esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards AI-Generated Essay Classification Using Numerical Text Representation
## 3 Natural Language Processing
### Numerical Text Representations
#### 3.1.1 Basic Numerical Text Representation Methods  
The fundamental technique is to create a text representation in the form of a vector, where each word is a key, and its value is the number of its occurrences in the document. This representation is known as Bag of Words (BoW) [16]. However, BoW has certain limitations because it does not consider the order of words or their context. Moreover, it focuses solely on the number of occurrences, which can assign a greater weight to frequently occurring words despite their low semantic significance [17].  
Another similar technique is the one-hot encoding of tokens, which uses binary values to indicate the presence of the word [18]. Both the Bag of Words approach and one-hot encoding represent each word by a vector whose length corresponds to the size of the vocabulary, i.e., the list of all unique words in the corpus. The key difference between these methods is how non-zero elements are recorded in the vectors. The bag of words representation counts the number of occurrences of words in a document, while one-hot encoding indicates whether a given word appears in the document through binary values [18]. A more refined method for numerical text representation is using TF-IDF weights (term frequency-inverse document frequency). This method assigns high weight to words that frequently appear in a specific document but not across many documents in the corpus. If a particular word occurs with high frequency in a document while rarely appearing in the entire corpus, it is likely very descriptive of the document's content. This mitigates the issue of assigning high weights to insignificant but frequently occurring words, a problem in the Bag of Words technique [8].  
_TF_ (term frequency) is the number of occurrences of a term (word) in a document, usually divided by the total number of words in the document [8]:  
\[TF(t,d)=\frac{x}{y} \tag{1}\]  
where:  
* \(x\)--the number of occurrences of the word \(t\) in the document \(d\),
* \(y\)--the number of words in the document \(d\).  
_IDF_ (inverse document frequency) measures the uniqueness of a word in the context of the entire corpus:  
\[IDF(t,d)=\log\left(\frac{N}{n}\right) \tag{2}\]  
where  
* \(N\)--the total number of documents in the corpus,
* \(n\)--the number of documents in which the word \(t\) appears.  
_TF-IDF_ weight is the product of the above measures:  
\[TF-IDF=TF\cdot IDF \tag{3}\]  
All of these methods share one common feature: They generate sparse matrices. A sparse matrix is characterized by the fact that most of its elements are zeros. This typically results from an extensive set of words in the vocabulary, leading to the creation of high-dimensional vectors, many of which contain no information about the given text. This can be particularly problematic with large datasets, as it significantly increases memory load [18].
# A Survey on Mobility Support in Wireless Body Area Networks
## 3 Mobility Model and Communication Protocol
### Group Mobility
#### 3.2.2 Protocols for General Mobility
As for the handover issue, dynamic channel allocation problem is essential for mission-critical applications; therefore, interference mitigation is important to minimize power consumption and increase the reliability of the system. Thus, [PERSON] et al. [50] proposed the prediction based algorithm through Smart Channel Assignment (SCA) techniques for unknown dynamics. To accommodate variations in link quality and network topology caused by handover, various mobility scenarios amongst coexisting WBAN were analyzed.
# Spectrum Demand Forecasting for IoT Services
## 4 Spectrum Forecasting Model
### Required Spectrum  
Calculating the required spectrum is now straightforward: dividing the required capacity by the area spectral efficiency. Particular care should be taken in two aspects:  
* The majority of IoT services need mostly (or sometimes even exclusively) spectrum for the uplink. A few services may need exclusively spectrum for the downlink. If a service is bidirectional (i.e., both directions actively send data and not just some control information), the calculation of the required capacity should be done twice and independently: one for the uplink and one for the downlink.
* The spectrum is not only needed for connecting the device to a gateway. Many of the IoT services also include a wireless transport (backhaul) link. It is very likely that this transport link will be provided by a cellular network, but it also needs to be calculated because it will be an additional spectrum need.  
The spectrum requirement is calculated adding non-cellular (\(C_{i}\)) and cellular (\(C_{i}^{q}\)) required capacity:  
\[F_{i}^{x}=\frac{C_{i}}{\bar{\eta}_{w,i}}+\frac{C_{i}^{q}}{\bar{ \eta}_{w,i}^{q}}, \tag{7}\]  
where \(\bar{\eta}_{w,i}\) is the average link spectral efficiency (in bits/s/Hz) and \(\bar{\eta}_{w,i}^{a}\) is the average area spectral efficiency (in bits/s/Hz/ha) for the \(i\)-th scenario. If the scenario has no share for cellular service, then \(C_{i}^{q}=0\).  
Additionally, the transport backhaul link spectrum (in case of a wireless link) should be calculated for the traffic that uses a wireless transport link (\(C_{i}^{T}+C_{i}^{T,a}\)) as:  
\[F_{i}^{T}=\frac{C_{i}^{T}+C_{i}^{T,a}}{\bar{\eta}_{w,i}^{T}}. \tag{8}\]
## 7 Conclusions and Future Work  
In this paper, an enhanced algorithm for sensor node localization based on improved DV-Hop and DE Algorithms for WSNs is proposed, namely DEIDV-Hop. From evaluations and analysis, it is observed that the average distance per hop of anchor nodes is improved, confirming its advantages and efficacy.  
In the localization estimation phase, the DE algorithm is introduced to locate unknown nodes, further reducing the localization error. To solve the problems of early maturity, low convergence, and low efficiency, the individual variation and cross operation formulas are improved. In the mutation operation, an attempt to increase the diversity of the population and the global search ability, the mutation vector is randomly mutated. Moreover, the social learning part of PSO is introduced in the crossover operation, and the optimization efficiency of PSO is improved by using the heuristic information carried by the optimal group value. Simulation results of the four network topology environments show that the average localization error of nodes can be effectively reduced by the DEIDV-Hop algorithm. Without the need for additional hardware, it shows excellent advantages and is effective. The simulation results show that the DEIDV-Hop algorithm has the fastest convergence speed and is the most stable. So far, a two-dimensional node location is the constraint to discussions in this investigation. The way of applying the algorithm to conduct node location in a more sophisticated space-three-dimensional node location is a direction to consider in terms of future research. In addition, based on the existing research, how to accurately apply the improved routing algorithm to the DV-Hop algorithm and make appropriate improvements to the routing algorithm to reduce the communication overhead of wireless threaded sensor networks and extend the life cycle of wireless sensor networks should be considered.  
Conceptualization, Methodology, and Investigation, D.H. and Y.Y.; Validation and Data Curation, Y.Y. and K.-C.L.; Formal Analysis, Y.Y. and D.H.; Investigation, Writing--original draft preparation, Y.Y. and D.H.; Writing--supervision, review, and editing, K.-C.L. and R.F.d.M. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (No. 61672338 and No. 61873160).  
The authors declare no conflict of interest.
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 5 Implementing the Proposed Model
### Data Retrieval and Analysis  
Effective data retrieval and analysis are essential in leveraging the benefits of the treatment cycle-based data tagging model. Advanced query functions enable healthcare providers to filter and access patient data based on treatment cycle identifiers, facilitating precise and relevant data retrieval. Clinicians can easily track a patient's treatment history and analyze outcomes across different treatment cycles, with clear distinctions between different treatment cycles maintained by the system via the anticipated queries and analytical tools.  
**Query Functions:** The EMR system incorporates advanced query functions that allow healthcare providers to filter and retrieve patient data based on treatment cycle identifiers. In this case, the system can provide the required data through various queries like the following:  
SELECT * FROM Patient Records WHERE Patient ID = 12004 AND Treatment Cycle = 'Cycle 1'.  
**Reporting and Analytics Tools:** Reporting tools are developed to generate insights and reports based on treatment cycle data, helping clinicians to analyze the treatment efficacy across different cycles [56]. Cyclical data tagging facilitates pattern recognition and trend analysis, enabling data-driven insights for improved healthcare outcomes and resource allocation. Tools like SQL 2019, Python 3.10 (with libraries like Pandas 1.5), or specialized healthcare analytics platforms (e.g., Tableau 2023.1 or Power BI) can extract insights from tagged data. For instance, analyzing treatment cycle data can reveal optimal treatment pathways or identify patient subgroups that respond better to specific therapies.
## 3 Reservoir-Scale Simulations
### Coupled Flow-Geomechanical-Geochemical Processes
For example, [PERSON] and [PERSON] [127] investigated this matter via reservoir simulations which involved setting different (porosity and permeability) heterogeneity levels, thicknesses, and numbers of inter-layers to evaluate the effectiveness of such inter-layers in terms of preventing the upwards migration of CO\({}_{2}\) in both the short and long term. Based on the simulation results, the behaviour of the CO\({}_{2}\) plume with the presence of inter-layers was very different compared to more homogeneous reservoirs with very thick caprocks. For instance, counter-intuitively, a thinner (0.3 m) shale inter-layer was found to act as a more effective local seal than a thicker (3 m) inter-layer when all coupled processes, including geochemical, geomechanical, and petrophysical heterogeneity; hysteresis; capillary pressure; and diffusion, were included in one model. This odd leakage pattern occurred because the large change in stresses due to the injection process led to a larger increase in the vertical displacement within the thick inter-layer compared to the thin inter-layer, which contributed to the diffusive loss by enhancing the layer surface area. Additionally, the sandstone reservoir porosity increase was also larger (in the case with the thicker shale interlayer) due to the large pressure difference between the initial reservoir and the pore pressure, and this led to large CO\({}_{2}\) accumulation under the ultra-low-permeability thick inter-layer, compared with the thinner inter-layer. This large accumulation resulted in significant capillary breakthrough pressure within the thick inter-layer, allowing more scCO\({}_{2}\) to enter the inter-layer, which was initially saturated with brine. As a result, more extensive chemical processes occurred within the thick inter-layer, such as a change in pH, higher solubility of CO\({}_{2}\) and lower brine salinity, and also faster reactive calcite dissolution, along with other minerals. All of these changes led to an enhancement in the levels of dissolved CO\({}_{2}\) at the reservoir/inter-layer boundary, which, in turn, increased the diffusive loss via the thick inter-layer. This ultimately led to 20% of the CO\({}_{2}\) leaking, via the thick inter-layer, to the overburden, which was substantially higher leakage than that which occurred for the thinner layer (as shown in Figure 11). This study showed how the inclusion of a fuller complement of coupled physical processes in the simulation can lead to unexpected findings that might otherwise not be predicted using simpler models. Flow, geochemical, and geomechanical processes can be further coupled to heat flow and thermal effects. However, these latter processes only tend to be of importance when large temperature gradients and/or high temperatures are involved [122], and many injection scenarios are close enough to isothermal to neglect thermal effects.
###### Abstract  
Ferrous sulfate (FeSO\({}_{4}\)) and calcium polysulfide (CaS\({}_{5}\)) stabilization are practical approaches to stabilizing hexavalent chromium (Cr(VI))-contaminated soil. The leachability and stability of Cr(VI) and Cr are important factors affecting the effectiveness of stabilized Cr(VI)-contaminated soil. This study compared the leachability and stability of Cr(VI) and Cr in Cr(VI)-contaminated soil stabilized by using FeSO\({}_{4}\) and CaS\({}_{5}\). The contaminated soil was characterized before and after stabilization, and the effectiveness of FeSO\({}_{4}\) and CaS\({}_{5}\) stabilization was assessed using leaching, bioaccessibility, alkaline digestion, sequential extraction, and X-ray diffraction tests. Results showed that FeSO\({}_{4}\) and CaS\({}_{5}\) significantly reduced the leachability and Cr(VI) content in the contaminated soil. The acid-buffering capacity and stability (leachability, bioaccessibility, speciation distribution, and mineral composition) of the Cr(VI)/Cr and Cr(VI) content of CaS\({}_{5}\) were better than those of FeSO\({}_{4}\). This study demonstrated that CaS\({}_{5}\) had a better effect than FeSO\({}_{4}\) on the stabilization of Cr(VI) in Cr(VI)-contaminated soil. The CaS\({}_{5}\) significantly enhanced the stabilization and immobilization of Cr(VI) and reduced its leachability and toxicity.  
hexavalent chromium; contaminated soil; leachability; stability; speciation +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Definition of the Survey-Based Questionnaire  
The survey-based questionnaire was designed with the aim of providing an answer to the research question. Surveys allow for fast and easy gathering of data from responders. Specifically, a close-ended questionnaire was developed [52] for obtaining quantitative data and to facilitate the following analysis. The contents and elements of investigation were derived starting from the scientific literature and adding further details and concepts based on the knowledge and experience of practitioners and authors, as in [53]. The questionnaire was structured with a five-point Likert scale, which enables respondents to answer questions by scaling responses from the lowest level (1) to the highest (5), including the possibility of a neutral response (3), as suggested by [48]. Also, in some cases, where required by the question, the answers were designed with a Boolean structure (\"yes\" or \"not\") or a multiple-choice structure. Moreover, several open-ended questions were included to better contextualize responses and gather more qualitative information on SMEs and customers' needs and expectations. Therefore, open-ended questions were not included in the quantitative analysis of responses. The questionnaire includes a first section of 5 questions aiming to gather information on the responding SMEs. After that, a second section of 32 questions investigates the topic of analysis, SMEs needs, preferences, and expectations. The questionnaire is therefore composed of 37 questions in total, and the expected time for its completion is estimated to be about 20 min. All the items of the questionnaire were designed collaboratively such that they could provide a market analysis in different countries. Indeed, starting from interesting tips and attributes suggested in the literature, through focus group sessions, the authors shared their ideas with respect to the contents of the items as well as considering the potential interpretations and different interests related to each different country. The questionnaire was developed in English, through an iterative and collaborative approach among the authors. This allowed for a cyclical process of proposal of questions, refinement, and validation [48]. Personal information of respondents was not requested or collected to ensure protection of privacy. The content of the questionnaire is presented below in Table 1.
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
###### Abstract  
Increasing environmental concerns and legal regulations have led to the development of sustainable technologies and systems in logistics, as in many fields. The adoption of multi-echelon distribution networks and the use of environmentally friendly vehicles in freight distribution have become major concepts for reducing the negative impact of urban transportation activities. In this line, the present paper proposes a two-echelon electric vehicle routing problem. In the first echelon of the distribution network, products are transported from central warehouses to satellites located in the surroundings of cities. This is achieved by means of large conventional trucks. Subsequently, relatively smaller-sized electric vehicles distribute these products from the satellites to demand points/customers located in the cities. The proposed problem also takes into account the limited driving range of electric vehicles that need to be recharged at charging stations when necessary. In addition, the proposed problem considers time window constraints for the delivery of products to customers. A mixed-integer linear programming formulation is developed and small-sized instances are solved using CPLEX. Furthermore, we propose a constructive heuristic based on a modified Clarke and Wright savings heuristic. The solutions of this heuristic serve as initial solutions for a variable neighborhood search metaheuristic. The numerical results show that the variable neighborhood search matches CPLEX in the context of small problems. Moreover, it consistently outperforms CPLEX with the growing size and difficulty of problem instances.  
routing; two-echelon electric vehicle routing problem; variable neighborhood search; large neighborhood search; Clarke and Wright savings +
Footnote †: journal:
## 14 Conclusions and Perspectives for Future Research
* Self-supervised learning: Developing self-supervised or unsupervised training paradigms to reduce reliance on paired high-resolution ground truth, expanding applicability to rare or novel clinical scenarios. * Adaptive acquisition: Implementing real-time adaptive sampling schemes where the MR and PET acquisition patterns are dynamically steered during scanning based on intermediate reconstructions and uncertainty estimates. * Vision Transformers and large-scale pretraining: Integrating Vision Transformers (ViTs) and hybrid CNN-Transformer architectures to better capture long-range dependencies and cross-modality anatomical correlations. * Real-time clinical deployment: Optimizing the pipeline for clinical deployment through model pruning, quantization, and FPGA/TensorRT acceleration, aiming for near real-time reconstruction on modern scanner hardware. * Cross-domain generalization: Enhancing robustness to variations across scanner vendors, acquisition protocols, and patient populations through domain adaptation and transfer learning. * Explainability and uncertainty quantification: Embedding explainable AI tools and Bayesian uncertainty estimation to provide radiologists with interpretable and trustworthy reconstructions. * Expansion to whole-body imaging: Extending the methodology to other anatomical regions, including cardiac, abdominal, pelvic, and whole-body MR-PET, where motion and sparsity challenges are even more severe. * Multi-tracer PET imaging: Investigating the applicability of the framework to multi-tracer PET protocols, leveraging joint sparsity across multiple functional channels.
## 4 Conclusions  
A novel perovskite La\({}_{2}\)ScZnO\({}_{5.5}\) and doped phases La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\), La\({}_{2}\)Sc\({}_{0.9}\) Ca\({}_{0.1}\)ZnO\({}_{5.45}\), and La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\) were obtained. An analysis of the effect of doping on the electrical properties was carried out. It was established that the introduction of small dopant concentrations leads to an increase in conductivity due to an increase in oxygen vacancies concentration. The conductivities increased in the order of La\({}_{2}\)ScZnO\({}_{5.5}\) < La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\) < La\({}_{2}\)Sc\({}_{0.9}\)Ca\({}_{0.1}\)ZnO\({}_{5.45}\) < La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\) with an increase in the free volume of migration. All of the phases are capable of incorporating water. The proton concentration increased in the order: La\({}_{2}\)ScZnO\({}_{5.5}\) < La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\) < La\({}_{2}\)Sc\({}_{0.9}\)Ca\({}_{0.1}\)ZnO\({}_{5.45}\) \(\approx\) La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\). It was established that for the samples La\({}_{2}\)ScZnO\({}_{5.5}\) and La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\), full filling of oxygen vacancies by water was not reached, but the samples La\({}_{2}\)Sc\({}_{0.9}\)Ca\({}_{0.1}\)ZnO\({}_{5.45}\) and La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\) exhibited a \(\sim\)90% hydration limit, i.e., almost filling the concentration of the oxygen vacancy. It was found that the conductivities in wet air increased in the same order: La\({}_{2}\)ScZnO\({}_{5.5}\) < La\({}_{2}\)Sc\({}_{0.9}\)Mg\({}_{0.1}\)ZnO\({}_{5.45}\) < La\({}_{2}\)Sc\({}_{0.9}\)Ca\({}_{0.1}\)ZnO\({}_{5.45}\) < La\({}_{1.9}\)Ca\({}_{0.1}\)ScZnO\({}_{5.45}\).  
Thus, the method of creating novel oxygen-deficient perovskite La\({}_{2}\)ScZnO\({}_{5.5}\) was successfully performed. The introduction of zinc makes it possible to obtain high-density ceramics while maintaining a significant level of ionic (O\({}^{\text{$\sum$}}\)-, H\({}^{+}\)) conductivity, which is an important requirement for fuel cells. Moreover, reducing the scandium content by the introduction of zinc will significantly reduce the cost of the materials for solid electrolytes.  
The authors declare no conflict of interest.