esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Works
### Oversampling Methods  
As mentioned earlier, the growing use of ML in the medical field brings challenges such as limited and imbalanced data. Despite this, the use of such datasets can be quite relevant for the medical field [21] and studies have been carried out to deal with such limitations. Some methods use ML algorithms, probability, or weights to define the samples to be resampled, while some methods perform the combination of oversampling and undersampling [44]. Some of these works will be reported below.  
One of the best-known techniques for dealing with this type of problem is SMOTE [34]. The purpose of SMOTE is to generate new synthetic minority class data, thus selecting a sample of the minority class randomly and its k nearest neighbors of the same class are calculated (by default 5) as a line is drawn around the selected samples and new synthetic data is generated.  
[PERSON] et al. [34] performed a combination of subsampling and supersampling techniques. The subsampling technique was proposed in conjunction with supersampling to increase the sensitivity of a classifier to the minority class. Thus, in the proposed method, samples from the majority class were taken randomly and samples from the minority class were synthetically generated until it has a specific proportion of the majority class. In another work, [PERSON] et al. [45] performed a combination of the SMOTE algorithm with the boosting procedure, changing the update weights and compensating for skewed distributions of misclassified instances to generate synthetic data, thus creating the SMOTEBoost algorithm.  
Unlike other methods that resample all examples from the minority class or that randomly select a subset, [PERSON] et al. [35] in their study, selects only the minority class samples that are Borderline and most likely to be misclassified, thus developing a variation of the SMOTE oversampling method called Borderline-SMOTE. While [PERSON] and [PERSON] [36] used the SVM classifier to find the boundary region, combined with extrapolation and interpolation techniques for oversampling the minority boundary instances.  
[PERSON] et al. [46] addressed two types of oversampling, namely, RACOG and wRACOG, where it used joint probability distribution of data attributes and Gibbs sampling to choose and generate the samples of minority classes synthetically. [PERSON] [44] used the SMOTE oversampling method only to support minority class vectors that were found by training the cost-sensitive SVM classifier.  
In contrast, we address very limited datasets by combining manual augmentation and automated augmentation. To verify the best combination, we experiment with manual augmentation along with automated augmentation using SMOTE, Borderline-SMOTE, and Borderline-SMOTE SVM.
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
###### Abstract  
The deep defluorination of water remains a significant environmental challenge. In this work, aluminum was loaded onto the bifunctional resin 9957 containing a phosphoric acid difunctional group for efficient fluoride removal. Al-S957 demonstrated excellent fluoride removal performance across a broad pH range. When anions and organics coexisted, Al-S957 exhibited significantly better fluoride adsorption performance compared to aluminum-loaded monofunctional resins. The adsorption followed an endothermic chemisorption process on a monolayer surface. FTIR and XPS analyses further revealed that fluoride removal relied on a ligand exchange mechanism. Column adsorption conducted over five cycles highlighted the strong practical potential of Al-S957. The results suggested that Al-S957 exhibits significant potential for practical applications.  
aluminum; resin; fluoride removal; adsorption; ligand exchange Article
## 3 Proposed Method
### Cross-Modality Feature Fusion Module (CMFFM)
In the PFEM and the MFEM, the PAN and MS features are extracted using multiple SABs, resulting in fused features that integrate information from both images. Within the CMFFM, instead of sending the PAN and MS features independently into the SSTM, we input the fused features alongside the MS (or PAN) features. The fused features incorporate the relevance and complementary information between PAN and MS images. Through this incorporation, spatial and spectral information are effectively integrated within the cross-modality fusion features. Consequently, this method enhances spatial resolution by incorporating additional details and reduces spectral distortion commonly associated with using a single spectral source.
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 2 Literature Review
### LoRa Primer and Its Mesh Networking  
LoRa is a kind of radio modulation technique using a proprietary spread spectrum modulation that is a derivative of chirp spread spectrum (CSS). According to the operating frequency band, LoRa can be divided into LoRa sub-GHz and LoRa 2.4 GHz. LoRa Wide Area Network (LoRaWAN) is a star network protocol based on LoRa sub-GHz. Currently, there is no LoRa 2.4 GHz network protocol.  
The research on LoRa networking mainly focuses on LoRaWAN. LoRaWAN is a star network with mature standards. The existing research mainly focuses on analyzing the standard LoRaWAN protocol and discussing how to mitigate the issues caused by signal attenuation and collisions in high-density wireless devices [11; 12].  
However, LoRaWAN does not have a mechanism for controlling frequency band selection to simplify device control layer design, reduce device power consumption, and consequently extend the battery life of battery-powered devices. Additionally, LoRa devices can transmit data to gateways at any time, causing severe packet collisions if multiple devices transmit signals simultaneously. This limitation restricts the outdoor deployment of LoRaWAN. In reference [13], the authors have found that the available uplink capacity of devices in LoRaWAN is related to the distance to the gateway and the number of devices connected to the gateway. In scenarios with a high number of gateway-connected devices, the uplink capacity may drop to an average of 100 bits per second. Moreover, in LoRaWAN, the lack of a clear channel assessment mechanism increases the probability of data packet collisions, and in high-density LoRa device environments, the data packet delivery rate can drop to 25% [11]. Another study [14] also points out that under significant path loss, star networks have a 30-70% higher packet loss probability compared to mesh networks.  
To address the shortcomings of LoRaWAN, a LoRa Mesh network system is proposed and its performance is evaluated [15]. However, current research on LoRa Mesh has not adequately addressed data congestion issues and has not conducted in-depth research on routing selection and related problems. In recent years, the LoRa network has been applied to the fields of animal rescue [16], environmental detection [17], industrial production [18], and so on. However, these applications of the LoRa network are limited to the field of data transmission and are not effectively combined with LoRa positioning technology for wide area positioning and tracking.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Expansion to Small Training Samples  
We introduced sample proportion to adjust the number of training samples in a bid to further explore the identification performance of the proposed LTS-SEI method when extended to a small number of training samples. Noteworthily, the sample proportion is defined as the ratio of the number of samples in the training set to those in the validation set. The samples of B1 are assumed to come from the source domain. The samples of B2, B5, B7, B9, B12, and B14 come from the target domain. The number of training samples in the source domain and target domain increases with increasing sample proportion. It is obvious that the greater the sample proportion, the better the recognition performance of LTS-SEI on all data subsets, as shown in Figure 12. Due to the same navigation signal formats transmitted by the two GPS satellites, LTS-SEI confuses different samples when the number of training samples is small. However, the predictive ability of LTS-SEI for new data from B2, B5, B7, and B14 considerably deteriorates when the sample proportion is less than 1.25. The recognition accuracy of LTS-SEI on all data subsets approaches saturation when the sample proportion is 3.5.  
Figure 11: Training loss curves of LTS-SEI on (**a**) A1-\(>\)A2, (**b**) A1-\(>\)A3, (**c**) B1-\(>\)B2, (**d**) B1-\(>\)B6, (**e**) B1-\(>\)B9, and (**f**) B1-\(>\)B14.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Component**} \\ \cline{2-7}  & \multirow{2}{*}{**Original I/Q**} & **Shallow** & **Deep** & **LTS-SEI (No** & **LTS-SEI (No** & **LTS-SEI (No** & **LTS-SEI** \\  & & **Confrontation** & **Alignment** & **Center Loss)** & **HOMM3 Loss)** & **LTS-SEI** \\ \hline A1-\(>\)A2 & 0.8958 & 0.9975 & 0.9375 & 0.9975 & 0.9992 & **0.9995** \\ A1-\(>\)A3 & 0.8592 & 0.9925 & 0.8667 & 0.9946 & 0.9942 & **0.9971** \\ B1-\(>\)B2 & 0.5200 & 0.5550 & 0.5708 & 0.9658 & 0.5742 & **0.9867** \\ B1-\(>\)B3 & 0.8650 & 0.9425 & 0.8392 & **0.9992** & 0.9217 & 0.9942 \\ B1-\(>\)B9 & 0.6817 & 0.6942 & 0.6800 & 0.9800 & 0.6958 & **0.9850** \\ B1-\(>\)B14 & 0.5150 & 0.4950 & 0.4908 & 0.9425 & 0.5492 & **0.9633** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiment (accuracy).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Component**} \\ \cline{2-7}  & \multirow{2}{*}{**Original I/Q**} & **Shallow** & **Deep** & **LTS-SEI (No** & **LTS-SEI (No** & **LTS-SEI** (No** & **LTS-SEI** \\  & & **Confrontation** & **Alignment** & **Center Loss)** & **HOMM3 Loss)** & **LTS-SEI** \\ \hline A1-\(>\)A2 & 0.8958 & 0.9975 & 0.9375 & 0.9975 & 0.9992 & **0.9995** \\ A1-\(>\)A3 & 0.8592 & 0.9925 & 0.8667 & 0.9946 & 0.9942 & **0.9971** \\ B1-\(>\)B2 & 0.5200 & 0.5550 & 0.5708 & 0.9658 & 0.5742 &
## 5 Simulations Results and Discussions
### Case Study 2: Home Is Equipped with DGs
#### 5.2.1 Electricity Cost  
Figure 9 shows the electricity cost of the consumer's load operation for the unscheduled and scheduled load by various optimization algorithms based on ECS for the case when DGs are equipped in the home. The unscheduled load operation gave a peak of 92 cents in the 9-10 h slot and 722 cents combined for 24 h of operation. In PSO, the maximum energy cost is 57 cents at the 9-10 time slot, whereas the combined 24 h is 602 cents. The WDO-based ECS gave 45 cents as a maximum hourly cost at the 9-10 time slot and a total energy cost of 649 cents in 24 h. Additionally, by using an ACO-based framework, it is 38 cents at the 7-8 h slot, and in 24 h, it is 639 cents. Whereas, in BFA, it is 48 cents in the 23-24 time slot and 678 cents for 24 h. In GA, it is 58 cents within the time slot of 9-10 and 643 cents for 24 h. In GPSO, it is 41 cents within the time slot of 9-10, while in GACO, it is 53 cents in the 8-9 time slot. Additionally, in GBFA, it is 64 cents in the 8-9 time slot. In GWDO-based hourly electricity cost, the highest is 43 cents at the 8-9 h slot.  
The GWDO-based ECS outperforms the existing techniques for ECS in terms of average electricity cost minimization for 24 h. The GWDO technique's average cost for 24 h is 537 cents and is 26.43% reduced compared to the unscheduled operation. Additionally, there is a 11.9%, 17.5%, 7.1%, 12.4%, 11%, 14.1%, 15.6%, and 16.8% reduction in the cases of GA, PSO, BFA, ACO, WDO, GPSO, GBFA, and GACO, respectively, when comparing with the unscheduled case.  
Figure 8: Case study 1: User comfort evaluation.
# The Status of Space Weather Infrastructure and Research in Africa
## 3 Human Capital Development
### International Collaborations  
Space weather training in Africa has been built on two models, namely:  
1. Training of trainers and their students: The first cohort of African lecturers who were converted from their fields of research to space weather research were made hosts of space weather equipment. The equipment hosts were then trained to archive  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Country** & **Year of Award** & **Award** \\ \hline [PERSON] & Uganda & 2014 & Basu Early Career Award in Sun-Earth System Science. \\ \hline [PERSON] & Uganda & 2016 & AGU Africa Award for Research Excellence in Space Science \\ \hline [PERSON] & Kenya & 2016 & Basu Early Career Award in Sun-Earth System Science. \\ \hline [PERSON] & Ethiopia & 2017 & AGU Africa Award for Research Excellence in Space Science \\ \hline [PERSON]\(\acute{\text{e}}\)[PERSON] & Burkina Faso & 2018 & AGU Africa Award for Research Excellence in Space Science \\ \hline [PERSON] & South Africa & 2018 & Basu Early Career Award in Sun-Earth System Science. \\ \hline [PERSON] & Nigeria & 2019 & AGU Africa Award for Research Excellence in Space Science \\ \hline [PERSON] & Nigeria & 2020 & AGU Africa Award for Research Excellence in Space Science \\ \hline \hline \end{tabular}
\end{table}
Table 10: Awards.  
and use the data produced for research. The pioneering students who used the data for their postgraduate research were jointly supervised by their home-based academic staff and equipment donors. This model has enabled Africans to do research at their home institutions where they were/are registered as students while also building international networks and collaborations. The model forestalled potential brain drain.
2. Intra-Africa co-supervision of postgraduate research: Many African students are given research topics by their African senior researchers who co-supervise them and also host them for research visits whenever possible.  
The weak link in these two models is the lack of investment by the researchers' home institutions in space weather infrastructure development.
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 3 Materials and Methods
### Data  
Our data comes from a major Eastern European seller and distributor of agricultural goods and equipment, operating predominantly in the Balkans region. We obtained invoice-level data for B2B transactions made between January 2019 and February 2022 (38 months in total). The dataset comprises 280,502 distinct invoices sent to 3470 different customers, containing 1,962,152 invoice lines in total. For each line, invoice ID, invoice date, customer ID, product ID, product price, and product quantity are recorded. Mean inter-purchase time over all customers is 15 days, with a standard deviation of 41 days. The company uses calendar months as grouping periods for reporting on key performance indicators and defines churn as three consecutive months of inactivity (i.e., if no purchases are made by a customer in three consecutive months, they are considered lost). However, the company is quite interested in assessing the potential benefits of using shorter churn definitions, as they might trigger new business initiatives (i.e., if customers who will not make a purchase in the following month or two can be reliably identified, they could be targeted with tailored promotions). Therefore, we also consider churn definitions of one and two consecutive months of inactivity.  
In light of these business rules, the active customer base (i.e., customers making a purchase within a given month) varies between 800 and 1605 customers, with churn rates between 4.72% and 24.01% for churn definition of three months, 6.14% to 30.48% for churn definition of two months, and 16.12% and 38.38% for a one-month churn definition. Churn rates obtained by using different definitions are illustrated in Figure 1.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 6 System Design and Implementation
### Design and Implementation of BLE Beacon Device  
The class of BLE devices that broadcast their presence through advertising packets are known as Bluetooth beacons and are widely used in applications like asset tracking, proximity sensing, and item finding. Some of the popular implementations of BLE beacons  
Figure 5: Navigating towards the BLE beacon by taking measurements in 4 locations.  
Figure 6: Arduino Nano 33 BLE sense.  
include iBeacon by Apple, Eddystone beacons by Google, and open-source AltBeacons. In this research, we programmed the Arduino Nano 33 BLE sense board as an Eddystone beacon in UID mode since it provides the best programming flexibility of its frame data. The packet structure of an Eddystone beacon, when used in UID mode (Figure 7), allocates 1-byte for frame type information, 1-byte for TX power, 16-bytes for beacon ID (10-bytes for Namespace ID and 6-bytes for Instance ID), and 2-bytes reserved for future use. The 10-byte namespace ID is unique for every Eddystone BLE beacon and can be useful for classification and organization among multiple Eddystone beacons.  
TX power for a BLE beacon signal can range from \(-\)20 dBm (0.01 mW) to +20 dBm (100 mW), and for this research, we set the value to 10 dBm considering a balance between the Bluetooth signal range and power consumption of the device. We also set the advertisement interval to 20 ms and programmed the beacon ID with a unique alpha-numeric string that allows the scanner device to find the right beacon among other nearby similar beacons.
## 4 Discussion
of ceramic particles that did not bind to the PLGA matrix, probably indicating a lack of chemical interaction between ceramic and PLGA, which can also explain the lowest mechanical resistance found in those groups. On the other hand, the composites with collagen showed a tight interaction with the HA-\(\beta\)-TCP particles, and the presence of collagen induced an increase in the wettability of the scaffolds. Biodegradable synthetic polymers, such as PLGA, are hydrophobic and would inhibit liquid absorption, decreasing cell adhesion and proliferation, preventing bone tissue remodeling [14; 15]. In vitro studies demonstrated that the enrichment of PLGA with collagen supported a greater adhesion and proliferation of cells if compared to the PLGA scaffolds alone [15; 40; 41]. Collagen-coated scaffolds showed a significantly higher compressive strength as well as a higher elastic modulus than noncoated ones. However, even if collagen improved the mechanical resistance of the biomaterials, all the scaffolds presented lower values of compressive strength and elasticity when compared to trabecular bone. The bone substitute materials should present mechanical properties similar to those of the host tissue. If the difference between elastic modules is wide, there will be a heterogeneous distribution of stresses, which may lead to failure of the bone or implant [35]. Regarding the mechanical resistance, this should allow the scaffold to be loaded with compressive and torsional tensions without any structural impairment. From a clinical point of view, the scaffolds should present minimum resistance, allowing their handling during implantation [35]. Indeed, the major disadvantage of Bio-Oss Block(r) in clinical use is its high attitude to fracture at the moment of fixation, due to its low mechanical strength and high stiffness, weaknesses that were confirmed by the results of the present study. On the other hand, the mechanical properties of the studied composites were not affected by the amount of biphasic ceramic added. [PERSON] et al. [42] and [PERSON] et al. [12] studied the effects of bioactive ceramics, BCP and HA, respectively, added to PLGA. In both studies, ceramic produced a large increase in the compressive strength and elastic modulus of the scaffolds; however, by increasing the amount of ceramic to more than 20%-30% by weight, the two properties decreased dramatically. Further in vitro and in vivo studies are needed to investigate the biological behavior of the newly developed composite materials and their potential, namely biocompatibility and bioactivity, for bone tissue regeneration.
## 3 Results
### Characterizing the Dynamic Behavior of RTV Using Molecular Dynamics Simulations  
The molecular dynamics of the RTV, Eudragit L100, and PVP K30 complex systems were examined through a detailed trajectory analysis, and the results are summarized in Table 1. We tracked these systems at three time points: the start of the simulation (0 ns), at 50 ns, and at the end (100 ns). The results indicate that both Eudragit L100 and PVP K30 show promise as effective agents for complexing with RTV, thereby maintaining the supersaturation of RTV. At the 50 ns mark, these polymers successfully interacted with RTV, but by 100 ns, this association had broken, probably due to recrystallization. Eudragit L100 is particularly notable for its role in targeted drug delivery; it can break down under specific pH conditions, like those found in the intestines, allowing for a controlled and gradual release of drugs [42]. PVP K30, on the other hand, contributes to drug stability by inhibiting recrystallization and protecting against oxidation, light degradation, and heat, and enhances the physical properties of drugs, such as their crystal stability and solubility, by forming inclusion complexes [43].  
Figure 3: Comparison of the induction time measurements for RTV in the presence of polymers (PVP K30 and Eudragit L100).  
These insights highlight the crucial role of polymer selection in drug formulations, as the stability and interaction provided by polymers can greatly affect crystallization and supersaturation, leading to higher drug efficacy. Eudragit L100's ability to enable controlled release is particularly relevant to modern drug delivery strategies, which seek to achieve precise therapeutic effects. Meanwhile, PVP K30's protective qualities are essential for extending the drug's shelf life and maintaining its effectiveness. These simulations provide valuable data on optimizing polymers to boost drug performance, offering a solid foundation for further research and innovation in drug delivery systems [44]. Overall, this study underscores the benefits of adding Eudragit L100 and PVP K30 to pharmaceutical formulations, especially in inhibiting drug recrystallization and maintaining the supersaturation of amorphous drugs, which offers a promising direction for future development in this field.
# Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles
## 2 Tracking of UAVs Using a Kalman Filter Algorithm with Error and Uncertainty Sensitivity
### Prediction Phase  
Suppose that the initial phase, \(x_{0}\sim\mathcal{O}(\hat{x}_{0},D_{0})\) with \(\hat{x}_{0}\) and \(D_{0}\) are known in advance. Since \(\alpha_{l}^{x}\) and \(y_{l}\) are uncorrelated, the state prediction \(\hat{x}_{l+1|l}=F_{l}\{x_{l+1}\}\) becomes  
\[\hat{x}_{l+1|l}=O\hat{x}_{||l}. \tag{23}\]  
Because of the state-dependent noise, the estimator must take more care in measuring the error covariance matrix. We consider \(X_{l}=X_{l}\{V(\left|x_{l}\right|)\}\). Given the tracking environment considered in Equation (20), the covariance of the corresponding responding predictive model is \(\hat{x}_{l+1|l}=F_{l}\{x_{l+1}\}\)  
\[D_{l+1|l}=OD_{||l}O^{R}+G_{l}, \tag{24}\]  
\[G_{l}=\delta\delta^{R}+\delta_{x}\delta_{x}^{R}+\overline{\delta}_{x}V\Big{(} D_{||l}+\hat{x}_{||l}+\hat{x}_{||l}\hat{x}_{||l}^{R}\Big{)}\overline{\delta}_{x}^{R} \tag{25}\]