esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In the quest to enhance production quality and nutritional value, the food industry is increasingly focusing on sustainable supply chains. Often overlooked, waste materials contain valuable bioactive compounds that can be harnessed to fortify various products [1]. By utilizing by-products from plants and fruits, innovative functional foods have been successfully developed [2]. Mango, a tropical fruit prized for its fragrance, taste, and nutritional value, grows naturally in South Asia and is cultivated in diverse regions worldwide, including India, Mexico, Thailand, and the coasts of Sicily, where favourable weather conditions prevail in particular areas [3].  
The steady increase in demand for mangoes in the European market has led to a rise in exports, both within Italy and abroad. Being a climacteric fruit, mangoes undergo rapid ripening after harvest and are often used in the production of fresh products, juices, or dried or freshly cut fruit [4].  
A substantial part of the mango (35-60%) is discarded during processing, without adequate treatment, resulting in environmental issues and economic losses [5]. Mango peel is rich in fibre, with approximately 16% to 28% soluble fibre and 29% to 50% insoluble fibre. It also contains valuable phytochemicals, including carotenoids, polyphenols, and other bioactive compounds, which are associated with potential health benefits [6; 7; 8].  
Traditional bread is conventionally leavened using either baker's yeast or sourdough technology [9]. Sourdough, in particular, benefits from a diverse microbial environment within which lactic acid bacteria (LAB) and yeasts coexist, adding various desirable qualities to the final product [10]. Compared with solely using baker's yeast, sourdough fermentation offers numerous nutritional and functional advantages. These include lowering the glycaemic index, enhancing the properties of dietary fibre complexes, improving mineral and vitamin absorption, and promoting the production of beneficial metabolites during fermentation, such as peptides and amino acid derivatives like aminobutyric acid [11; 12].  
In alignment with European initiatives aimed at reducing waste and enhancing the sustainability of the food industry, this study explores the use of mango peel (MP) in powdered form (MPP) as an ingredient for making semolina bread. Although MPP has been used to enrich bread made with wheat flour and baker's yeast [13; 14; 15], there are no published studies on incorporating these by-products into sourdough Italian-style breads. The objective of this research is to functionalize traditional sourdough bread produced in southern Italy. By repurposing MPP, valuable polyphenols can be introduced into a staple food within the nutritional pyramid.
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
## 2 Materials and Methods
### Characterization  
The point of zero charge, pH\({}_{pzc}\), was measured by the pH drift method [23]. The pH\({}_{pzc}\) test was performed by adjusting pH of 50 mL 0.1 M NaCl solution to a value between 3 and 10 with HCl and NaOH. The adsorbent, CA beads or CA-SCGs beads, were added to the solution (0.5 g). The solutions were placed in an orbital shaker at 25 \({}^{\circ}\)C, at 200 rpm and the final pH was measured after 48 h under agitation. The pH\({}_{pzc}\) is the point where pH\({}_{initial}-\)pH\({}_{final}=0\).  
Raman measurements were carried out using a laser beam with a wavelength of 532 nm with an excitation power of 30 mW at the sample. The laser was focused on the sample using a microscope setup equipped with a 50\(\times\) objective. The instrument used was a Renishaw InVia Raman Microscope (Renishaw plc, Gloucestershire, UK). The analysis was restricted from 500 cm\({}^{-1}\) to 2000 cm\({}^{-1}\) because the most important bands of carbohydrates and polyphenols are in this region. Unwanted fluorescence background was removed by Renishaw proprietary intelligent fitting software.  
Fourier-transform infrared (FT-IR) spectra of the CA beads and CA-SCGs beads and SCGs were obtained using an Agilent Cary 630 FT-IR Spectrophotometer (Agilent, Santa Clara, CA, USA), Attenuated Total Reflection (ATR). Transmission spectra were obtained in the range of 4000-450 cm\({}^{-1}\). An air background was used as a blank for all spectra collected. The most important peaks were identified at its specific wavenumber to see which peaks are characteristic of the CA, of the spent coffee grounds, or both.  
Scanning Electron Microscopy coupled with an energy-dispersive X-ray spectroscopy (SEM-EDX) was used to see the elemental surface composition and morphology of the beads before and after of the adsorption of each metal in a solution of 100 ppm of Cd\({}^{2+}\) or Ni\({}^{2+}\). The instrument used was a JEOL JSM-6480 LV SEM with energy-dispersive X-ray analysis (JEOL Ltd., Peabody, MA, USA). In EDX spectroscopy, an incident beam excites an electron in an inner shell, ejecting it from the shell and creating an electron hole where the electron was. An electron from an outer, higher-energy shell then fills the hole, and the difference in energy between the higher-energy shell and the lower energy shell may be released in the form of an X-ray. The X-ray lines in EDX spectroscopy are identified by a capital Roman letter indicating the shell containing the inner vacancy (K, L, or M). In this study, the X-ray line L was used for Cd\({}^{2+}\) and Ni\({}^{2+}\). The X-ray line K was used for the other elements. The accelerating voltage used was 20 kV.
## 4 State of Art of the Smart Energy Management Systems
### Smart Energy Meter  
The key component in the proposed SEMS architecture in the open-pit mine application is the energy meter. In this section, we select the best energy meter that can fit perfectly with the existing SCADA architecture. We used PM8000 Schneider energy meters, which can communicate easily with the PLCs through the Modbus protocol. The power meter measures all the basic readings [27].  
The PLC transfers all the acquired data to the energy database, as shown in Figure 5. Then the predictive model calculates the forecasted data, after the preparation, modelling and evaluating processes. The SCADA system shows both real time data and forecasts energy consumption [25]. It also shows alerts and the abnormal consumption [8], so the technician could take decisions.
## 3 Methodology
### YOLOv8 and RTDETR  
YOLOv8 [41], developed and released by Ultralytics, the same company behind YOLOv5, aims to provide a high-performance and efficient solution for object detection tasks. Built on the forefront advancements in deep learning and computer vision, it offers unparalleled performance in terms of speed and accuracy. Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs. Compared to previous versions of YOLO, YOLOv8 introduces new design philosophies and technologies to enhance the model's accuracy and speed. It has been optimized in model architecture, data augmentation, and network design, among other aspects, resulting in outstanding results in object detection tasks. YOLOv8 not only performs well in general object detection tasks but also finds applications in various domains such as autonomous driving, industrial inspection, and object recognition.  
RTDETR [47], developed by Baidu, is a real-time end-to-end detector based on the DETR architecture, achieving outstanding performance in speed and accuracy. Its development primarily addresses a significant improvement needed in YOLO detectors, namely the requirement for post-processing non-maximum suppression (NMS), which is often difficult to optimize and not robust enough, leading to detection speed delays. DETR eliminates the need for post-processing NMS and is a Transformer-based end-to-end object detector. However, compared to the YOLO series detectors, DETR detectors are much slower, which does not manifest the advantage of \"NMS-free\" in terms of speed.  
The development of RTDETR aims to address the aforementioned issues and explore real-time end-to-end detectors, intending to design a real-time detector based on the excellent architecture of DETR to fundamentally solve the speed delay problem caused byNMS for real-time detectors. It is the first real-time end-to-end object detector. Specifically, RTDETR designs an efficient hybrid encoder to efficiently handle multi-scale features by decoupling intra-scale interactions and cross-scale fusion. It also proposes an IoU-aware query selection mechanism to optimize the initialization of decoder queries. Additionally, RTDETR supports flexible adjustment of inference speed by using different decoder layers without the need for retraining, which contributes to the practical application of real-time object detectors.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### User Impersonation Attack  
Assume that an attacker \(A\) has obtained the REQUEST message \(\{H_{i},R_{c}\}\) and extracted the data \(\{B_{i},C_{i},m,b,K_{s}\}\) stored in the client's memory device. When \(A\) intends to impersonate the user, \(H_{i}^{\prime}\) and \(R_{c}^{\prime}\) need to be constructed. However, as we mentioned before, without the client's true identity \(ID_{i}\) and server's secret key \(k_{s}\), \(A\) cannot recreate \(A_{i}\). Therefore, our proposed scheme resists user impersonation attacks.
## 4 Experimental Validations  
In order to avoid the influence of day boundary error caused by the orbit and clock product, only 20 hours' data from 02:00:00 to 22:00:00 were processed for each day. The positioning details at the station BRST on 5 March 2017 are first presented as an example, in which the influence of cycle slips on PPP estimation is depicted. The positioning errors and satellite numbers of the combined solutions are also presented. Afterwards, the positioning performance is evaluated for all stations based on the proposed robust PPP approach.
## 1 Introduction
Another challenge in FL is communication; the communication between clients and the central server is considered a bottleneck due to the issue of limited bandwidth in the network and the number of communication rounds as clients in FL train local models and share them with the central server in repetitive rounds [12; 13]. Since communication rounds between clients and the central server can be costly [13], reducing the number of rounds or the number of parameters exchanged can lead to efficient communication in FL [14; 15; 16]. These challenges are important in the FL environment, and many studies in the literature have been proposed to address them. There are a few mapping studies on the federated learning environment; for example, the study in [17] covers the motivation for using FL, and the study in [18] focuses on applying federated learning on energy-constrained IoT devices. However, there are no comprehensive studies that aim to provide a systematic mapping study to cover the techniques utilized to provide communication efficiency and overcome the non-IID data challenge. Therefore, we present a systematic mapping of the literature to summarize and analyze the research carried out on these challenges. Mapping studies are review studies considered an evidence-based technique that helps to analyze a research topic systematically. This method summarizes the research area and identifies the amount and kind of research and available results [19]. This work aims to conduct a systematic mapping study on the challenges mentioned earlier to provide in-depth knowledge in the FL environment. Specifically, we will examine articles that address non-IID data problems, improve communication efficiency in FL, or tackle both challenges simultaneously. This work also aims to identify techniques widely used to overcome these challenges in the FL environment. The non-IID data skewness utilized in the studies aims to overcome the non-IID data challenge. Additionally, the work covers the learning models and datasets most widely used to evaluate the proposed techniques. Furthermore, the work highlights the publication venues and years of these articles. The main contributions of this study are as follows:  
* Providing in-depth knowledge about the techniques that have been proposed to overcome the non-IID data challenge in FL. * Offering a deep understanding of the techniques that have been proposed to provide efficient communication in federated learning. * Identifying the widely used learning models and datasets and associating the respective learning models with the utilized datasets. * Highlighting promising research directions that can open up new opportunities for future studies. The rest of this paper is organized as follows: Section 2 covers the preliminaries and related work. Section 3 provides the research methodology of this study. In Section 4, the results of this systematic study and future research directions are discussed. Section 5 provides the conclusion of the work. Figure 2: Example showing the model divergence for federated learning with IID and non-IID data.
## 4 Radar System Design and Construction Details  
An X-Band 10-GHz CW radar system was designed in-house to perform Doppler measurements. By transmitting a constant frequency signal, the returned signal is received and processed. Since this is a continuous-wave system, there are no ranging capabilities available. Figure 9 shows the block diagram of the 10-GHz radar system, which was constructed and used for data collection. The 10-GHz signal is generated by powering the voltage-controlled oscillator (VCO) with a 12-V power supply. This signal is sent to the splitter, where half of the power is sent to the transmit chain and the rest is sent to the In-Phase/Quadrature (I/Q) mixer. In this step, it is necessary to compare and mix the transmitted and received signals to obtain the Doppler frequencies. To avoid saturation within the power amplifier (PA), a 12-dB  
Figure 8: Different rotation accelerations. (**a**) Ascending; (**b**) Hovering; (**c**) Descending. Figure 9: Block diagram of X-Band 10-GHz radar system. attenuator was used to reduce the output power before amplification. A +18 dBm power output is transmitted through a 10-dB gain horn antenna. After the signal is reflected from a target, it is received through an identical horn antenna and fed through two low-noise amplifiers (LNA2 and LNA1). Next, the signal passes through a bandpass filter (BPF) to filter any undesired reflections or clutter. This filtered signal is mixed with the local oscillator signal to produce the Doppler frequency in two channels. The inphase channel maintains a pure Doppler signal, while the quadrature channel implements a 90\({}^{\circ}\) phase shift. Both signals are passed through two low-pass filters (LPF1 and LPF2) before going through an auxiliary cable to be processed within a laptop environment through MATLAB. Table 3 lists the overall parameters of the radar system, while Table 4 lists the relevant component specifications. A photograph of the breadboard radar system is shown in Figure 10. The radar system was mounted on a cart, as shown in Figure 11. The 10-GHz radar system setup consists of three shelves. The top shelf contains the laptop and the horn antennas. The middle shelf is home to the DC power supply and the RF components of the 10-GHz system. The bottom shelf carries the portable power station that supports the DC power Supply. The horn antennas were configured on a wooden mount pointed at 30\({}^{\circ}\) above the horizon, as shown in Figure 12. There is also a webcam placed above the horn antennas for ground truth when recording data. By using a 30\({}^{\circ}\) look angle, the micro-Doppler signatures of the drones were more pronounced, which will be discussed in greater depth in Section 5. The polarization used was horizontal-transmit horizontal-receive (HH), which is expected to provide the best UAV detection results at far ranges because the blade faces are wider horizontally than vertically when viewed afar at low grazing angles. Similar conclusions were drawn using forward-scattering data under different polarizations [29].
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Performance of RTK Positioning
#### 3.4.3 SR and Accuracy under Different Cutoff Elevation Angles  
To assess the difference in the positioning performance of LCM and TCM RTK, a series of cutoff elevation angles was set from 0 to 50\({}^{\circ}\) with an interval of 5\({}^{\circ}\) to validate the improvement provided by TCM RTK with respect to LCM RTK, as shown in Figure 10. The corresponding statistics (SR and STD) of the correctly fixed solution and float solution are listed in Table 5. The SR and STD of the three-dimensional errors of both the correctly fixed solution and float solution of LCM and TCM RTK under different cutoff elevation angles are displayed as well. It can be observed that with increasing cutoff elevation angle, the SR and positioning accuracy obviously decrease. The significant increase in SR in TCM RTK with respect to LCM RTK when a high cutoff elevation angle is applied provides more application in RTK positioning. For example, an unusual cutoff elevation angle over 40\({}^{\circ}\) can be applied when the rover station is surrounded by buildings, suffering high-level multipath interference, etc. Though the correctly fixed solution retains centimeter-level precision, it varies from about 2-3 cm to about 4-5 cm as the cutoff elevation angle increases. Little improvement was found in the accuracy of the correctly fixed solution of TCM RTK with respect to LCM RTK. For the float solution, more obvious improvement in TCM RTK occurs with increasing cutoff elevation angle. An improvement in the float solution of about 20% can be achieved by TCM RTK with respect to LCM RTK with a high cutoff elevation angle of 50\({}^{\circ}\). The results demonstrate that the TCM RTK can achieve higher positioning accuracy and higher SR in ambiguity resolution due to the extra redundant observations from the unnecessary pivot satellites. With increasing cutoff elevation angle, the satellites saved through choosing the common pivot satellite among the mixed constellation play an increasingly important role in the improvement in positioning performance.  
Figure 10: Success rate (SR) (top), three-dimensional errors of the correctly fixed solution and the float solution of LCM and TCM RTK positioning on DOY 120, 2021 for the baseline GODE-USN7 with different cutoff elevation angles ranging from 0 to 50\({}^{\circ}\). The bars and lines denote the corresponding statistics.
## 3 Model Application in a Simulated River Network
### Impact of Observational Noise and the Weight of the Regularization Term  
In practice, the observational errors of hydraulic state variables are inevitable. A robust inversion model should be able to produce inversion results that are closer to the real solution within a certain observational error range without large errors due to small observational perturbations [17]. In addition, the above parameter optimization shows that the regularization term is critical to controlling the smoothness of the inversed roughness distribution and thus maintain stable model calculation. In this section, the impact of the observational noise and the weight of the regularization term on the model inversion performance is explored.  
To analyze the impact of observational noise, observational-noise levels \(\mathbf{C}_{d}^{1/2}=0.001\), \(0.005\), \(0.01\), and \(0.05\) m were analyzed given the weight of regularization term \(\mathbf{C}_{n}^{-1/2}=100\) (i.e., \(\mathbf{C}_{n}^{1/2}=0.01\)). To analyze the impact of the weight of the regularization term, regularization term weights \(\mathbf{C}_{n}^{-1/2}=20,100,200\), and \(1000\) m\({}^{-1}\) were analyzed given observational-noise level \(\mathbf{C}_{d}^{1/2}=0.005\). Three measurement stations at Nodes 3, 7, and 26 were used, and each had 30 consecutive observations. For each parameter combination scheme, a total of 100 random inversion tests were performed. The average roughness RMSE values were calculated and are shown in Table 4.  
As expected, the roughness RMSE gently increased with increasing observational noise, indicating the model had good noise resistance capacity (Figure 9a). For example, with a 10-fold increase in observational-noise level from 0.001 to 0.01 m, the average roughness RMSE only increased by 16%. The weight of the regularization term was very effective in reducing roughness RMSE by 52% when its value increased from 20 to 100, but the roughness RMSE stabilized with a further increase in regularization weight (Figure 9b). Therefore, the weight of the regularization term was set to 100 (i.e., \(\mathbf{C}_{n}^{1/2}=0.001\)).  
Our parameter optimization study shows that the measured data with a certain amount of noise could inevitably increase the roughness inversion errors, while the error could be  
Figure 8: Roughness inversion results at all section nodes with different initial roughness values.  
significantly reduced by controlling the smoothness level of inversed roughness by using a small weight of the regularization term, e.g., 100 in the simulated case of this study.
## 3 Results
### Offline Classification Results
For example, for 4 mm subsequence there will be two subsequence examples of straight path and three subsequence examples of curved path. That is, there will be five examples in the new dataset out of one example from the original dataset. The examples in the new dataset will be given target labels according to the path (straight or curved). The 4 mm-2C/3S dataset will have 137 \(\times\) 5 = 685 examples to train and test using the algorithms. In the same way, new datasets, namely, 3 mm-3S/3C, 2 mm-4S/5C, and 1 mm-8S/10C, have 137 \(\times\) 6 = 822, 137 \(\times\) 9 = 1233, and 137 \(\times\) 18 = 2466 examples, respectively. Table 2 presents the accuracy results for the path prediction during the insertion process using different subsequence length datasets. These results show that as the subsequence length is decreasing, the accuracy of all algorithms also decreases. This is obvious because of the shorted length vector; we have less information available for prediction. However, the decrease is not linear due to increased number of examples on which the model is trained and tested, which makes generalization easier. As shown in Table 2,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sub-Seq** & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \cline{3-6}  & **SNN** & **SVM** & **KNN** & **RF** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **EnEn** \\ **again, the highest accuracy of 87.4% is achieved by SNN on 4 mm subsequences. Overall, SNN also outperformed other machine learning models in terms of accuracy. The lowest performer was the random forest model, with accuracy of 65.9% when 1 mm subsequences were used.