esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Big Data Frameworks:
## 5 Comparative Analysis of Big Data Frameworks
### Feature Vector Related to Performance  
This feature vector includes performance metrics and those parameters that have a significant impact on the performance of a big data framework. This includes latency  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Hadoop** & **Spark** & **Storm** & **Samza** & **Flink** \\ \hline Architecture & master-slave & master-slave & master-slave & publish-subscribe & master-slave \\ Model & master-slave & stand alone, YARN, & YARN, & stand alone, YARN, & stand alone, YARN, \\ Resource Manager & YARN & YARN, Mesos & Mesos & Mesos & Mesos \\ Storage & HDFS & HDFS, HBase, & HDFS & HDFS & HDFS, streams \\  & & Hive, Casandra & default, isolation, & & & \\ Scheduling & Fair, FIFO, & FIFO, Fair & default, isolation, & & \\  & Capacity & FIFO, Fair & multitenant, & & \\  & & & resource aware & & \\  & & Password based & & & \\ Security & Kerberos & shared secret & Kerberos & & Kerberos and \\  & authentication & configuration, & authentication & no built-in security & TLS/SSL \\  & protocol & Access Control & protocol & & authentication \\  & & Lists (ACLs) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of big data frameworks with respect to architectural features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Hadoop** & **Spark** & **Storm** & **Samza** & **Flink** \\ \hline Execution Format & batch only & batch and stream & stream only & stream only & batch and stream \\ Data Processing Model & MapReduce & DAG & Topology & DAG of operators & data flow graph \\ Processing Mode & batch processing & micro-batching & micro-batching & native streaming & native streaming \\ In-memory processing & No & Yes & Yes & yes & yes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of big data frameworks with respect to feature vector related to data processing.  
that defines how quickly a data item can be processed, throughput and scalability. The desired value of latency is as low as possible and the desired value for throughput is as high as possible. Scalability defines the ability of a system to adapt to change in workloads by involving additional resources. Scalability can be achieved either by making existing resources stronger and faster so that they can efficiently handle increased workload (referred as scale-up) or adding more resources in parallel to spread out the increased load (referred as scale-out). Comparison of big data frameworks with respect to performance related features is shown in Table 6.
## 4 Discussion
Addressing the diverse needs of people with stroke, including responding to spasticity, cognitive changes, fatigue, pain, technology literacy, and accessibility are essential to enabling participation in robotic upper-limb rehabilitation [70; 71; 72; 73]. However, personalization to individual needs was only completely reported in three robotic intervention groups ('_Personalisation_--_Needs_', 11%). Similarly, personally meaningful rehabilitation is essential to the individual's enjoyment and satisfaction from engaging with robotics [59; 69; 72; 74]. Yet, personalization to preferences was entirely absent ('_Personalisation_--_Preferences_', 0%), with no interventions reporting the ways in which individuals' interests, goals, life roles, choice of game or object, inclination toward specific motivational cues, or other preferences were considered. Poor reporting of personalization may perpetuate the use of generic, non-functional tasks that diminish the perceived value of robotic rehabilitation [72]. Alongside personalization, the adoption of complex, novel technologies hinges upon their safe use for a range of individuals [71; 74]. As safety data for robotic interventions is still developing, the lack of planning to monitor their adverse consequences ('_Harms_--_Plan_', 18% complete) and the inconsistent reporting of actual adversity ('_Harms_--_Actual_', 65% consistency) poses a significant limitation, as it risks exacerbating unfavorable attitudes toward health technology and may restrict clinical uptake [75]. Without complete reporting, it is difficult to determine who the intervention is suitable for or how to manage safety risks, making it unsuitable for widespread use [75]. Furthermore, beyond what is 'planned', responsiveness to personalization and harm often occurs through real-time adjustment. Yet, the poor and scattered reporting of robotic intervention '_Protocol deviations_' (11% complete, 25% consistency), impedes visibility over how such adjustments occur to ensure person-centered and safe implementation. Overall, these gaps not only hinder the acceptability and efficacy of upper-limb robotics but also impedes the generation of high-quality evidence needed to advance its use for clinical uptake in stroke rehabilitation.
###### Abstract  
Effective and eco-friendly technologies are required for the treatment of tannery wastewater as its biological toxicity and large volume leads toyground water pollution. Hydrophobic (unmodified carbon felt) and hydrophilic modified carbon felt with Linde Type A zeolite (LTA zeolite) and bentonite were examined for their effects on bacterial attachment, current generation, and tannery wastewater treatment efficiency. Chronoamperometry and cyclic voltammetry confirmed the higher electron transfer obtained with modified anodes. Maximum current densities of 24.5 and 27.9 A/m\({}^{2}\) were provided with LTA zeolite and bentonite-modified anodes, respectively, while the unmodified carbon felt gave a maximum current density of 16.9 A/m\({}^{2}\). Compared with hydrophobic unmodified carbon felt, hydrophilic modified electrodes increased the exploitation of the internal surface area of the 3D structure of the carbon felt by the electroactive biofilm. The study revealed 93.8 \(\pm\) 1.7% and 96.3 \(\pm\) 2.1% of chemical oxygen demand (COD) reduction for LTA zeolite and bentonite, respectively. Simultaneous chromium removal was achieved with values of 94.6 \(\pm\) 3.6 and 97.5 \(\pm\) 2.2 for LTA zeolite and bentonite, respectively. This study shows the potential approach of carbon felt clay modification for the efficient tannery wastewater treatment using bioelectrochemicals systems (BESs) accompanied with high current recovery.  
Keywords:clay; acclimated sludge; LTA zeolite; bentonite; tannery wastewater; bioelectrochemical systems +
Footnote †: journal: Applied Physics Letters
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 5 Evaluation
### Energy Cost  
The energy cost to transmit one bit of user data is the last performance parameter that we examine. We use Equations (13) and (16) to determine the energy cost of both unconfirmed and confirmed transmissions, respectively. We use 3.3 V as the supply voltage of the device [30].  
The energy cost of sending the maximum FRM Payload for every DR with and without ACK is displayed in Figure 15. Note that for a given DR, the average current consumption tends to the sleep state current consumption (\(I_{Sleep}\)) when \(T_{Period}\) grows, but the number of transmitted data bits stays constant. Therefore, longer \(T_{Period}\) results in a greater energy cost, as the energy consumption grows for the same amount of transmitted data. DR9/DR11 outperform DR8/DR10 in terms of energy cost, despite performing similarly in terms of average current consumption, for maximum FRM Payloads. The payload sizes play a key role in this phenomenon: DR9 and DR11 can deliver more data at a time and, consequently, achieve a lower energy cost, since their maximum FRM Payload size is 115 bytes, while DR8 and DR10 can only carry up to 50 bytes for their maximum FRM Payload. However, DR9 and DR11 are still much less efficient than DR5 (although they even outperform DR0 in terms of energy efficiency). Due to its higher bit rate, shorter ToA, and higher maximum FRM Payload size, DR5 is the most energy efficient. For a given DR, the energy cost difference between confirmed and unconfirmed transmission is not negligible, but it follows a very similar tendency across DRs, where the former is slightly less efficient.  
The energy cost differences between sending a 1-byte FRM Payload size and the maximum FRM Payload size are shown in Figure 16. Maximizing the FRM Payload size has a significant impact on the energy cost of data transmission, as the energy consumed per every transmitted bit of data is lower. This behavior also depends on the DR used because greater-bit-rate-capable DRs like DR5 or DR9/11 achieve the best results. Note that the combination of bit rates and higher maximum FRM Payload sizes makes these DRs the most energy-efficient ones. Nonetheless, it should be noted that DR5 outperforms all other studied DRs, achieving nearly two orders of magnitude better performance for the shortest \(T_{Period}\) values. Regarding DR8/DR10, the energy cost decreases up to around 21 times when the payload is maximized, and the decrease is around 31 times for the DR9/DR11 case.
# A Survey of CNN-Based Network Intrusion Detection
## 7 Other Factors of CNN-Based IDS Experiments
### Imbalanced Dataset Handling  
Utilizing an imbalanced dataset to train a model is one of the issues in machine learning-based IDSs. The issue of an imbalanced dataset is frequently resolved by implementing the general synthetic minority oversampling technique (SMOTE). First, from the minority class in the dataset, a data point, x, is randomly chosen. Then, novel points from the minority class are synthesized in this process. Usually, a value of 5 is set for k upon the k neighbors of x being identified. Simple oversampling, in comparison, demonstrates lower performance and robustness than the mentioned method. Additionally, overfitting is effectively decreased by SMOTE [20].  
Another algorithm for addressing an imbalanced dataset is adaptive synthetic sampling (ADASYN). In line with the distribution of minority samples, ADASYN adaptively synthesizes minority samples [44].  
In [29], the imbalance processing module was separated into two portions. The removal of noise samples is focused on by the undersampling technique named the edited nearest neighbor (ENN). The nearest neighbors to every synthetic sample are calculated on the basis of the Euclidean distance. When distinct classes are within the majority of the k-nearest neighbor samples to the synthetic samples, the synthetic samples are removed [29].  
Minority samples were augmented via SMOTE, while the majority samples were diminished through one-side selection (OSS) in one study [49]. Undersampling of the surplus samples was enabled by utilizing a Gaussian mixture model-based (GMM-based) clustering algorithm within the majority class. The labeled sample set was expanded via the production of rare samples following the use of the GMM-based WGAN in the minority class [59].
###### Abstract  
Background: Pes cavus is a multiplanar foot deformity with an abnormal plantar pressure distribution and an overhigh medial longitudinal arch (MLA). Its causes are complex. In the past, people have usually focused on clinical surgery for pes cavus. However, this is not necessarily the best choice for some patients with non-ongoing or mild symptoms. In the 21 st century, studies have just begun to focus on assistive devices intervention for pes cavus, which has been proven to be an effective non-surgical treatment. However, the effectiveness of assistive devices for patients with arched feet of any etiology has not been evaluated and evidence-based guidelines for clinical treatment options are lacking. Methods: A systematic review and network meta-analysis were performed, employing a comprehensive search across the databases of Web of Science, PubMed, as well as Scopus. The selected studies adhered to specific eligibility criteria, which included: (1) involving patients with pes cavus; (2) interventions with assistive devices; and (3) outcome measures of plantar pressure distribution and anatomical characteristics (MLA). Meanwhile, the standard mean difference was selected as the effect size. Results: A total of three studies were selected, and the authors achieved an agreement on the risk of bias with a kappa value equal to 0.74. According to the results of network meta-analysis, customized foot orthotics compared to other devices (lace-up ankle-support brace, semirigid brace) demonstrated the highest likelihood of being the most effective in optimizing plantar-pressure distribution among pes cavus patients. On the other hand, wearing hard custom foot orthotics compared to other devices (soft custom foot orthotics, off-the-shelf orthotics) showed the greatest potential in improving the medial longitudinal arch (MLA) of pes cavus patients. Discussion: Although becoming better than wearing regular footwear, wearing lace-up ankle-support braces or semirigid braces might not be optimal choices for treatments of pes cavus with the potential mechanism that the internal force created by the fixation of the proximal joint might be much less than the ground reaction force loaded on the distal segments that touch the ground. It could be concluded that foot orthotics show great potential in treating pes cavus under non-surgical conditions. This systematic review could provide valuable evidence for future research and clinical practice. Other: The PROSPERO Registration Number is CRD42022349687.  
pes cavus; assistive devices; foot plantar pressure; MLA; network meta-analysis 2023
# Fracture Prediction in Weldox 700E Steel Subjected to High Velocity Impact Using LS-DYNA
## 2 Materials and Methods
### Fracture Model  
The fracture model [29] is used to incorporate the \"fracture\" in the target material for analyzing the fracture behaviour. The \"fracture\" is represented as a scalar parameter to integrate the deteriorating mechanical response under the high-velocity impact. The fracture parameter (\(D\)) represents the void initiation, coalescence, and growth in the material. The \"Element Birth and Death\" technique is used to delete the finite elements for modelling fracture. An element is deleted, and the stress value is set to zero for that element when the calculated value for \(D\) reaches \"unity\". Further, the fracture in the material is calculated when the value of triaxiality exceeds \((-1/3)\)[30]. Additionally, an element is deleted, if the equivalent plastic strain value exceeds a threshold of 10 [26]. The fracture model is also incorporated with the user-defined material model as an external FORTRAN subroutine in LS-DYNA, given as  
\[D=\sum\biggl{(}\frac{de_{eq}^{p}}{e_{t}}\biggr{)} \tag{2}\]  
where \(d\varepsilon_{eq}^{p}\) is an incremental equivalent plastic strain and \(\varepsilon_{t}\) is the fracture strain of ongoing state. The fracture strain is estimated using Johnson-Cook (JC) fracture criteria, given as [27]:  
\[\varepsilon_{t}=(D_{1}+D_{2}\exp(D_{3}\eta))(1+D_{4}\ln\hat{\varepsilon}_{p}^{ *})(1+D_{5}T^{*}) \tag{3}\]  
where \(D_{1},~{}D_{2},~{}D_{3},~{}D_{4}\)_and_\(D_{5}\) are the material constants and \(\eta\) is the triaxiality which is defined as the ratio of mean stress \(\sigma_{m}\) to the equivalent von Mises stress \(\sigma_{eq}\). Mean stress is defined as the average of three principal stresses, i.e., \(\sigma_{m}=(\sigma_{11}+\sigma_{22}+\sigma_{33})/3\) where \(\sigma_{11},\sigma_{22}\)_and_\(\sigma_{33}\) are the direct Cauchy stress components. The value of the room temperature is taken as \(T_{0}=298\) K, and the melting point temperature of the material is \(T_{m}=1800\) K. The reference strain rate is taken as \(\hat{\varepsilon}_{0}=5\times 10^{-4}\) s\({}^{-1}\).
## 4 Simulation Results
### Online Test Campaigns: Performance Evaluations  
In this phase, a driver replicates some reference cornering tests using the above-described RT driving simulator through the HiL interface system. To assure reliable comparative metrics, reference closed-loop maneuvers are repeated supposing four scenarios, in which the vehicle model is alternatively implemented with different controlling techniques:  
* **Non-Controlled Scenario**: in this case, the vehicle is driven without the assistance of any kind of lateral stability controller. Thus, it was possible to establish inner vehicle maneuverability and test the driver's capabilities. Many free driving tests are conducted to identify the limit of controllability;
* **Commercial-Controlled Scenario**: tests are conducted with the _Continental GmbH_ proprietary ESP controller. The strategy, from our side, is completely unknown. However, we can make some assumptions, supposing the control technique is advanced and represent the SoA of the industrial ESP solutions, which is actually implemented on different vehicles in the market.
* **LQR-Controlled Scenario**: the _Linear Quadratic lateral Tracking Control_ is tested, thanks to the RT co-simulation capabilities of the virtual environment, between _MATLAB Simulink_ and _VI-Grade_;
* **SMC-Controlled Scenario**: here, the _Sliding Mode Lateral Stability Controller_ is investigated, exploiting the same control rig from the previous scenario (ESP in _MATLAB Simulink_ and vehicle model in the _VI-Grade_).  
The investigated reference maneuvers are repeated many times, selecting the results which appear more comparable. Indeed, to ensure reliable comparative metrics it is fundamental to reproduce the close-loop test as similar as possible. Selected cornering tests are summarized in Table 1.  
However, boundary conditions specified by the related standards are modified to guarantee more severe conditions in terms of stability performances. This is useful to highlight the results [43]. Indeed, due to the excellent handling capabilities of the vehicle model, using the reference operative scenario produce no unstable behavior, making it difficult to assess the improvements related to the investigated controllers. The main contributions of ESP systems concern the stabilization of vehicle trajectory and desired driving path when unstable Non-Linear (NL) behavior onset.  
To ensure an easy understanding of the results, output plots are presented for LQR and SMC, compared with Non-controlled and Commercial-controlled scenarios.
# Trepenoid Metabolic Engineering in Photosynthetic Microorganisms
## 3 Photosynthetic Microorganisms as Terpenoid Production Hosts
### Eukaryotic Algae  
Microalgae are unicellular, (mostly) photosynthetic eukaryotes and form an enormously diverse group that spans across several branches of the eukaryotic tree of life. Because of their huge diversity, this group of organisms is largely unexplored. However, their terpenoid repertoire is estimated to be vast and diverse [109]. Microalgae are promising candidates as production platforms for high-value product manufacturing in light of several intrinsic advantages. They are eukaryotic. Therefore, they are capable of carrying out complex biological and biochemical functions. They are photosynthetic and, thus, have inexpensive growth requirements. In most cases, algae are resistant to stresses usually associated with industrial cultivation and they are often suitable for industrial production scale-up.  
Recent advances in sequencing technologies, metabolomics, and proteomics have paved the way to the metabolic characterization at the genetic level of representative microalgal species. The development of increasingly efficient genetic toolboxes for model algal species [110; 111; 112] allows targeted genetic manipulations for metabolic characterization and gene discovery as well as metabolic engineering. In addition, the recent development of metabolic networks [113; 114; 115] and genome-scale metabolic models of the main model species [116; 117; 118; 119; 120; 121] have placed those species under the spotlight as promising next generation, solar-powered, cell-sized biofatories. Among many applications, some microalgae species are currently emerging as attractive alternative platforms for the heterologous production of industrially relevant terpenoids.
# Cross-Lingual Named Entity Recognition Based on Attention and Adversarial Training
## 3 Model
### Double Attention  
The common attention mechanism mainly uses the position information of sentence sequences to perform its calculations. The double attention mechanism based on this paper passes the input text sequence context information into either the semantic attention layer or the structural attention layer. Different queries will assign different weights to the contents of the source text, so as to capture the potential information. The semantic attention layer focuses on the inherent semantic information of words, while the structuralist layer tends to focus on the correlation between words, through which the text features can be further extracted.  
The double attention layer is based on the double attention mechanism to process the context vectors obtained from the encoding layer and then learn the deeper semantic information in the text. The double attention mechanism in this layer aims to obtain the weight matrix R by calculating the correlation between each token in the English and Chinese sequences, respectively, and then weighting and summing the normalized weights and the corresponding key values to obtain the final attention representation. Here, the correlation is reflected by the dot product score matrix, and the main role of dot product attention is to learn self-alignment information, i.e., the interaction information of token pairs. The self-attention mechanism captures the internal relevance of text features by comparing the sequence itself to capture the connection between the sequence and the global features, and its simplified structure is shown in Figure 3.  
Figure 3: Self-Attention Structure.  
In order to solve the problem of embedding the context information of the lost word with the word conversion word, this paper introduces the double attention mechanism into the cross-lingual Chinese named entity recognition model. The algorithm makes full use of the features extracted from the pre-trained English named entity recognition model. A double attention mechanism is used to transfer the high-level information of the English model to the Chinese model by drawing on the encoder-decoder attention weight in the Seq2 Seq translation model [19]. The attention weight represents the degree of word correlation between the source language and the target language in the model. Due to the equivalence of word correspondence in the two languages, the information extracted from the pre-trained English named entity recognition model can be translated into the Chinese language by using the attention matrix in reverse. Compared with the method based on shared representation, this model uses the pre-trained English named entity recognition model to extract the task-related information and uses the attention matrix to reverse transform, which results in higher transfer efficiency and less noise [20].