esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Radio Link Model for Underground Mines
### Propagation Zone Model
#### 2.2.3 Multi-Mode (Fundamental) Dividing Point  
The distance of Dividing Point 3 (\(DP_{3}\)) from Tx is calculated in Equation (13), where only the horizontal and vertical walls of the tunnel are used without any surface roughness.  
\[DP_{3}=Max\{Z_{h},Z_{v}\} \tag{13}\]  
where,  
\[Z_{h}=\frac{w^{2}\times Max(x_{t},w-x_{t})}{\lambda}+\frac{w^{2}\times Max(x_{ r},w-x_{r})}{\lambda} \tag{14}\]  
\[Z_{v}=\frac{h^{2}\times Max(y_{t},h-y_{t})}{\lambda}+\frac{h^{2}\times Max(y_{ r},h-y_{r})}{\lambda} \tag{15}\]  
where \(Z_{h}\) and \(Z_{v}\) are the horizontal and vertical distances between Tx and Rx, \(x_{t}\) and \(x_{r}\) are the horizontal coordinates of Tx and Rx, respectively, and \(y_{t}\) and \(y_{r}\) are the horizontal coordinates of Tx and Rx, respectively. The detailed path loss analysis of the model calculations and measured results is shown in Figure 3. The different multi-mode zones are well separated by 19 m and 30 m distances.  
Figure 3: Path loss analysis of the dividing points using calculated and measured results.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 6 Open Issues and Future Research Directions
### Generalisation  
A significant challenge in video deepfake detection using CNN and LSTM is the generalisation of models across diverse datasets. Typically, these models are trained on specific datasets, which can limit their ability to perform well when exposed to new, unseen datasets with different content, quality, or generation methods [84]. This lack of generalisation reduces the practicality of detection systems in real-world scenarios where deepfakes vary widely in their creation techniques and video characteristics [40; 56]. One major factor contributing to this challenge is dataset diversity. Most deepfake detection models are trained and validated on a small number of standardised datasets, such as DFDC or FaceForensics++, which represent only a fraction of the possible deepfake types. When tested on entirely different datasets, especially those featuring less common or more sophisticated techniques like neural textures, the performance of these models tends to degrade significantly. Over-reliance on dataset-specific artefacts, such as facial landmarks or lighting patterns, further reduces the model's ability to generalise. To address this, more research is needed to focus on developing large, diverse, and standardised benchmark datasets that can enhance model generalisation. Additionally, exploring transfer learning and domain adaptation techniques may improve the robustness of models, allowing them to perform effectively across different types of deepfakes and video domains.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 4 Workflow Stages in MLOps
### ML Training Phase  
This is the phase where data scientists play a significant role. These experts ensure the flexibility and scalability of ML pipelines, as well as an appropriate technology selection and efforts in model performance improvement [30]. In order to train a model until a specific goal (e.g., accuracy threshold) is reached, data scientists are responsible for choosing ML pipeline structures, algorithms and hyperparameters through model versioning and validation. They are the most expected users for the data processing phases in big data projects [17]. Every model version candidate is evaluated, and one is chosen with the domain expert for further deployment.  
Dependent on the required degree of automation in search for an ML pipeline, data scientists and domain experts are supported by different so-called AutoML techniques and tools. This area of ML was started to let non-technical domain experts somewhat configure the model training instead of having to implement the individual steps manually. In assembling different ML concepts and techniques, the development of feature preprocessing, model selection, and hyperparameter optimization are automated [44]. The process of selecting an algorithm, as well as its hyperparameters, is often implemented in one singular step and is referred to as CASH. In order to improve the performance in automated model training and hyperparameter optimization, techniques like k-fold cross-validation can be applied for early stopping the training when reaching a particular threshold [22].
## 3 Proposed Work
### Model Development and Training
#### 3.3.2 Random Forest with Extra Trees Regressor  
Random Forest Regression is a powerful machine learning technique that combines multiple decision trees, each trained on a random subset of data, to make predictions. By aggregating the outputs of multiple trees, the algorithm enhances stability and reduces variance, making it more robust against overfitting. One of its key strengths is its ability to handle complex datasets effectively. Additionally, it introduces an extra layer of randomness by selecting split thresholds randomly rather than strictly optimizing them, allowing for greater generalization in predictive tasks. This added randomness makes the model faster to train, reduces variance, and enhances robustness to noise compared to traditional Random Forest models. Mathematically, the model aggregates predictions from multiple decision trees to generate the final output for the regression problem. For a given set of features where we have \(\mathrm{X}=[\mathrm{x_{1}},\mathrm{x_{2}},\ldots\mathrm{x_{n}},]\) each tree \(\mathrm{Ti}(\mathrm{X})\) in the ensemble predicts a target value \(\mathrm{yi}\), and the final prediction is the average of all tree outputs:  
\[\hat{\gamma}=\frac{1}{\mathrm{N}}\mathrm{\sum_{i=1}^{N}Ti(X)}\]  
where N is the total number of trees in the ensemble, and \(\mathrm{Ti}(\mathrm{X})\) represents the prediction from the \(i\)-th tree. In this study, the Random Forest with Extra Trees Regressor utilizes the  
Figure 8: Linear Regression model prediction of RUL with Cycle Count included.  
Figure 7: Linear Regression model prediction of RUL without Cycle Count.  
entire features to predict the RUL. The n_estimators used = 100, and random_state = 42. All the features, but excluding the cycle count, were utilized to see how it will perform and after which we included all features including the cycle count and compared both performance metrics. For Remaining Useful Life (RUL) prediction, Mean Squared Error (MSE) is a widely adopted loss function in regression-based models such as the Extra Trees Regressor and Random Forest Regressor. As shown in Figure 9, excluding the cycle count as a feature led to a notable gap between the actual and predicted RUL values, with only moderate overlap indicating fair but suboptimal predictive performance. However, when cycle count was included (Figure 10), a visible improvement in overlap between predicted and actual values was observed, reflecting more accurate predictions. To further enhance the model's performance, we applied hyperparameter tuning to the Random Forest with Extra Trees Regressor. Specifically, we set the number of estimators to 300, the maximum depth to 20, and adjusted the minimum samples required for splits and leaves to 4 and 2, respectively. As illustrated in Figure 10, this significantly increased the overlap between actual and predicted values, minimizing prediction error while also helping to prevent overfitting. Overall, this process led to a more robust and reliable model for RUL estimation.
# An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion
## 5 Conclusions and Discussion  
Aiming at the missing feature of static text context of existing models, the proposed method used threshold to choose context area dynamical in threshold selector layer. The Bi-LSTM_ATT network was utilized to improve compatibility for the longer texts in feature extraction layer. To enhance text context representation, the MFF method was picked up in feature fusion layer. Making a comparison with prior studies, the SPERT-DC shares F1-score outperformance over theirs on the public datasets, the practicability of which was proved on ERE tasks. More attention will be paid to dynamic selction of the context representation and other languages in the future work. What is focused on is not only the existing feature area but also sematic representation, making a further advancement on the ERE model. It is noted that we only carry out experiments on English sentence dataset. We will also try to study the performance of our method on other languages such as Chinese in next research.  
In this paper, a inspiring method based on dynamic context is proposed for entity relation extraction task, which can dynamically extract feature information. According to the experimental result on two datasets, it is found that the proposed method bears fruit theoretically. In reality, the entity relation extraction is aimed at convert unstructured information to structural data that will be saved in database to be widely applied to natural language processing applications such as search engines and question answering systems. Due to the rapid increase of knowledge in the network, people prefer to obtain information quickly and accurately, rather than manually annotating the relation between entity pairs. In order to add abundant knowledge to the knowledge graph as timely and accurately as possible, researchers are constantly striving to explore technical methods for efficiently and automatically acquiring knowledge, that is, to continuously improve the effect of entity relation extraction, which also is precisely the purpose of this paper and the future application filed.  
Conceptualization, X.M. and K.W.; methodology, X.M.; software, K.W.; validation, H.K.; formal analysis, K.W. and H.K.; writing-original draft preparation, H.K. and X.L. All authors have read and agreed to the published version of the manuscript.  
This workwas supported by the National Natural Science Foundation of China (No. 61772088).  
Figure 8: Receiver operating characteristic curves of the method compared to other model. (**a**) is the ROC curve for CoNLL04 dataset. (**b**) is ROC curve for ADE dataset. The number “1” and “2” in left image represent the model in paper [12] and paper [13] respectively, unlike it represent the model in paper [7] and paper [12] respectively. The number “3” refers to baseline and “4” refers to our method.
## 4 Materials and Methods
### Project I  
The goal of the project was to predict production failures on automated screwing heads, therefore lowering failure rate on the screwing mechanism. Failures were connected to violation of established boundaries of key parameters: adhesive pressure, axial torque, and screwing depth. There were other available parameters of the process: quality and chemical constitution of material, its thickness, position of screw, etc.  
Data sources:  
* Screwing heads failures;
* Failures of the connecting technology attached to the screwing heads;
* Measure points;
* Product moves.  
The last data source ('product moves', containing timestamps for various points of the product occurrence during the production process) was originally not meant to be used at all. However, it had to be included early after the start of the 'Data Preparation' phase, since there was no identifier in the data common for all the sources, and, furthermore, they were not time-synchronized, making joining of the sources impossible. The 'product moves' data source contained enough information to connect it with all the three other sources.  
Specific problems arose due to multilingual nature of the company. Attribute names were often listed as a mixture of two or more languages, and the same applied to notes written manually by workers. This made some words illegible when using specific characters (such as Slovak letters with diacritics), particularly when transferring from one data source to another. Another problem connected to the issue was mixed usage of decimal point and decimal comma. In some cases, also dates were affected by multilingualism: date formats 'yyyy-MM-dd' and 'yyyy-dd-MM' were used interchangeably, and on some occasions, some of the records were accepted in incorrect format, while others were identified as text by analytical tools. Another minor (but still important) issue was the numerical values being recorded together with their units (such as [Nm] in screwing moment). If this was omitted during the preprocessing phase, the attribute was often misclassified as textual during the data modelling phase, while it was clearly a numerical attribute with wrong formatting.  
After successful preprocessing, in the 'Modelling' phase the appearance of various error types during individual weeks was analyzed on six-month period sample. We realized that the major problem of the screwing process was the screwing technique, so after that we focused mostly on 'Errors of the connecting technology' data source, which contains more detailed description of the attributes evaluated during the process. Further analysis was applied on ten robots with the biggest number of errors.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 5 Conclusions  
This paper introduces newly developed mathematical models that deal with the automatic network infrastructure design. These models were developed for two use-cases and implemented in the GAMS optimization tool. In the first case, the model enables the deployment of gNodeB nodes in a new area (without any existing infrastructure), and in the second case, it adds additional gNodeB nodes to the area with an existing, but not sufficient, infrastructure.  
Both models take into account important aspects such as the required user throughput that is rapidly growing these days in 5G networks as well as the aspect of deploying the gNodeB nodes that need to coexist with the existing infrastructure. These models can be used to improve the network design of the existing software solutions such as Atoll Radio Planning Software to do the network planning more efficiently. Further, we provide a data representation and computation concept to be able to implement it in an optimal way. Based on that, the models are validated through simulations provided for each deployment scenario (urban, suburban, rural area). The results of the simulations prove that these models are well-designed and can optimize the network in a significant manner. Furthermore, these simulations show that to deploy emerging wireless networks on a full scale with all the applications as is AR, VR and others, the network infrastructures have to be significantly enhanced.  
For future research, we will focus on the employment of a swarm of drones as flying on-demand gNodeB nodes to dynamically cover the selected areas in critical scenarios (e.g., public safety, and natural disasters), which typically require considering additional aspects.  
Writing-original draft preparation, P.S.; software, P.S., M.S.; formal analysis, P.S., M.S.; visualization, P.S.; writing-review and editing, J.H., M.S.; supervision, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Brno University of Technology Program under Grant BD182018001.  
Research described in this paper was financed by the Brno University of Technology Program under Grant BD182018001. For the research, infrastructure of the SIX Center was used.  
The authors declare no conflict of interest. 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  
Figure 8: Selected gNodeB deployment with the new requirements, the numbers in the circles indicate the number of gNodeB nodes in the particular location.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 2 Related Work
### Backdoor Attacks in Remote Sensing  
Researchers have mostly used traditional, natural image-based backdoor attacks, and defenses in the remote sensing domain. [PERSON] et al. were the first to explore the susceptibility of remote sensing domains to backdoor attacks [46]. They utilized the UC-Merced [47] dataset and a road quality image dataset [48]. They successfully achieved a high attack success rate with BadNets-type attacks by planting a backdoor pattern of \(25\times 25\) white pixels in the corner of the input image and changing the label to a target label. Their work demonstrated how backdoor attacks could seriously compromise and threaten the safety of remote-sensing AI models. They also found that two defense approaches, Neural Cleanse and Activation Clustering, were not always effective in identifying and mitigating backdoor attacks. [PERSON] et al. proposed a wavelet transform-based attack, known as WABA, in remote sensing datasets [49]. They injected a trigger image into the low-frequency components of the image to achieve invisibility. They first decomposed both benign and poisoned images into wavelet coefficients and then blended the trigger image's wavelet coefficient with the benign image. [PERSON] et al. developed a triggerless backdoor attack scheme where they injected a backdoor pattern into a multi-UAV system's offloading policy, increasing the computational burden on the UAV system, exhausting computational resources, and undermining observation systems [50].
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 2 Materials and Methods  
In order to explore new possibilities of GNSS-R systems such as different polarizations, complex (amplitude and phase information) reflection coefficients, shorter integration times, and/or statistical analysis of the observables, a GNSS-R-like system was developed by UPC which included on the reception side a dual-polarization (left and right hand circular polarizations, or, in short, LHCP and RHCP) down-looking antenna to pick the reflected GNSS signals, and a RHCP up-looking antenna to pick the direct signal. On the transmission side, several RHCP down-looking antennas created 3 different elevation angles (30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\)) at azimuth 0\({}^{\circ}\), and 3 different azimuth angles (30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\)) at an elevation of 30\({}^{\circ}\). In this study, only the azimuth 0\({}^{\circ}\) results are presented. The study included the analysis of the received signal intensity at both polarizations (LHCP and RHCP) looking for polarimetric signatures, and/or the instantaneous phase difference between them.  
The following sections describe the GNSS-R system, and the experiment in detail.
## 5 Experiment
### Results
#### 5.4.4 WebQSP and CWQ Dataset Analysis  
WebQSP and CWQ are analyzed together, given that CWQ extends WebQSP with enhanced complexity. These two datasets present substantial challenges due to three primary factors: the linguistic intricacy of natural language questions, the expansive scale of the underlying knowledge graph, and the increased reasoning hops required for accurate answers. As shown in Table 8, comparative experiments reveal that all models exhibit a markedly lower performance for WebQSP and CWQ compared to MetaQA and PathQuestion. Notably, the DRKG and ROG frameworks, both leveraging LLMs as their foundation, significantly outperform traditional embedding-based methods. However, their methodologies critically diverge: ROG relies on KG-driven query plan generation, while the DRKG emphasizes semantic query plan generation directly from natural language input. This distinction enables the DRKG to achieve superior scalability and effectiveness in large-scale knowledge graph scenarios, where semantic alignment with complex questions becomes paramount.
# Extracellular Vesicles for Regenerative Medicine Applications
## 2 Extracellular Vesicles (EVs)  
EVs are lipid-bound vesicles produced by cells and secreted into the extracellular space [10]. EVs have been shown to transfer functional cargo (including lipids, proteins, and nucleic acids) to recipient cells whereby they influence cell function and phenotype [11]. EVs have been shown to be involved in development, tissue homeostasis, and wound healing, and have also been shown to be dysregulated during disease progression [12; 13]. There are five major subclasses of EVs currently identified: exosomes, microvesicles, apoptotic bodies, biomineralization vesicles, and matrix-bound nanovesicles (Figure 1). The cargo of EVs includes intraluminal proteins and nucleic acids as well as the lipids contained within the membrane of these vesicles. While all five subclasses share overlapping similarities in the types of cargo they contain, the biogenesis, size, specific cargo, and biological function of these subclasses are distinct from each other. The characteristics and functions of these five subclasses are reviewed below.  
Figure 1: **Major subclasses of extracellular vesicles.** The five major subclasses of extracellular vesicles used in tissue engineering and regenerative medicine include exosomes, microvesicles, matrix-bound nanovesicles (MBVs), biomineralization vesicles, and apoptotic bodies. Size, cargo, and biogenesis of each vesicle subclass are illustrated. Abbreviations; ESCRT: endosomal sorting complexes required for transport machinery; TSG101: tumor susceptibility gene 101; HSC70: heat shock cognate 71 kDa protein; HSP90 beta: heat shock protein 90 kDa beta; CD: cluster of differentiation; TNAP: tissue-nonspecific alkaline phosphatase; ENPP1: ectonucleotide pyrophosphate; phase/phosphodiesterase; MBV: Matrix-bound Nanovesicles.
## 2 Materials and Methods
### Kinetic Studies  
Three non-linear models, the pseudo first-order [29], pseudo second-order [30], and Elovich models [31], were used to describe the kinetic behavior of the sorption process.  
The equation of the pseudo first-order model can be written as:  
\[q_{t}=q_{e}\Big{(}1-e^{-k_{1}t}\Big{)} \tag{4}\]  
where \(k_{1}\) (min\({}^{-1}\)) is the pseudo-first-order rate constant, \(q_{t}\) (mg/g) is the amount of Zn ions adsorbed at time \(t\) (min), and \(q_{e}\) (mg/g) is the amount of Zn ions adsorbed at equilibrium [29].  
The pseudo-second-order equation is:  
\[q_{t}=\frac{q_{e}{}^{2}k_{2}t}{q_{e}k_{2}t+1}. \tag{5}\]  
where the parameter \(k_{2}\) is the pseudo-second-order rate equilibrium constant (g/mg min) [30].  
The non-linear form of the Elovich equation is:  
\[\mathrm{q}=\frac{1}{\beta}\ln\left(v_{0}\beta+\frac{1}{\beta}Int\right), \tag{6}\]  
where \(\beta\) (g/mg) is the desorption constant and \(v_{0}\) (mg/g min) is the initial sorption rate [31]. Kinetic parameters were obtained using non-linear regression with OriginPro 2018.  
For both the kinetic and isotherm models, the residual root mean square error (RMSE) was determined to evaluate the fit of the isotherm equations to the experimental data. The smaller the error function value, the better the curve fit. The calculated expressions of error function can be defined as follows:  
\[\mathrm{RMSE}=\sqrt{\frac{1}{n-p}\sum_{i=1}^{n}\left(q_{e(exp)}-q_{e(calc)} \right)^{2}}, \tag{7}\]  
where \(n\) is the number of experimental data points, \(p\) is the number of parameters in the isotherm model, and \(q_{e(exp)}\) (mg/g) and \(q_{e(calc)}\) (mg/g) are the experimental and calculated values of sorption capacity in equilibrium, respectively.