esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Spatial Distribution of GWLF Potential  
After determining the weighting coefficients for different rainfall conditions, the spatial distribution of GWLF potential can be calculated from the feature score matrix [P] and estimated weighting coefficients in Figure 13. As shown in Figure 15, the groundwater  
Figure 14: Comparison between the observed and estimated groundwater level fluctuation in the wet and dry seasons.  
level fluctuation potential in the wet season is higher than that in the dry season. The groundwater level fluctuation potentials in different hydrological years are quite different. In particular, the potential for groundwater level change in the wet season is more pronounced than that in the dry season year. Regarding spatial outcomes, the potential of groundwater level fluctuation in the downstream of the study area is more significant than that in the upstream of the study area, regardless of the season type. The higher GWLF potential in the downstream area, in addition to the characteristics of influencing factors, may be due to lateral replenishment from the upstream area. In conclusion, by viewing the GWLF potential map, the spatial and temporal dynamic behavior of groundwater in mountainous areas can be clearly understood.
# A Conceptual Blueprint for Making Neuromsculoskeletal Melically Useful
## 4 Description of Needs
### Clinical Needs
Third, a clinical model should be patient-specific, capable of having relevant model parameter values calibrated to the patient's pre-treatment movement and other data. A real-life example involving computational design of a personalized rehabilitation treatment for medial knee osteoarthritis demonstrates how these three elements work together [17]. The patient studied had knee pain due to medial compartment knee osteoarthritis and was seeking an effective non-surgical treatment option. The goal became to design a modified gait pattern that reduced the patient's knee pain. The selected clinical measure was the peak knee adduction moment. This quantity is highly correlated with the rate of medial knee osteoarthritis progression [167], and a low value following high tibial osteotomy surgery is associated with the best long-term functional outcome [168]. For clinical requirements, knee loading is heavily influenced by patient-specific leg alignment and walking pattern, standard non-surgical treatment approaches are ineffective at relieving pain and restoring walking function, the peak knee adduction moment can quantify initial clinical state and final treatment outcome, the subject's walking motion can be modified and will change the peak knee adduction moment, and finally the peak knee adduction moment can be measured in the lab prior to treatment, predicted with a computational walking model to design treatment, and measured again in the lab following treatment. The clinical model predicted the patient's peak knee adduction moment for different modified walking motions, was minimally complex by modeling only the skeleton (i.e., no muscle or neural control models) and omitting foot-ground contact models, and utilized a patient-specific kinematic structure and mass distribution. An optimization problem was formulated to predict the patient-specific gait modifications that would minimize the patient's knee adduction moment while still producing a normal-looking walking motion. The optimization predicted that a modified gait pattern involving knee medialization during stance phase could produce a 34% reduction in the patient's peak knee adduction moment. After learning to perform the predicted gait modification, the patient was able to achieve a 37% reduction when retested in a gait lab [17]. This reduction was comparable to that expected from high tibial osteotomy surgery [169] and decreased the patient's risk of osteoarthritis progression by a factor of 10 [167]. The patient incorporated the modified gait pattern into his daily life, experienced significant pain reduction, and was able to return to running and recreational sports activities.
## 3 Results and Discussion
### Bioimpedance Measurement Prototype Validation
From the measurements, one can estimate the equipotential lines that can be evaluated at the lateral border of the container by the electrodes. The black rectangles represent the current injection electrodes. The equipotential lines provide a visual feedback to help the electrode positioning. The voltage measurements cannot be performed between electrodes that are linked at the same equipotential line. The experimental results of Figure 7 resemble the expected equipotential lines of Figure 2. The colour intensity variation between Figure 7A and Figure 7B is due to the change in impedance magnitude caused by the difference in concentration of the saline solution and it is coherent with the results shown in Figure 5. At high concentrations, there are many ions available for electrical current transportation. Thus, the impedance magnitude is lower and the voltage drop across the load is quite small, as shown in Figure 7A. For lower concentrations, the impedance magnitude is higher. As a consequence, the voltage drop across the load is greater. From Figure 7B, one can notice a voltage drop of 1 V, approximately. The distilled water used to make the dilutions has an approximate impedance value of 7 k\(\Omega\) with a frequency variation up to 10\({}^{5}\) Hz. Figure 5: Impedance measurements in saline solution at different concentrations and frequencies. The 100% NaCl solution corresponds to a concentration of 2 mol \(\cdot\) L\({}^{-1}\). (**A**) Impedance magnitude concerning frequency, and (**B**) Impedance magnitude concerning molar concentration. (**C**) Impedance phase concerning frequency, and (**D**) Impedance phase concerning molar concentration.
## 4 Security and Performance Analyses
### Authentication Proof of the Proposed Scheme Using BAN Logic
#### 4.1.1 Inference Rules of BAN Logic  
* **Rule 1.**\(\frac{P\big{|}=P\sum_{k}^{K}Q,\ P\ \phi\ (X)_{K}}{P\big{|}=Q\times X}\) : If the entity \(P\) believes that the secret \(K\) is shared with \(Q\) and sees message X is encrypted using \(K\), then P believes that \(Q\) once said \(X\).
* **Rule 2.**\(\frac{P\big{|}=\#(X),\ P\big{|}=Q\times X}{P\big{|}=Q\times X}\) : If the entity \(P\) believes that \(X\) is fresh and the entity \(Q\) once said \(X\), then P believes that \(Q\) believes \(X\).
* **Rule 3.**\(\frac{P\big{|}=Q\times X,\ P\big{|}=Q\big{|}=X}{P\big{|}=X}\) : If the entity \(P\) believes that \(Q\) has jurisdiction over \(X\) and \(Q\) believes \(X\), then P believes that \(X\) is true.
* **Rule 4.**\(\frac{P\big{|}=\#(X),\ P\big{|}=Q\big{|}=X}{P\big{|}=P\sum_{k}^{K}Q}\) : If the entity \(P\) believes that \(X\) is fresh and \(Q\) believes \(X\), then \(P\) believes the secret \(K\) that is shared between both entities \(P\) and \(Q\).
* **Rule 5.**\(\frac{P\big{|}=\#(X)}{P\big{|}=\#(X,\ Y)}\) : If the entity \(P\) believes that \(X\) is fresh, then \(P\) believes the freshness of \((X,\ Y)\).  
\begin{table}
\begin{tabular}{l l} \hline
**Notation** & **Abbreviation** \\ \hline \(P\ |\equiv\ X\) & The entity \(P\) believes the statement \(X\) \\ \hline \(P\ \Longrightarrow X\) & \(P\) has jurisdiction over the statement \(X\) \\ \hline \(P\ |\ \sim\ X\) & \(P\) once said \(X\) \\ \hline \(P\triangleleft X\) & \(P\) sees \(X\) \\ \hline \(\langle X\rangle_{K}\) & Formula \(X\) is encrypted under the key \(K\) \\ \hline \(P\ \stackrel{{ K}}{{\leftrightarrow}}\ Q\) & \(P\) and \(Q\) communicate via shared key \(K\) \\ \hline \(P\to Q\,:m\) & \(P\) sends the message \(m\) and \(Q\) receives it \\ \hline \#\(X\) & The message \#\(X\) is freshly generated \\ \hline \end{tabular}
\end{table}
Table 2: BAN logic notations [34].
# From DevOps to MLOps: Overview and Application to Electricity Market Forecasting
## 2 Related Work
### MLOps from DevOps  
With the successful adoption of DevOps, various organizations are trying to incorporate continuous practices in ML system development [62]. ML developers and operations teams are trying to adopt DevOps concepts to ML systems for end-to-end life-cycle automation. Containerized microservices and cloud-based DevOps have seen good stability and a reasonable success rate in production deployments [63]. For example, Kubeflow [64; 65] is an ML toolkit for Kubernetes, available for almost all major cloud providers. Such toolkits aim to create ML workflows in which containers are already present. [PERSON] et al. [66] discussed such concepts and issues of applying DevOps practices to ML applications, while [PERSON] et al. [62] discusses DevOps applications in ML systems. Data play an influential role in any ML application. Unlike conventional software development practices, the SDLC of an ML application revolves around data. In most cases, the core ML code is minimal, but it must integrate with some significant components of a bigger system. [PERSON] et al. [4] analyzed such systems and explored the hidden technical debt. The complexity increases when ML applications are deployed on the cloud or interfaced with web APIs. [PERSON] et al. [67] proposed operationalizing ML applications for such an environment, and MLOps could be implemented for such hybrid cloud deployments.  
Several authors have investigated MLOps. [PERSON] et al. [9] studied the state of ML to understand the extent of MLOps required and to analyze the issues with data; however, the authors did not consider deployment. Some research has been performed on the trends and challenges in MLOps. [PERSON] et al. [68] recapped trends in terms of properties such as the fairness and accountability of sustainable ML operations of a software system. Several works propose the MLOps framework for applications [62; 69] such as the automotive industry, supply chain management, and IoT. [PERSON] et al. [70] presented issues related to the MLOps pipeline when multiple organizations are involved, highlighting factors such as scalability. Different cloud providers offer MLOps solutions as a service so that the whole ML product life cycle can be managed on the cloud. Azure MLOps [71] and AWS SageMaker for MLOps [72] are some examples of such cloud services. A few cloud providers have published a detailed guide on the MLOps life cycle for the purpose of building ML applications and MLOps pipelines on the cloud or on-premises servers [73].
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 3 Materials and Methods
### Evaluation  
The accuracy evaluation of scene recognitioin involves the comparison of results from the proposed approach to other approaches. For this purpose a variety of techniques can be exploited, e.g., hidden markov model (HMM), embedded HMM, support vector machines (SVM), etc. [73; 74; 65; 75; 76]. SVM, developed by [PERSON] and [PERSON] [77] is one of the powerful machine learning techniques to perform classification. It divides the hyper-plane optimally so that to maximize the distance between the classes [78]. Various kernel types can be used with SVM including radial, polynomial, neural, etc. We used radial basis function as kernel to train SVM. Gamma is set to 0.0001 which is the parameter of a Gaussian Kernel for non-linear SVM classification.  
The evaluation of localization approach is performed using multiple techniques. KNN is the most widely used technique in localization systems. We evaluate the accuracy of our localization module by comparing it with KNN results. Since we have modified KNN approach, so it is more logical to compare it to KNN than any other technique. However, we are using magnetic fingerprint (MFP) with scene information and without scene information to analyze the impact of scene recognition on localization accuracy. MFP involves taking the user sent magnetic sample and match it against the magnetic fingerprint database to calculate the Euclidean distance. Minimum Euclidean distance represents the user's current location. Using scene information, MFP technique loads only the narrowed magnetic database; however, full magnetic database is used as search space when scene information is not available.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 2 Design Methodology
### Phase 1: Design of Super Wideband Antenna (Antenna 1)  
A microstrip-fed printed monopole antenna for SWB application is used. The proposed antenna is derived from a conventional circular monopole antenna. To achieve wide bandwidth and matching, a conventional circular monopole antenna was modified by adjoining a pair of ears at the upper part of the radiator and also by modifying its partial  
\begin{table}
\begin{tabular}{c c c c} \hline
**Symbol** & **Dimension (mm)** & **Symbol** & **Dimension (mm)** \\ \hline W & 40 & R4 & 2.6 \\ L & 45 & R5 & 2.3 \\ R1 & 9.7 & L\({}_{\text{F}}\) & 21.4 \\ R2 & 5.8 & W\({}_{\text{F}}\) & 3.6 \\ R3 & 4 & L\({}_{\text{G}}\) & 15 \\ E1 & 34 & C1 & 5 \\ E2 & 10 & C2 & 2.4 \\ E3 & 1.2 & C3 & 1.6 \\ EMIN & 4.2 & CG & 0.6 \\ EMAX & 8.16 & CT & 0.54 \\ EG & 0.6 & CS & 0.6 \\ E6 & 0.2 & & \\ \end{tabular}
\end{table}
Table 1: Optimal values for different parameters of the designed triple band-notched SWB antenna structure.  
Figure 1: Labelled geometry of the triple band-notched super wide band (SWB) antenna front view and back view made on RT-Duroid 5880 substrate with dielectric constant of 2.2, tan \(\delta\) = 0.0009, and thickness of 1.57 mm.  
ground plane. The anatomy of the designed SWB antenna and its corresponding VSWR variation over frequency are displayed in Figure 2. The values of VSWR between 1 and 2 throughout the frequency range (1.2-47.5 GHz) depicts good impedance matching for the proposed SWB antenna. Since there is no predefined standard frequency spectrum for SWB antenna structures, a comparison between the wideband antenna structures can be made in terms of an index term, i.e., the bandwidth dimension ratio (BDR) [16], which defines the antenna bandwidth (in percentage) per unit area of the antenna. The following equation is used for BDR calculation:  
\[BDR=\frac{BW\%}{\lambda_{length}\times\lambda_{width}} \tag{1}\]  
where, \(\lambda_{width}\) and \(\lambda_{length}\) are the electrical width and length, respectively, of the antenna in the context of wavelength associated with the lower cut-off frequency, and \(BW\%\) is percentage bandwidth considering a VSWR \(\leq\) 2. The designed antenna structure achieved a total bandwidth range of 1.2-47.5 GHz (VSWR < 2) along with a \(BDR\) of 38.9:1 and a fractional bandwidth of 190%.
## 2 Key Steps in a FRB Search Pipeline
### Rejection of Radio Frequency Interference
#### 2.1.7 Secondary, Off-Line RFI Mitigation  
In buffered or off-line data, more sophisticated methods can be used. With these, FRBs that were detected from the real-time pipeline can be cleaned from RFI to achieve a higher signal-to-noise ratio (S/N). But, these also reduce the S/N down to which new FRBs can be found and studied. Off-line, RFI filters can take multiple passes to, e.g., first assess the noise levels in the entire data set, and next define individual bright outliers as RFI (e.g., [16; 17]), or even remove low-level periodic RFI throughout [18].
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 4 FL-Aided Fuel Consumption Prediction
### Tuning of Learning Hyper-Parameters  
When training a multi-layer perceptron-based ANN model, the selection of the hyperparameters has to be cautiously considered, as their values can significantly impact the prediction error convergence. Among several learning hyper-parameters, different values of the learning rate (\(a\), it influences the back-propagation and ANN weight adjustment) and ANN deepness (number of hidden layers, it impacts the model complexity) were found to cause serious variations in the model performance. To that end, extensive experimentation on those parameters was performed to achieve their best value tuning. As the activation function of the ANN units, the ReLu function was adopted, whereas an Adam optimizer was applied for implementing the Stochastic Gradient Descent of the back-propagation iterations. Initially, we separated the dataset into training/validation data using a 90/10 split. For each model training setup, the Mean Absolute Error (MAE) was extracted over the validation samples. To quantify the best hyper-parameter configuration, the Normalized Accuracy (\(A_{norm}\)) of each derived model was inversely proportional to MAE and referenced to the minimum MAE model (\(A_{norm}=1\) for the most well-tuned model), according to the formula:  
\[A_{norm}^{i}=\frac{\min_{j\in M}\{\sum_{n=1}^{N_{val}}|y_{pred}^{n,j}-y_{real}^ {n,j}|\}}{\sum_{n=1}^{N_{val}}|y_{pred}^{n,i}-y_{real}^{n,i}|}, \tag{2}\]  
where \(A_{norm}^{i}\) is the Normalized Accuracy of model \(i\); \(y_{pred}^{n,i}\) and \(y_{real}^{n,i}\) are the predicted and actual values of validation sample \(n\) and \(N_{val}\) is the count of validation data. Indices \(i\) and \(j\) run over the set of models \(M\) derived by different hyperparameter configuration (i.e., count of models is \(a\) levels multiplied by deepness levels or \(5.5=25\) models). Thus, the accuracy of all models is referenced to the best model performance, since the denominator reflects the MAE resulted from model \(i\), whereas the nominator calculates the minimum MAE across all models.  
Figure 2 shows the Normalized Accuracy for 30 pretrained models considering (five) varying values \(a\) for each of the six ships, considering fixed ANN deepness for all ships of four hidden layers. Evidently, local ANN models for all ships are optimally configured for \(a=0.001\) and four hidden layers. The neurons contained in each hidden layer were, respectively, [1000, 800, 600, 400].
# The Effect of Laser Pulse Widths on Laser--Ag Nanoparticle Interaction: Femto- to Nanosecond Lasers
## 5 Conclusions  
This study evaluated the production of Ag nanoparticles according to the laser pulse width. As the laser pulse width decreased, the average nanoparticle size decreased. The nanoparticles produced using femtosecond and picosecond laser pulse widths were found to include more 25 \(\upmu\)m particles than particles of other sizes, whereas, by using nanosecond laser pulse widths, nanoparticles ranging in size from 25 to 200 nm were produced. The rate of increase in the particle size is larger in the nanosecond region, which is seen as a characteristic of the laser thermal ablation related to the surface area. In comparison, in the femtosecond and picosecond regions, a larger number of nanoparticles was produced. This phenomenon can be interpreted on the basis that the shorter the pulse width, the more efficient the occurrence of fragmentation.  
This study was supported by the Research Program funded by the SeoulTech(Seoul National University of Science and Technology).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
Figure 8: Nanoparticle yield rate as a function of the pulse width.  
Figure 7: Mean nanoparticle diameter as a function of the pulse width.
# Dynamic Scheduling Optimization of Production Workshops Based on Digital Twin
## 5 Dynamic Scheduling Strategy and Case Analysis Based on Digital Twin
### Basic Assumptions and Mathematical Models  
According to the disturbance, the dynamic scheduling problem should consider the workpieces, processes, available machines, and the available time of workpieces and machines. The assumptions of the workshop dynamic scheduling problem model in this paper are as follows:  
1. All workpieces can be processed at the rescheduling time.
2. All machines can only be used after the previous process is completed.  
The optimization objective includes minimizing the completion time (min\(S_{d}\)) and minimizing the total energy consumption (minV\({}_{d}\)). The mathematical model is expressed as follows:  
\[sODP_{xy}\leq sOP_{xy} \tag{2}\]  
Figure 7: Workshop production scheduling based on digital twin.  
\[Q\geq w \tag{3}\]  
\[sODP_{xy}\geq t \tag{4}\]  
where \(t\) is the time when disturbance occurs, \(Q\) is the number of machines in total, \(w\) is the number of machines available for unfinished process when disturbance occurs, \(sOP_{xy}\) is the start processing time of the \(y\)-th operation of \(x\)-th workpiece in the initial scheduling scheme, and \(sODP_{xy}\) is the start processing time of the \(y\)-th operation of \(x\)-th workpiece in the rescheduling scheme.  
Equation (2) indicates that the start processing time of each process rescheduling scheme of each workpiece cannot be less than the start processing time of its initial scheduling scheme. Equation (3) indicates that the number of machines available for each process rescheduling scheme of each workpiece cannot exceed the number of machines available for its initial scheduling scheme. Equation (4) shows that the start processing time of each process rescheduling scheme of each workpiece is after the disturbance time
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 1 Introduction
In general, SD-FEC yields large coding gains, but poses implementation challenges in terms of complexity, delay, power consumption, and circuit area [35]. Therefore, for applications with strict latency and complexity requirements (e.g., short reach), HD-FEC codes are the preferred alternative. Staircase codes (SCCs) [36; 37] and product codes (PCs) [38] are two popular families of codes that give large coding gains and allow low-complexity HD-FEC decoding structures. At present, PCs have been adopted by the optical submarine standard [39] and SCCs are part of the 400 ZR Implementation Agreement (as an outer code) in the Optical Internetworking Forum [40]. PC and SCC decoders have been implemented and evaluated using synthesized gate netlists in a 28 nm in very-large-scale integration system, reaching more than 1 Tb/s information throughput with energy efficiency of \(\sim\)2 pJ/bit only [41]. Closing the gap between the performance of HD-FEC and SD-FEC is an active research area. One idea for improving decoding is to use pure soft-input/soft-output decoder for a HD-FEC structure within all iterations [42], another idea is to use soft-aided algorithms with hard-input/hard-output decoder via scaled reliabilities [43; 44; 45], soft-aided bit-marking (SABM) [46; 47], or a combination of the two [48]. In this paper, we propose a geometrically-shaped soft-aided SCC (GS-SA-SCC) coded modulation scheme. In particular, similar to the works in [17; 31], we optimize the constellations and mapping strategy in terms of GMI. We then apply the SABM decoding algorithm for SCCs to yield additional coding gains. We show that SCCs with SABM decoding and GS provide gains up to 0.83 dB with respect to the baseline scheme with equidistant QAM and standard decoding for SCCs. Furthermore, we evaluate the proposed CM scheme in both single-span and multi-span wavelength-division multiplexing (WDM) optical fiber communication system, which covers a wide range of distances up to 950 km and line rates up to 666 Gbit/s. By evaluating the AIRs and post-FEC bit error rate (BER) results, we show that the proposed GS-SA-SCC CM scheme achieves up to 24% reach increases with respect to the system using equidistant QAM and standard decoding for SCCs for multiple line rates between 450 Gbit/s and 666 Gbit/s. The paper is structured as follows. In Section 2, we discuss general aspects of AIRs, GS, and SCCs with SABM decoding as well as the proposed GS-SA-SCC CM scheme. In Section 3, we present and analysis the advanced CM scheme with geometrically-shaped constellations and SABM for SCCs over additive white Gaussian noise (AWGN) channel. The simulation results of optical CM transmission system for both single-span system and multi-span systems are further presented in Section 4. Finally, we conclude this paper in Section 5.