esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of OM Structure and Functions
### OM Proteins and Lipoproteins  
Cell-environment exchanges across the OM are ensured by OM proteins (OMPs) implicated in several functions, such as nutrients uptake, transport, and secretion of various molecules (proteins, polysaccharides, drugs). While integral IM proteins are typically composed of \(\alpha\)-helical bundles [14] the vast majority of OMPs are integral proteins that assume a \(\beta\)-barrel topology [15]. OM-associated proteins are generally lipoproteins that are anchored to the periplasmic side of the OM via a lipid tail attached to an \(N\)-terminal cysteine residue [16]. In _Escherichia coli_ the majority of lipoproteins are OM associated, and only a minor fraction faces the periplasm by IM anchoring. The most abundant lipoprotein in _E. coli_ is Lpp (or [PERSON]'s protein) that anchors the peptidoglycan layer to the OM [17].  
OMPs and lipoproteins are synthesised as pre-proteins in the cytoplasm and then secreted across the IM by the SEC translocase a universally conserved machine that transport unfolded proteins [18].  
Following translocation, lipoproteins are processed into mature forms on the periplasmic side of cytoplasmic membrane where a lipid moiety is attached to the N terminus to anchor these proteins to the membrane. [16]. Lipoprotein sorting has been extensively studied in _E. coli_ where their traffic system is essential for growth [19,20]. Indeed in _E. coli_ lipoproteins play vital roles in OM sorting of OMPs, LPS and lipoprotein themselves [3]. Lipoproteins with OM location are transported by the dedicated Lol system (LolABCDE) composed by five essential proteins. LoICDE constitute an ABC transporter that initiates sorting by mediating the detachment of lipoproteins from the IM and transferring them to the periplasmic chaperone LolA. The water-soluble lipoprotein-LolA complex crosses the periplasm and at the OM LoLA transfers its cargo to LoB for incorporation into the lipid bilayer [20].  
Once secreted across the IM, misfolding of \(\beta\)-barrel OM proteins precursors in the periplasm is prevented by molecular chaperones, such as SurA and Skp [21], which assists OMPs transport across the periplasm and deliver them to the BAM complex a molecular machine driving \(\beta\)-barrel assembly [22]. The Bam machinery is composed by the essential BamA OMP that work in complex and is tightly associated with four lipoproteins partners BamB, BamC, BamD, and BamE. The Bam complex may be viewed as a modular molecular machine in which BamA forms the protein:lipid interface at which OMP substrates enter into the lipid phase of the membrane. BamB interacts with BamA and is proposed to form a scaffold to assist \(\beta\)-barrel folding. BamB, BamC, and BamD interact and form a module suggested to drive a conformational switch in the BAM complex that enables \(\beta\)-barrel insertion into the OM [22].
## 2 Contributions of the Paper
Second, we show that the selection of explanatory variables relying heavily on the proposed I-score method can produce robust prediction performance. The removal of these influential features produces subpar performance along with a reduced I-score. The phenomenon of reduced I-score allows us to compare the explanatory power of any combination of variables. This desired property of the I-score allows us to directly compare any variables or any combination of variables. Furthermore, this makes the I-score the ideal candidate for feature selection and for removing noisy variables in the data. This satisfies the requirement of the second dimension \(\mathcal{D}2\). Third, we propose and implement a novel method of combining features from any combination of variables in order to construct interpretable and explainable convolutional layers to represent the information in image data. This methodology heavily relies on the usage of the I-score, which satisfies all three dimensions \(\mathcal{D}1\), \(\mathcal{D}2\), and \(\mathcal{D}3\). The key novelty of our proposed approach (see the executive design in Figure 2) rests on the collection of features identified by the I-score. Based on the contributions described above, the proposed method is model-free and hence meets the first requirement \(\mathcal{D}1\). It describes a quantifiable measure of how much a combination of explanatory variables impacts the outcome variable, which allows statisticians to perform comparisons and screen for influential features. This phenomenon meets the second requirement \(\mathcal{D}2\). In addition, the statistics, the I-score, provides a measurement for explanatory variables that is directly associated with the predictivity of the explanatory variables on the outcome, variable which satisfies the third condition \(\mathcal{D}3\). With all three conditions (\(\mathcal{D}1\), \(\mathcal{D}2\), and \(\mathcal{D}3\)) satisfied, the design of the proposed architecture produces an interaction-based convolutional neural network (ICNN) that is innately interpretable and explainable. It extracts influential information from the image data and generates explanatory features that directly associate with the predictivity of the data. Because all three conditions are met, the proposed design of CNN is interpretable and it serves as a touchstone in the field of XAI.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Non-Isolated Topologies
#### 3.1.4 Other Non-Isolated Topologies  
In addition to the buck, boost, and buck-boost topologies, other topologies have been discussed in the literature for satellite applications. For instance, in [73], a SEPIC converter was used to implement MPPT for a CubeSat. In contrast to the conventional buck-boost converter, the SEPIC converter provides a positive polarity output and does not have the problem of floating switches during switching phases. Nevertheless, the converter's efficiency is very low under full irradiation conditions. Another study employed a SEPIC converter with a latching relay to implement MPPT in CubeSats [4]. The latching relay helps charge the battery directly from the solar array without MPPT under low-voltage conditions. The experimental results demonstrated the improved reliability of the converter. In [91], a one-switch constant power equalization charger using a multiple-stack buck-boost converter was proposed for the series connection of supercapacitors in satellites. The multiple-stack buck-boost converter can be realized using the SEPIC, Cuk, and Zeta converters. A 25 W prototype of the system using a SEPIC converter showed promising results. The literature does not provide other demonstrations of buck-boost converters, such as the Cuk and Zeta, for satellite applications. A comparison of the non-isolated topologies discussed above, in terms of the switching frequency, component count, type of satellite, and power, is provided in Table 1.
# On the Modeling and Analysis of Brittle Failure in Existing R/C Structures Due to Seismic Loads
## 2 Procedures for Simulating Strength Mechanisms in Substandard R/C Buildings
### Simulation of Inelastic Flexural Response in Columns and Beams  
Flexural behavior of R/C structural elements is associated with ductile member response and materializes only if it can be supported by all other resistance mechanisms developing along the element length. The inelastic flexural response is linked with the formation of extensive cracking, perpendicular to the axis of bending of R/C elements, in regions where the longitudinal reinforcement yields in tension. In columns and beams subjected to earthquake loading, flexural cracking usually forms in both ends, in a length approximately equal to twice the elements' width. By way of contrast, in R/C beams subjected to excessive gravity loading, the yielding of longitudinal reinforcement occurs at locations of maximum values for the developing bending moment.  
Figure 1: Simulation of strength mechanisms that develop within the structural system of substandard R/C buildings. Hatched regions represent cross-sections of members extending normal to the plane of view (e.g., slab and transverse beams).  
Simulation of the inelastic flexural response of R/C elements may be performed using different, alternative approaches that are documented in the international literature. The most preferable approach in simulating inelastic flexural response in R/C elements subjected to biaxial bending is the use of linear elements encompassing fiber discretization of their cross-section [41]. According to this approach, the reinforcing bars plus the confined and the unconfined concrete of an R/C structural element are all modeled as separate fibers, possessing stress-strain relationships that characterize their response under uniaxial loading (see Figure 2). Based on the assumption that plane sections remain plane during member deformation, member stresses and deformations in both section directions are calculated through the integration of the response of the fibers over the element section. For R/C beams under seismic excitation that are primarily subjected to uniaxial bending, an alternative simulation approach is through the use of a lumped plasticity approach which combines a linear element with non-linear hinges at the locations where beams connect with other structural members (columns and/or beams). Inelastic response in each hinge is then defined by appropriate force-deformation relations, derived from sectional analysis of the corresponding element and classical mechanics.
## 5 Experiments and Results
### Baseline Models  
To validate the performance of Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES), we compare it against a range of established baseline models that represent both traditional and state-of-the-art approaches in extractive summarization. These models are selected to cover a variety of methodologies including heuristic-based, machine learning, and deep learning techniques:  
* **LEAD-3** is a heuristic baseline that extracts the first three sentences of a document, based on the assumption that the lead sentences contain key information. It serves as a simple yet effective comparison point.
* **SummaRuNNer**[41] is an RNN-based sequence model that evaluates the salience of sentences based on features extracted across the document to generate a summary.
* **NeuSum**[42] integrates sentence scoring and selection into a single joint model, using an RNN-based encoder-decoder framework to predict the saliency of sentence combinations directly.
* **BanditSum**[43] implements a reinforcement learning approach, treating the summarization task as a contextual bandit problem where the model learns to choose sentences that maximize the ROUGE metric.
* **JECS**[44] utilizes a joint extraction and compression strategy, employing a syntactic transformation of the input text to produce concise summaries.
* **HIBERT**[45] uses a hierarchical bidirectional transformer to encode long documents and perform document-level summarization.
* **BERTSUMEXT**[1, 46] is an extension of BERT for extractive summarization, using pre-trained contextual embeddings to assess sentence importance within a document.
* **NeRoBERTa**[47] is a variant that adapts RoBERTa embeddings specifically for nested hierarchical document structures to enhance summarization.
* **MatchSum**[48] formulates the summarization task as a semantic matching problem between candidate sentences and the document, using a contrastive learning framework.
* **HAHSUM**[49] incorporates a hierarchical attention mechanism with heterogeneous graph representations to refine the summarization process across multiple document levels.
* **GenCompareSum**[50] is a hybrid model that combines unsupervised abstractive techniques with extractive methods, tailored for summarizing long and complex documents, like scientific papers.  
These baselines span a range of approaches and have shown varied degrees of success across different datasets and summarization tasks. By comparing MCHES against these models, we aim to demonstrate its superiority in leveraging complex document structures and semantic relationships for producing high-quality summaries.
## 6 Applications of VSNs
### Industrial Monitoring  
VSN applications for industrial monitoring can be associated with a broad range of methods to increase productivity, energy efficiency, and safety of industrial operations in engineering facilitiesand manufacturing plants. Many companies currently use time-consuming and expensive manual monitoring to predict failures and to schedule maintenance or replacements in order to avoid costly manufacturing downtime. VSN can be inexpensively installed and provide more frequent and more reliable data. The deployment of VSNs can reduce equipment downtime and eliminate manual equipment monitoring that is costly to carry out. Additionally, data analysis functionality can be added to the network, eliminating the need for manual data transfer and analysis. Industrial monitoring can be largely split into the following application fields:  
Process Monitoring and Control: combining advanced energy metering and sub-metering technologies with wireless sensor networking in order to optimize factory operations, reduce peak demand, ultimately lower costs for energy, avoid machine downtimes, and increase operation safety. A plant's monitoring boundary often does not cover the entire facility but only those areas considered critical to the process. Easy to install wireless connectivity extends this line to include peripheral areas and process measurements that were previously infeasible or impractical to reach with wired connections.  
Machine Surveillance: ensuring product quality and efficient and safe equipment operation. Critical equipment parameters such as vibration, temperature, and electrical signature are analyzed for abnormalities that are suggestive of impending equipment failure.  
Supply Chain Management and Asset Tracking: with the retail industry being legally responsible for the quality of sold goods, early detection of inadequate storage conditions with respect to temperature will reduce risk and cost to remove products from the sales channel. Examples include container shipping, product identification, cargo monitoring, distribution and logistics.  
Storage Monitoring: sensor systems designed to prevent releases of regulated substances to ground water, surface water and soil. This application field may also include theft/tampering prevention systems for storage facilities or other infrastructure, such as pipelines.  
From the above application points of view, it becomes apparent that VSNs are a most promising technology that can change our lifestyle. If we can provide a business model then this technology can be easily deployable to the customer.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Radio Stripes
#### 2.3.4 Radio Stripe Network Path Loss  
Even though most mm-Wave propagation concerns have been resolved in radio stripe networks, path loss remains the most significant propagation difficulty, particularly in very high bands (\(f_{\text{C}}>100\) GHz). The omnidirectional free space path loss increases with  
Figure 3: Block diagram of a typical scenario of radio stripe deployment [14].  
frequency according to Friis law (the power received is equal to the square of the wavelength).  
However, the authors demonstrated that path loss may be compensated for with a proportional increase in antenna gain and suitable beamforming. Thus, the mmWave propagation problem in radio stripe networks can be overcome [14].
## 4 Practical Implementation
### Special Risk Groups
#### Other Risk Groups  
With the development of advanced medicine, the new patient group of so-called chronic critically ill patients has emerged and is often treated in the step-down unit. A recent review provides a thorough discussion of the nutritional approach for these patients [101]. The ASPEN guidelines, based on expert consensus, define chronic critically ill patients as having persistent organ dysfunction requiring ICU treatment >21 days and suggest management with aggressive high-protein EN therapy and, when feasible, use of a resistance exercise program [27].  
Patients with renal failure can be placed on a standard enteral formula following the recent ICU recommendations for protein administration [17; 27]. According to the ASPEN guidelines, a special formula might be indicated in case of significant electrolyte or volume abnormalities. Renal replacement therapy may require increased protein support (<2.5 g/kg/day) (grade very low) and adapted micronutrient support [17; 27]. Importantly, protein support should not be restricted in order to avoid or delay initiation of dialysis therapy [27].  
The ESPEN Society provides separate nutritional management recommendations for severe burn patients [102] and for patients with acute or chronic liver failure [103].  
Patients with alcohol abuse are particularly at risk for thiamine deficiency, as are patients with severe sepsis, congestive heart failure, or burn injuries [104]. Recommended daily doses for alcoholic patients are substantially higher than for non-alcoholic patients, reaching up to 500 mg thiamine three times daily [105]. Another review underlines the importance of additive therapies such as correction of electrolyte disturbances and malnutrition [106].
## 4 Results and Discussion
### Raman Scattering
#### 4.1.2 Analysis of Optical Constants in the FIR \(\rightarrow\) NIR Region  
We have constructed the complex dielectric functions \(\widetilde{\varepsilon}_{\rm L}(\omega)\) for both the binary InP, InAs, InSb, and ternary InP\({}_{1-x}\)Sb\({}_{x}\) alloys by using a classical \"Drude-Lorentz\" model. The optical constants are simulated by using Equations (3) and (4) in the frequency range of 400 cm\({}^{-1}\geq\omega\geq 100\) cm\({}^{-1}\) by incorporating the phonons estimated from the RSS measurements (Sections 4.1 and 4.1.1). In Figure 5a-e, we have displayed \(\omega\)-dependent results of R(  
One must note that the polar materials reflect/absorb light in the FIR region due to the interaction of electromagnetic (EM) wave field with the \(\omega_{\rm TO}\) modes and coupling to the \(\omega_{\rm LO}\) phonons. For each binary material, the \(\omega_{\rm TO}\) mode can be seen as a peak in the \((\omega)\), \(\varepsilon_{2}(\omega)\), and R(\(\omega\)) spectra due to resonance interaction with EM wave field while the coupling of \(\omega_{\rm LO}\) modes appeared near n\((\omega)=(\omega),\varepsilon_{1}(\omega)=0\). For the ternary InP\({}_{0.67}\)Sb\({}_{0.33}\) alloy, however, the perusal of Figure 5a,d,e have revealed results of the optical constants providing well-separated InP-like (\(\omega_{\rm LO}\), \(\omega_{\rm TO}\)) modes while the InSb-like phonons fall very close to each other (<3 cm\({}^{-1}\)).  
For all GS-MBE-grown samples, the unresolved FWHM \(\sim\)10 cm\({}^{-1}\) of InSb-like optical mode in InP\({}_{1-x}\)Sb\({}_{x}\) is quite large. For x = 0.48, while the wide separation (see Figure 4) between InP-like (\(\omega_{\rm LO}\), \(\omega_{\rm TO}\)) modes decreases, the split in InSb-like phonons increases but only slightly (\(\sim\)3-4 cm\({}^{-1}<<\) FWHM). As stated before, the experimental results have not been able to identify the impurity vibrational modes (i.e., the gap mode for InP:Sb near x \(\to\) 0 and LVM for InP:P near x \(\to\) 1) in the limiting values of x. Following ATM-GF theory (described in detail elsewhere) [59] with appropriate perturbation models, we have calculated the LVM \(\sim\)295 cm\({}^{-1}\) and a gap mode near \(\sim\)205 cm\({}^{-1}\) (shown by blue color arrows in Figure 4), providing strong confirmation of the \"two-phonon-mode\" behavior.
## 5 Conclusions  
This study evaluated three different stimulus lists to determine their effects on speech accuracy scoring in a small group of highly proficient TE speakers. An analysis of open-versus closed-set scoring was conducted to evaluate the influence of the scoring method on TE speaker assessment. Whole word and phonemic scoring were also compared to determine the sensitivity and clinical applicability of accuracy scoring for TE speakers. Differences between individual speaker accuracy across the three tests were recorded to further comment on the variability of accuracy ratings across a small group of speakers. Finally,the quantity and type of TE speaker errors were measured and analyzed to distinguish voicing and phonemic trends in this group of speakers.  
Scores for each speaker varied across all three stimulus lists, with the highest accuracy scores attributed to the NIT. These results may also provide initial support for the use of the open-set response paradigm, in that more specific, sound-based information may be gathered through transcription; such information may be lost with scoring methods that require forced-choice identification from multiple options. Additionally, based on the current data, listeners provided reliable perceptual judgments of speaker production. Taken together, we would anticipate that phonemic scoring procedures may yield more specific and clinically useful assessment information. While confounding factors, such as word frequency or familiarity, the phonetic construction of stimuli, etc., cannot be fully eliminated [49], efforts were employed to reduce such potential influence on listener responses. Individual speaker variability emerged despite the small sample size and highly proficient rating for all speakers prior to study inclusion. Voicing errors were found to be common amongst TE speakers, with voiced-for-voiceless substitutions accounting for the most common perceptual error. Overall, this study supports the use of open-set, phonemic scoring methods when evaluating proficient TE speaker accuracy.  
P.C.D.: Conceptualization, Study Design and Methodology, Data Curation and Analysis, Writing--original draft and editing. N.G.: Data Analysis, Writing--original draft, review, and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Research Ethics Board of Western University #18588, June 2017.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are unavailable due to privacy and ethical restrictions.  
The authors declare that they have no competing interests, either financial or non-financial, or personal relationships that could influence the conduct or interpretation of the work reported in this paper.