esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The study aims to implement a high-resolution Extended Elastic Impedance (EEI) inversion to estimate the petrophysical properties (e.g., porosity, saturation and volume of shale) from seismic and well log data. The inversion resolves the pitfall of basic EEI inversion in inverting below-tuning seismic data. The resolution, dimensionality and absolute value of basic EEI inversion are improved by employing stochastic perturbation constrained by integrated energy spectra attribute in a Bayesian Markov Chain Monte Carlo framework. A general regression neural network (GRNN) is trained to learn and memorize the relationship between the stochastically perturbed EEI and the associated well petrophysical log data. The trained GRNN is then used to predict the petrophysical properties of any given stochastic processed EEI. The proposed inversion was successfully conducted to invert the volume of shale, porosity and water saturation of a 4.0 m thick gas sand reservoir in Sarawak Basin, Malaysia. The three petrophysical geobodies were successfully built using the discovery wells cut-off values, showing that the inverted petrophysical properties satisfactorily reconstruct the well petrophysical logs with sufficient resolution and an accurate absolute value at the well site and are laterally conformable with seismic data. Inversion provides reliable petrophysical properties prediction that potentially helps further reservoir development for the study field.  
petrophysical; reservoir; seismic; inversion; stochastic; neural network; regression; high resolution Article  
Predicting Reservoir Petrophysical Geobodies from Seismic Data Using Enhanced Extended Elastic Impedance Inversion
## 2 Challenges, Trends and Constraints in WSAN Hardware Design
### Open-Source-Hardware Architectures versus Open-Architecture Systems
#### 2.3.3. Firmware Development Constraints  
1. _Lack of code optimization:_ The application scenario that is hosted and running in the program memory of the main-board's MCU, also called as firmware, normally ought to be optimized and tidily developed, so as to ensure the reliability of the ultimate hardware system. In the case of the Arduino-like expandable architectures, the firmware development is mainly implemented into the particular IDE of the hardware vendors. Whilst such IDEs provide many development facilities to the engineers through the use of extensive ready-made APIs or project templates, they produce firmware that is far from being optimized. For instance, the firmware for just toggling a single LED indicator may involve several kilobytes of program code. Every single line of code in the firmware, when it is executed by the processor, consumes a portion of the available system's energy. In battery-operated WSAN systems, such in the case of remote agricultural applications, wordy firmware is a well-hidden source of energy wastage. Thus, the ease of firmware development is counteracted by the excess in energy consumption. Today, there are programming tools solutions for the open-source developers that can help to the production of efficient and optimized code (e.g., mbed IDE). Hence the key point is the firmware developers to start thinking about the energy effectiveness.
2. _No provision for multi-MCU development:_ According to the existing OSEP architectures, the application scenario of the system is solely developed with the MCU of the main-board. The OSEPs' IDEs support only one MCU per application. The concept of multi-MCU aspect is totally absent from the design strategies.
3. _Evanescence of the hardware realm:_ The trend of open-source to use ready-made pieces of firmware or even complete projects from the developers' community is very catty, because the hardware details are totally ignored, or in the best cases, they are partially acknowledged.
## 6 Conclusions  
In order to address the problem of long range outdoor VLC, a new VLC receiver design is herein proposed. Thus, unlike in other works, the VLC sensor is based on a logarithmic transimpedance circuit instead of a linear transimpedance circuit.  
The experimental evaluation demonstrated that this solution provides the VLC receiver with enhanced resilience to noise, an extended dynamic range, and it significantly reduces the possibility of photoelectron saturation. Therefore, in order to prevent the photodiode saturation, the logarithmic transimpedance circuit reduces its sensibility as the power of the incident light increases. So, from this point on, the communication performances are determined by the ability of the signal processing plan to properly reconstruct the data signal.  
The experimental evaluation in long range outdoor conditions also demonstrated that the logarithmic transimpedance solution is able to provide robust 50 m communications in sunny conditions while using a standard power VLC emitter. Thus, this article provides a new argument in support of the usage of VLC technology in automotive applications.  
**Author Contributions:** Conceptualization, S.A.A and A.D.; methodology, A.-M.C. and M.D.; software, S.A.A.; validation, A.-M.C., M.D. and M.P.; investigation, A.-M.C. and S.A.A; formal analysis A.-M.C., M.D. and M.P.; resources A.-M.C. and M.D.; original draft preparation, S.A.A. and A.-M.C.; writing--review and editing, A.-M.C., M.D. and M.P.; visualization, A.-M.C. and S.A.A; supervision, M.D. and A.-M.C; project administration, M.D. and A.-M.C; funding acquisition, M.D. and A.-M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was founded by a grant of the Romanian Ministry of Research and Innovation, CCCDI - UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0917, contract 21 PCCDI/2018, within PNCDI III. This work also was funded by Ministry of Research and Innovation within Program 1 - Development of national research and development system, Subprogram 1.2 - Institutional Performance -RDI excellence funding projects, contract no. 18 PFE/16.10.2018.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Analysis of the Simulation Signal  
To verify the effectiveness of the method proposed in this paper for denoising AE signals, simulation signals with different frequencies are used to verify the proposed method. The mathematical model of AE constructed by [34] is expressed as follows:  
\[f(t)=\sum_{i=1}^{n}A_{i}e^{[-P_{i}(t-t_{i})^{2}]}\sin[2\pi\rho_{m}(t-t_{i})]. \tag{26}\]  
where \(A_{i}\), \(P_{i}\), \(t_{i}\), and \(\rho_{m}\) are the amplitude, attenuation coefficient, delay time and dominant frequency of the superimposed signal of \(i\), respectively. \(\beta\) is the number of superimposed signals in the model.  
The AE signal is obtained by using the above model, and its parameters are shown in Table 1.
## 5 Design Recommendations
### Reduction of Thermal Stresses  
In order to achieve high bonding strength, reduction of thermal stresses that might lead to small cracks or deformation of the interface is crucial. For this reason, the materials being used in the same process should show a small difference in the coefficient of linear thermal expansion.  
If the difference is too high, thermal stresses induced into the interface while cooling may lead to failure of the produced component even without load or may significantly weaken the interface strength. Designing the component in such a way, that the interface shows high stiffness can partially counteract this phenomenon. Additionally, the direction of highest thermal contraction should be reinforced by design measures in order to avoid catastrophic failure. In the same vein, orientation of the infill pattern can lead to reduction of thermal stresses by stiffening the structure in direction of the highest thermal contraction.  
On the other hand, thermal stresses can be eliminated by annealing the structure during printing. By holding high temperatures inside the printing volume for extended periods, thermal stresses are relieved and bonding strength is increased. If the used additive manufacturing machine has no encapsulated printing volume if the interface zone is in a low z-position, a similar effect can be achieved by increasing and holding printing bed temperature for some time. Slowly cooling the part inside the printing volume can further reduce thermal stresses and thereby increase interface strength.
## 6 Simulation Results
### Simulation Results of the Proposed 2 WF-SSRT Topology
#### 6.3.2 Permanent dc Line-Line Fault on Line L\({}_{12}\)  
A permanent dc line-line fault F\({}_{1}\) is experienced on L\({}_{12}\) of unit-1 as explained in scenario-1 of Section 4.1.1, which results in the cutoff of power flow to SS ring via L\({}_{12}\). Thus, the flow of powers from WF\({}_{1}\) and WF\({}_{2}\) of unit-1 is diverted to WF\({}_{5}\) of unit-3 and WF\({}_{3}\) of unit-2, respectively. Power flow within the SS ring almost remains the same. Power and dc-link voltage profiles are depicted in Figure 14. The dc voltage stabilizes itself to 1.025 pu. Thus, the dc grid parameters are within the safe limit which shows that 2 WF-SSRT has even stable topology for dc-link faults within remote/far-end/the last unit.
## 4 Conclusions  
This study verified the effectiveness of biochar/biochar + humic acid (HA) on the remediation of Mn and Cu soil pollution and revealed the effective components and functional groups of/binding to Mn and Cu from the perspective of dissolved organic matter (DOM). Analyses of DOM extracted from biochar/biochar + HA showed that humus-like components played a more important role in binding to HM. The incorporation of HA enhanced the molecular structure and density of DOM and provided more functional groups for binding to HM. The 2D-COS results indicate that the stretching vibrations of C-O bonds in polysaccharides and the protonation stretching vibrations of carboxylic acids, as well as the bending vibrations of O-H bonds in phenolic compounds, play a significant role in binding with HM. However, the introduction of HA also leads to the presence of aromatic ring C-H bonds in DOM, which introduces disturbances in the binding process with HM. In summary, the addition of HA enriches the functional groups of DOM and enhances its binding capacity with HM. This finding provides a theoretical basis and practical guidance for the development of efficient and sustainable soil remediation technologies.  
Methodology, J.W.; Validation, G.C.; Formal analysis, Z.C.; Investigation, L.Z. and Z.X.; Data curation, J.N.; Writing--original draft, Q.S.; Writing--review & editing, Z.L.; Visualization, Z.J.; Supervision, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2024J)5154), the Key Research and Development Program of Hunan Province, China (Grant No. 2022 SK2075), the Natural Science Foundation of Hunan Province, China (Grant No. 2023J)30230), the Scientific Research Foundation of Hunan Provincial Education Department, China (Grant No. 23A0378), the Opening Project of Key Laboratory of Agro-environments in Tropics, Ministry of Agriculture and Rural Affairs and Guangdong Engineering Research Center for Modern Eco-agriculture and Circular Agriculture, South China Agricultural University, and the Opening Project of Key Laboratory of Clean Utilization of Coal and Mine Environmental Protection of Hunan Province, Hunan University of Science and Technology.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 8 Open Issues
### More Effort Is Required to Fulfill Specific Privacy and Security Criteria  
As previously stated, several security and privacy requirements summarised above either need to explore particular technologies fully or include them. For instance, integrity, availability, non-repudiation and privacy-preservation requirements are not taken into account by any technique on a stand-alone basis but integrated with other criteria. Therefore, future research should focus on developing techniques that consider these considerations.
# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 2 Machine Learning Algorithms in Healthcare  
ML algorithms have become pivotal tools in various fields [1], including healthcare [2; 3]. Also, they can be broadly categorized into several types, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning [4]. Examples of supervised learning algorithms are linear regression, random forest, and support vector machine [5]. These algorithms are among the most used for healthcare modeling development, as specified in this article [6], which discusses their effectiveness in various healthcare applications.  
ML algorithms are a class of algorithms that learn from data and can be used for a variety of tasks, including predicting hospital admissions and predicting hospital LoSs. The application of ML algorithms can be divided into the following steps [7]:  
* Data gathering [8]: The data needed to train ML algorithms can be collected using various sources, namely hospital information systems, electronic medical records systems, and patient surveys;
* Data preparation: The previously gathered data must be prepared for training the algorithms, and this includes cleaning, removing null values, and standardizing the data [9];
* Choice and consideration of algorithms to use: There are many ML algorithms, so it is necessary to choose the most appropriate one in terms of the prediction to be made;
* Algorithm training: ML algorithms are trained and use previously prepared data. Training involves presenting data to the algorithms and evaluating their prediction ability;
* Improve the model: Constantly try to improve the model and obtain better results based on the data provided.
## 5 Experimental Results
### Research Questions  
Based on the mentioned results, we provide the answers to the research questions:  
**RQ1:** _Are DSSMs semantically distant from DISMs?_  
In order to answer this question, we need to refer to Tables 4 and 5. The DSSMs are representative of the Jira single-domain model, while the DISMs are representative of the Twitter single-domain model. The Reddit self-domain model falls somewhere in between. Looking at the F1 scores, we can see that whenever the Jira model performed the best, the Twitter model performed the worst and vice versa. Meanwhile, the Reddit model consistently performed at a moderate level. This means that a particular sentiment is expressed differently across different domains, making it seem distant from each other. In our example, Twitter's casual and informal messages contain domain-independent words to describe emotions like love, joy, surprise, sadness, fear, or anger. On the other hand, the SE field utilizes entirely different syntaxes to express the same emotions. After considering these factors, we deduce that DSSMs and DISMs are semantically distant from each other.  
**RQ2:** _Are sentiments expressed differently in each domain?_  
In the Twitter dataset, a negative comment will generally express a disagreement between the users. However, in the Jira dataset, such comments could be a form of criticism or feedback within a team. While negative sentiments in the first case can lead to conflicts, in a community of software engineers the sentiments can indicate growth within the team. It is worth mentioning that negative sentiments could be more valuable than positive ones in SE communication. The sentiments might not be expressed differently in all domains as evidenced by the Reddit dataset, which was found to be similar in sentiment expression to both Jira and Twitter.  
**RQ3:**_Could cross-domain models accurately capture the meaning of DSSM terminology?_  
Our experiment has concluded that cross-domain models outperform single-domain models in terms of average F1 score. Therefore, we may state that cross-domain models are able to successfully capture the meaning of DSSM terminology.
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 3 Experiments and Analysis
### Detecting Defective Stainless Steel Plate  
In this experiment, magnetic flux leakage detection (MFL) was performed on a stainless steel plate of type 2 Cr13 with dimensions of 120 mm \(\times\) 30 mm \(\times\) 5 mm, as shown in Figure 12. To simulate the defect on the stainless steel plate, a circular groove with diameters of 4 mm was manufactured on the surface of the plate, with a depth of 3 mm. Two cylindrical magnets with a magnetic strength of 350 mT were used to provide magnetic flux. The stainless steel plate on the bottom was used as a sinking plate to form the magnetic circuit.  
The detection results for the defective steel plate are presented utilizing two Hall arrays in Figure 13a,b. A comparison of the images indicates the result measured by the unpackaged Hall element array provides the shape of the MFL signal more completely, allowing for easier observation of the direction of the magnetic circuit in the steel plate. In Figure 13c,d, the MFL signal flowing through the center of the groove was measured. The Hall sensor array failed to detect the actual peak point of the MFL signal, which measured only about 3.82 mT, yet the unpackaged Hall element array captured a peak point of 4.79 mT. In addition, from analyzing Figure 13c, the distance between the two peaks of the  
Figure 11: The detection results of two Hall arrays for cylindrical magnets: (**a**) 3D image measured by Hall sensor array; (**b**) 3D image measured by unpackaged Hall element array; (**c**) two Hall arrays detected the first row of magnets; (**d**) two Hall arrays detected the second column of magnets.  
MFL signal in the horizontal direction can only be roughly evaluated as 4 mm. However, according to Figure 13d, this distance is relatively accurately evaluated as 3.32 mm, less than 4 mm. The experiment demonstrates that the magnetic field imaging system with high spatial resolution better restores the defect-induced MFL signals, which assists us in analyzing and researching the property characteristics of different defects.
# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
## 2 Methods  
Here, we describe the dataset, algorithms, and experimental setup designed to predict LOS in the ICU using classification and regression models. The publicly available MIMIC dataset is utilized for the experiments by defining a cohort with specific features for modeling. Data from the first 24 h of ICU admission are included, with features of interest grouped into four categories: demographics, administrative data, vital signs, and laboratory measurements. Data cleaning and preparation are performed prior to modeling, and the model performance is finally evaluated.
# Outpatient Appointment Optimization: A Case Study of a Chemotherapy Service
## 2 Problem Description
### Outpatient Scheduling Problem  
The outpatient scheduling at the chemotherapy service includes two subproblems, according to [PERSON] and [PERSON] [3]. In the first problem, in which the time horizon is considered by the number of days, the scheduler must decide the starting time of a sequence of _treatment sessions_ (as known as _appointments_) and _rest days_ in-between (Figure 1). The second planning problem has the time horizon limited by a day and consists of a four-stage process requiring multiple resources at each stage (Figure 2).  
The first subproblem considers, for each patient, a complete chemotherapy outpatient therapy, which is called the _multi-day pattern_. The therapy comprises several treatment sessions (i.e., appointments) distanced by predefined resting time windows for healing, as illustrated in Figure 1. These inter-appointment periods are fixed so that the patient's health recovers for the next session. For the sake of brevity, this sequence is denoted as a _cycle_ of a patient.  
The second subproblem handles the scheduling on the day of the appointment, decided by the first subproblem. After being admitted, the patient consults a doctor to check up on her/his current health condition. If the patient is judged to be not healthy enough to receive the treatment, the current session is rescheduled to another day. Otherwise, the patient follows a nurse to an available seat (a bed or an armchair). In parallel, the required chemotherapy drug is prepared at the pharmacy unit. Once the drug is ready, the patient proceeds to receive the treatment under the monitoring of a nurse.  
Figure 1: Example of a cycle of three appointments.  
Figure 2: Gantt diagram of a chemotherapy outpatient care.  
However, in some exceptional cases, the treatment session does not require a consultation in the same day because this consultation can be made in advance. In this case, the consulting time of the patient is considered zero. Furthermore, occasionally, the mixing drug step is permitted to proceed _one day in advance_ so that the waiting time can be reduced. The Gantt diagram in Figure 3 illustrates those operations.  
Both subproblems have time-varying resource availability, illustrated as in Figure 4. Since the processing of each stage requires one or multiple resources, the variation in resources implies the maximal number of patients that the service can handle simultaneously. In this problem, four types of resources are considered:  
* **Pharmacy:** a resource only available during the predefined opening hours. The capacity of the pharmacy is considered unlimited.
* **Doctor:** a discrete time-varying resource. A doctor can receive a maximum of one patient at a time.
* **Nurse:** a discrete time-varying shared resource. Each nurse can install a patient to a seat or simultaneously monitor at most \(\Psi\) patients.
* **Seat:** a constant renewable resource. Each patient occupies a seat from the beginning of installation until the end of the session of care.