraded by an accumulation of block noise and thus were seriously corrupted sometimes. In summary, with OLSR, playing of a video stopped less frequently but videos were degraded by large block noise. With AODV, distortion from block noise was smaller but videos suffered from the effects of instantaneous interruptions more severely.
# Feature Normalization Reweighting Regression Network for Sugar Content Measurement of Grapes
## 4 Conclusions  
This study used deep learning technology to predict the sugar content of Red Globe grapes. The deep convolutional neural network retraining using AlexNet, the transfer learning method using InceptionV3 and ResNet50, and the transformer model can ensure the integrity of the grapes by accurately and effectively predicting the sugar content value. Furthermore, we designed a grape image acquisition system. However, the collected dataset of grapes presented an imbalanced distribution. We proposed the FNRR framework to address this issue and demonstrate that it performed well on small samples of grapes with an imbalanced distribution. In addition, under our proposed multi-label loss function, and among all prediction models, the FNRR framework in conjunction with the ViT-L_16 transformer and the dataset categorized with intervals of 0.8% yielded the optimal performance, with R = 0.9599 and RMSE = 0.3841%. Using transformer models to predict the sugar content of grapes is a potential method for the non-destructive quality testing and grading of grapes. In future studies, we aim to enrich our dataset to make it applicable to other fruits and apply the FNRR framework to industrial production.  
Conceptualization, funding acquisition, J.L. ([PERSON]); methodology, T.H.; software, J.L. ([PERSON]); validation, formal analysis, Y.J.; investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization, supervision, project administration, M.J. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Performance results using FNRR-net: (**a**) prediction results based on AlexNet model with the interval of 0.4% and MSE loss function, (**b**) prediction results based on InceptionV3 model with the interval of 0.4% and MSE loss function, (**c**) prediction results based on ResNet50 model with the interval of 0.4% and MSE loss function, (**d**) prediction results based on ViT-L_16 model with the interval of 0.8% and balanced loss function. The solid line is the fitting line between the predicted and measured values, and the dotted line is 1:1 line.  
**Funding:** The Fundamental Research Funds for the Central Universities (grant nos. 2662022 XXYJ006, 266201 PY059 and 2662015 PY066), the National Natural Science Foundation of China (grant nos. 61176052 and 61432007), and the Cooperation funding of Huazhong Agricultural University-Shenzhen Institute of Agricultural Genomics, Chinese Academy of Agricultural Sciences (HZAU-AGIS Cooperation Fund SZYJY2021011).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The detection of abusive language in Roman Urdu is important for secure digital interaction. This work investigates machine learning (ML), deep learning (DL), and transformer-based methods for detecting offensive language in Roman Urdu comments collected from YouTube news channels. Extracted features use TF-IDF and Count Vectorizer for unigrams, bigrams, and trigrams. Of all the ML models--Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB)--the best performance was achieved by the same SVM. DL models involved evaluating Bi-LSTM and CNN models, where the CNN model outperformed the others. Moreover, transformer variants such as LLaMA 2 and ModernBERT (MBERT) were instantiated and fine-tuned with LoRA (Low-Rank Adaptation) for better efficiency. LoRA has been tuned for large language models (LLMs), a family of advanced machine learning frameworks, based on the principle of making the process efficient with extremely low computational cost with better enhancement. According to the experimental results, LLaMA 2 with LoRA attained the highest F1-score of 96.58%, greatly exceeding the performance of other approaches. To elaborate, LoRA-optimized transformers perform well in capturing detailed subtleties of linguistic nuances, lending themselves well to Roman Urdu offensive language detection. The study compares the performance of conventional and contemporary NLP methods, highlighting the relevance of effective fine-tuning methods. Our findings pave the way for scalable and accurate automated moderation systems for online platforms supporting multiple languages.  
deep learning; machine learning; support vector machine; large language model +
Footnote †: journal: _Algorithms_
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 4 Problem Formulation and Solution  
### Problem Formulation  
The goal was to maximize \(EE^{[\text{sec}]}\) defined in (8) over all possible combinations of active/idle cells, \(\mathbf{\Phi}\), and consequent possible allocation matrices, \(\mathbf{\Gamma}\).  
\[\begin{array}{cc}(\mathbf{\Gamma}^{*},\mathbf{\Phi}^{*})&=\max_{\mathbf{\Gamma},\mathbf{\Phi} }\bigl{\{}EE^{[\text{sec}]}\bigr{\}}\\ \text{s.t.:}&\sum_{c}\gamma_{u,c}=1\quad\forall u=1,\cdots,\mathcal{N}_{U} \end{array} \tag{11}\]  
The constraint indicates that each user is associated with one serving SBS.