esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Equipment and Measuring Apparatus  
The thin-film solid-phase microextraction (TF-SPME) support made from stainless steel mesh was bought from Ann Filters (Poland). A magnetic stirrer (SunLab SU1300, Aschaffenburg, Germany) was used to synthesize DESs. A pH-meter (EL20, Mettler Toledo, Greifensee, Switzerland) equipped with the LE407 electrode was applied for controlling pH. An orbital shaker (PSU-20i, Grant-Bio, Keison Products, Chelmsford, UK) set to handle bottles of up to 250 mL capacity was used for extraction. An HP 1100 chromatographic system (Hewlett Packard, Palo Alto, CA, USA) equipped with a UV-Vis detector was applied for the determination of formaldehyde. The system was controlled by the ChemStation software from the same company. A Zorbax(r) ODS column (150 mm \(\times\) 4.6 mm I.D., 5 mm particles) from Agilent (Santa Clara, CA, USA) was used in the analysis. The Fourier-transform infrared spectroscopy (FT-IR) allowed for the recording of spectra of the sorbent prepared from DES. The analysis was done using the IFS 66v/S FT-IR spectrometer from Bruker Optic (Ettlingen, Germany). Scanning was done in the reflection mode using the attenuated total reflection (ATR) module from Perkin Elmer (Waltham, MA, USA). The samples underwent scanning from 500 to 4000 cm\({}^{-1}\). The morphology of the sorbent on the mesh support was studied using a scanning electron microscope (SEM, S-3400N, Hitachi, Japan). For better quality of the images, the Cressington Carbon Coater was used which enabled coating the surface of studied materials with conductive carbon material. A tungsten wire cathode working at the acceleration voltage of 0.3 to 30 kV served as the primary electron beam source. The range of magnification was from 5 to 300,000\(\times\) with a resolution of 3.0 nm for secondary electron image (30 kV acceleration voltage, in high vacuum mode), 10 nm for secondary electron image (3 kV acceleration voltage, in high vacuum mode), 4.0 nm for backscattered electron image (30 kV acceleration voltage, in low vacuum mode). The NSS spectral imaging software (Thermo Scientific, Waltham, MA, USA) was applied to perform the elemental analysis using the Energy Dispersive X-ray Spectrometry technique (EDS).
## 2 Materials and Methods
### Subsection  
The dynamic response of the driver's arms applied at the steering wheel can be represented by a parallel spring and damper, as shown in Table 1. The passive damping and stiffness of the arms represent the properties of the muscles, skin and tendons in relaxation, which can be simplified as arm inertia \(J_{dr}\), arm damping \(B_{dr}\) and arm stiffness \(K_{dr}\) systems [18]. The steering system, including the steering wheel, column, rack and pinion, uprights and wheels and tires can also be represented as simple inertia \(J_{st}\), damper \(B_{st}\) and spring \(K_{st}\) systems. In addition, the torque feedback term \(K_{a}\) is inferred to the torque \(M_{t}\) obtained from the slip angle at the front axle [19].  
The steering mechanism dynamics and steering arm dynamics are coupled at the point where the driver grips the steering wheel. The equation of motion and corresponding transfer function is obtained as follows:  
\[(J_{dr}+J_{st})\delta_{\text{sw}}+(B_{dr}+B_{st})\delta_{\text{sw}}+(K_{dr}+K_ {st})\delta_{\text{sw}}=T_{m}-\frac{M_{T}}{n_{rsw}} \tag{1}\]  
\[G_{s}(s)=1/[(J_{dr}+J_{st})s^{2}+(B_{dr}+B_{st})s+(K_{dr}+K_{st})] \tag{2}\]  
where \(T_{m}\) represents the total muscle torque applied at the steering wheel; \(n_{rsw}\) and \(\delta_{\text{sw}}\) indicate the steering gear ratio and wheel steering angle respectively.  
Figure 1: Human–vehicle shared control (HVSC) dynamics model.  
The spindle feedback muscle information is transferred to the alpha-motor neurons to maintain muscle length. The reflex control of muscle length can cause a change in the intrinsic passive stiffness and damping of the muscle. The value of the reflex gain varies depending on muscle activation [20]. The reflex control dynamic can be expressed as Equation (3):  
\[H_{r}(s)=\left[\omega_{c}(sB_{r}+K_{r})e^{-s\tau_{dd}}\right]/(s+\omega_{c}) \tag{3}\]  
where \(\omega_{c}\) refers to the cutoff frequency; \(B_{r}\) indicates the reflection damping.  
Noteworthily, the inherent time delay can be approximated as a first-order transfer function:  
\[e^{-s\tau_{dd}}\approx 1/(1+\tau_{dd}s) \tag{4}\]  
The steering torque command is generated by a reference model, which is, essentially, a learned inverse model of the arms, steering and vehicle. The following reference model dynamic is extremely appropriate for real driver behavior [20].  
\[\hat{G}(s)=\frac{0.067}{0.028s^{2}+0.083s+1} \tag{5}\]
## 2 Literature Review
NB-IoT and BLE are considered as communication options for an IoT device that is running a Lightweight Machine-to-Machine/Constrained Application Protocol (LwM2M/CoAP) protocol stack. SCHC is used to send compressed IP packets over non-IP packets, and Datagram Transport Layer Security (DTLS) is used to ensure security. The study finds that SCHC is crucial for the functioning of the multimodal approach. [PERSON] et al. evaluated the behavior of SCHC and fragmentation in a real implementation of SCHC over LoRaWAN network within their university campus in [28]. As a result of SCHC, IPv6/UDP/CoAP packets can be transmitted over the LoRaWAN network at all bitrates (data rates). In addition, the authors found that SCHC requires limited resources on the LoRaWAN device while reducing the transmission time (time-on-air) by up to 40%. Ref. [29] discusses several solutions in order to run IPv6 over IoT. Its applicability over LoRaWAN, DASH7 and NB-IoT is investigated. The authors concluded that CoAP and SCHC provide the best approach for LPWAN's technologies. In [30], the authors show that the SCHC compression and fragmentation mechanisms can be used to enable interoperable and secure communication over constrained IoT networks such as Sigfox, LoRaWAN and NB-IoT. While a security-based internet protocol adds a relatively large overhead, SCHC is able to significantly reduce it. Table 1 summarizes the existing work on different LPWAN technologies. Because SCHC over NB-IoT standardization was only recently completed ([31], 2023), few studies have studied the use of SCHC over NB-IoT. While Refs. [27; 29; 30] aimed at studying the application of SCHC on NB-IoT, none of the existing studies evaluated its impact on latency and energy consumption. To the best of the authors' knowledge, this paper is the first to experimentally measure the impact of using SCHC over NB-IoT in terms of latency and energy efficiency considering different scenarios. Although SCHC can be used in a wide range of IoT applications, this paper focuses on the application of SCHC on metering applications. We consider real DLMS metering traffic optionally secured by DTLS. We implement two application setups for SCHC and non-SCHC scenarios and conduct real experiments on an NB-IoT device that sends and receives data using both IP and non-IP data delivery transport modes. Real measurements are conducted in a controlled
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### DR and Load Management in Operation
#### 3.2.2 Load Management  
The evolution of digital tools has significantly enhanced load management in distribution power systems, as evidenced by recent studies. A power distribution network management system (DNMS), leveraging the Spring Boot + SSM framework and LSTM-based load forecasting with big data technology, offers comprehensive power system analysis for improved control and risk management, excelling in urban distribution complexities [37].  
A cloud-based intelligent power management system is explored in [41] that utilizes analytics for balancing control signals and integrating demand power management with renewable energy, resulting in decreased consumption and costs through cloud optimization.  
A toolbox designed for incorporating RESs into distribution networks [121] employs heuristic optimization to simulate energy storage and optimize power output, boosting operational efficiency. Additionally, a load power and energy management system [27] utilizes Proteus Visual Design software and Arduino Mega 2560 for advanced metering and load control that is suitable for various applications.  
These studies highlight the transformative impact of digital advancements on power distribution load management, emphasizing the importance of innovative tools in system management, control, and optimization, particularly with renewable integration.
## 4 Conclusions  
The performance of the units was affected by the design and operating conditions. Key factors such as vegetation, HRT, and the composition of the porous media were found to significantly impact the effectiveness of the CWs. These factors should be carefully considered during the design of CW systems. An HRT greater than 10 days is likely to enhance the performance of the units. Additionally, the presence of zeolite in the porous media delivered promising results, improving the performance of the unit in the removal of all the pollutants studied. Therefore, CW systems can be considered a sustainable alternative to conventional treatment methods, providing environmental, economic, and social benefits.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Mean Values of Pollutant**} & \multicolumn{3}{c}{\multirow{2}{*}{\(t\)**-Test for Equality of Means**}} \\  & \multicolumn{2}{c}{**Removal (\%)**} & & \multicolumn{1}{c}{\multirow{2}{*}{**\(t\)**}} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**_p_ **(2-Tailed)**} \\ \cline{2-3} \cline{5-6}  & **W1 FG-R** & & & & \\ \hline BOD & 45.9 & 47.7 & \(-\)0.486 & 86 & 0.628 \\ COD & 40.6 & 48.3 & \(-\)2.802 & 88 & 0.006 * \\ TKN & 40.9 & 45.5 & \(-\)1.214 & 88 & 0.228 \\ NH\({}_{4}\)-N & 49.0 & 49.4 & \(-\)0.119 & 85 & 0.905 \\ \hline \multirow{3}{*}{BOD} & **W3 FG-R** & **W5 FGZ-R** & & & \\ \cline{2-3} \cline{5-6}  & 47.7 & 60.4 & \(-\)3.740 & 63 & 0.001 * \\ COD & 48.3 & 56.7 & \(-\)3.091 & 87 & 0.003 * \\ TKN & 45.5 & 50.9 & \(-\)1.356 & 86 & 0.179 \\ NH\({}_{4}\)-N & 49.4 & 63.8 & \(-\)4.334 & 78 & 0.001 * \\ \hline \hline \end{tabular}  
* Statistically significantly different mean values.  
\end{table}
Table 6: \(t\)-test results between mean values of pollutant removal: of W1 FG-R and W3 FG-R units; of W3 FG-R and W5 FGZ-R units.  
**Author Contributions:** Conceptualization, G.D.G.; Methodology, G.D.G.; Formal Analysis, G.D.G., I.N., I.-E.M., I.P., P.K. and P.P.; Investigation, G.D.G., I.N., I.-E.M., I.P., P.K. and P.P.; Resources, G.D.G.; Writing--Original Draft Preparation, I.N., G.D.G. and P.P.; Writing--Review and Editing, G.D.G., I.N. and P.P.; Visualization, G.D.G.; Supervision, G.D.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data supporting the research findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# The Cluster Computation-Based Hybrid FEM-Analytical Model of Induction Motor for Fault Diagnostics
## 7 Results and Discussion  
### Stator Current Spectrum under Healthy and Broken Rotor Bar Cases  
The varying inductances give rise to the harmonics in the stator voltage and currents. The most prominent of them are supply based and spatial harmonics. A comparison of the frequency spectrum of stator current obtained from the proposed model and the measurements taken from the laboratory test rig is shown in Figure 8. The only additional harmonics in the practical signal are the third harmonics coming from the supply side. Another major source of harmonics in the signal spectrum is the fault, which can act as a definition component for condition monitoring. Figure 9 shows the development of the left side harmonics (LSH) due to the broken bars at the rated load.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 1 Introduction
Therefore, in this paper we first point out the limitations and problems of downlink communication in LoRa class A, and then propose a cooperative downlink listening (CDL) algorithm, which can solve the downlink limitations of class A while maintaining low-power characteristics. In addition, we also propose various data-centric models such as groupcasting and geocasting, based on CDL. The major contribution of this paper is that we propose a novel algorithm to enhance downlink availability which is considered the most critical drawback of LoRa class A. Nevertheless, the new algorithm and protocol are designed compatible with the LoRa standard. In addition, we also open the new area of a data-centric IoT model for LPWAN, which is introduced in wireless sensor networks. The remainder of this paper is organized as follows: Section 2 presents the technical overview and limitations of LoRaWAN. In Section 3, to address the problem, a new algorithm, CDL, is presented. In Section 4, IoT data-centric models associated with CDL are introduced. The performance evaluation of the proposed algorithm is presented in Section 5. Finally, this paper concludes with Section 6.
## 1 Introduction
Pixel-based classification using a support vector machine (SVM), an artificial neural network (ANN) and a random forest (RF) classifier were performed and compared to distinguish between normal tissue, tumor tissue and background elements. HSI has been also used to assess tissue perfusion, the identification of blood vessels, the differentiation of arteries from veins as well as blood-flow measurement in the skin and ischemia detection [19; 20; 21; 22]. Therefore, it can be used to obtain information about the superficial anatomical structure, allowing us to look deeper below the skin surface and capture features which are not detectable with visible wavelengths [23; 24]. We have previously presented a novel approach for tissue tracking by using multispectral imaging (MSI). We have acquired multispectral (MS) images from the skin of healthy volunteers with an MS camera, which uses a filter wheel and multiple light sources [25]. We have proved the feasibility oftracking skin features without any markers, reaching sub-pixel accuracy for wavelengths at 430 nm and 970 nm, when a simulated movement transformation is applied to the MSI images. The aim of this study is to validate the use of HSI for skin feature detection and to propose a method for the integration of the HSI technology with an image-guided spinal navigation system to facilitate patient tracking. We present an investigative study aiming at a future integration of the HSI technique with an image-guided spinal surgery system to improve the clinical system suitability and achieve markerless patient tracking. The contributions of our method are as follows: (1) we propose a learning network-based approach for local skin feature detection and matching, (2) we explore novel HS data based on a new experimental camera, and (3) we validate the HS study on data acquired in human volunteers.
## 3 Methodology
The \(C/N_{0}\) values of the four satellites at station CEDU decreased sharply, and all of them had a loss of lock at 03:31 (UTC), with a maximum duration of approximately 450 s. In contrast, none of the four satellites at station KUNM had a loss of lock, despite the significant decline in \(C/N_{0}\) of approximately 12 dB-Hz. However, the fading trends of all the GPS satellites at the different stations were almost the same. In other words, most satellites had valleys at the same time despite their different degrees of decline, especially at approximately 3:31 (UTC). Based on the above analysis, we propose an intense L-band SRB detection method using the fact that SRBs lead to a decrease in the \(C/N_{0}\) values for signals from several GNSS satellites over a large area of the Earth's surface. In this process, data preprocessing is initially performed, and the specific details are as follows:  
* The solar elevation angle of all the IGS stations is calculated to select the stations close to the subsolar point. * The data of improperly working receivers are excluded, such as some stations with incomplete data. * The data type of \(C/N_{0}\) is set to an integer to eliminate the impact of the differences in the data precision of various types of receivers on the results. * The elevation mask angle is set to \(10^{\circ}\). Note that the elevation mask angle is lower than normal to increase the amount of observation data. Due to the fact that GPS L5 and GALILEO E5 include few satellites that can be observed at the same time from the data provided by the IGS. In addition, multipath cannot lead to a simultaneous decrease in the \(C/N_{0}\) values of multiple satellites over a large area close to the subsolar point. Hence, the proposed method can prevent the impact of multipath to some extent, which is reflected in the low false alarm rate in the subsequent experiments. Then, the valley periods of a single satellite at a single station are calculated, and finally, the detection result of an intense L-band SRB is obtained by comparing multiple satellites and multiple stations.
###### Abstract  
Agriculture in free spaces within urban areas sustains residents as they grow crops to ensure food availability while at the same time selling other produce to fund other needs. Due to the value of urban agricultural production, the main focus of this study was to investigate the implications of urban growth on maize yield in Harare metropolitan city. In order to achieve this, Landsat multispectral and multi-temporal data were used to establish the responses of the estimated maize yield to city growth from 1984 to 2018. Initially, Land Use and Land Cover map for each period were produced using multispectral images and field observation in Support Vector Machine (SVM)-based supervised image classification. The maps were reclassified using a binary scheme of croplands and non-croplands, which was used to quantify cropland area in each period and hence change over time. The maize yield for each period was estimated from data obtained from a study of maize yields obtained at the University of Zimbabwe Farm (UZ) farm for maize grown under variable agricultural practices. Results showed that cropland area was reduced from 11,120 ha in 1984 down to 2631 ha in 2018. The estimated average maize yield decreased from 52,264 tons in 1984 to 12,366 tons in 2018. In addition to showing the value of urban agriculture, the findings of this study are important in informing the government, municipalities, and other stakeholders about how urban growth has the potential to compromise food security and livelihoods, especially for the urban poor.  
classification; land use and land cover; remote sensing; support vector machine; urban agriculture; yield estimation +
Footnote †: journal: Medical Informatics
# A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments
## 3 Measurement Campaigns and Experimental Setup  
The data for the analysis are collected in and around York University, Toronto, Canada. York University and adjoining areas cover all the aspects of multiple multipath profiles needed for the experiment. The profiles include highways, parking lots, sub-urban with vegetation, overpasses, and open sky environments, each having a unique multipath characteristic and contributing to better appreciating the range error behaviors. Figure 2 shows the aerial and detailed street view photos of these measurement campaigns, and blue arrows indicate the direction.  
As shown in Figure 3, the experimental setup contains two sensor suites. The first contains two geodetic GNSS receivers to provide position trajectory references for tested smartphone. One NovAtel OEM7 receiver is mounted on a rooftop as a reference station within a 5 km baseline, whilst the other receiver is connected with a geodetic antenna and mounted inside the vehicle experimental box (see Figure 3a). The second sensor suite includes Xiaomi MI 8 phone, which is fixed with holders and mounted on the vehicle dashboard to mimic real-life applications (see Figure 3b). In addition, it is feasible to mount MI 8 on the top of the experimental box for in-depth comparison and further analysis. The lever arm between the smartphone and the referenced center has been carefully measured.  
Table 1 highlights three datasets used in this study, including the test number, collections date, time duration, as well as phone mounted points. Road tests were carried out on two separate days with identical route and different traffic conditions, and the time durations for each test are 31, 28, and 29 min, respectively. These datasets are used and analyzed in the following section:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test \#** & **Collection Date** & **Duration** & **Mounted Points** \\ \hline
1 & 8 September 2020 & 22:29–23:00 & Roof \\ \hline
2 & 8 August 2021 & 1:14-1:42 & Roof \\ \hline
3 & 8 August 2021 & 1:50–2.19 & Dashboard \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of collected smartphone datasets.
# Matheuristics and Column Generation for a Basic Technician Routing Problem
## 5 Dantzig-Wolfe Reformulation and CG Matheuristics
### Extended Formulation and Column Generation
The computations of \(RC^{*}_{i}\) are independent for the technicians and can be formulated using MILP formulations of Section 4. \[ \begin{array}{ll}RC^{*}_{i}=&\min_{u}\sigma_{i}+\sum_{n,n^{\prime}\in \mathcal{N}_{i}}(D_{n,n^{\prime}}-\pi_{j})u_{n,n^{\prime}}\\ s.t:\forall j\in\mathcal{J},&\sum_{n\in\mathcal{N}_{i}}u_{n,j}= \sum_{n^{\prime}\in\mathcal{N}_{i}}^{n^{\prime}}u_{j,n^{\prime}}\leqslant 1 \\ &\sum_{j\in\mathcal{J}_{i}}u_{i,j}=1\\ \forall j\in\mathcal{J}_{i},&u_{i,j}\leqslant 0\\ \forall j,j^{\prime}\in\mathcal{J}_{i},&t_{j}+L_{j}+T_{j,j^{\prime}}\leqslant t _{j^{\prime}}+\left(1-u_{i,j^{\prime}}\right)\cdot M^{3}_{i,j}\\ j\in\mathcal{J},&T^{start}_{i}+T_{i,j}\leqslant t_{j}+\left(1-u_{i,j}\right) \cdot M^{2}_{i,j}\\ j\in\mathcal{J},&t_{j}+L_{j}+T_{i,j}\leqslant T^{end}_{i}+\left(1-u_{j,i} \right)\cdot M^{1}_{i,j^{\prime}}\\ \forall j\in\mathcal{J}&t_{j}\in[T^{min}_{j},T^{max}_{j}]\\ \forall n,n^{\prime}\in\mathcal{N}_{i}&u_{n,n^{\prime}}\in\{0,1\}\end{array}  \tag{35}\]  
LP relaxation of (27)-(29) is calculated by Algorithm 5, with an initial set of columns as input. The initial set of columns can be empty, and RMP is always feasible thanks to the penalty costs. Matheuristics can initialize the CG algorithm adding the columns corresponding to primal solutions. ```
\(\mathcal{C}\) set of initial columns. do: solve RMP (31) with columns defined in \(\mathcal{C}\)  store dual variables \(\sigma\) and \(\pi\) and optimal cost from (31) for each  technician \(i\in\mathcal{I}\) :  solve (35) to optimality with last \((\sigma,\pi)\) values if\(CR^{*}_{i}<0\)then add the optimal column to \(\mathcal{C}\) endfor while: columns are added in \(\mathcal{C}\) return the last cost of the RMP (31)
```  
**Algorithm 5** Standard column generation algorithm  
**Remark 1**: _Constraints (28) are written with inequalities, and the MILP model is still valid with equalities instead. One could also have equalities in (29), defining explicitly empty routes. Using inequalities induces a better stability of the column generation algorithm, with signed dual variables \(\pi,\sigma\)[11]._