esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and System Specifications
### Proposed Platform Overview and Assumptions  
In order for the soil measurements to be available to the final user, specific connectivity and interoperation arrangements should be made, as explained in Figure 1. According to these arrangements, each sensor node should be able to host from one to (the set of) five different soil instruments belonging to the categories presented in Section 2.1 or similar. The behavioral characteristics of these instruments should be respected, and a very efficient powering profile should be followed. The data that each peripheral sensor node periodically transmit over LoRa are directed to a gateway/sink node. The latter node, in order to accept soil-specific information, includes a LoRa receiver, bridged with the gateway node via a USB port, thanks to a dedicated microcontroller unit (MCU). The gateway node should also incorporate functionality to further process and to potentially store and visualize the data arriving from the peripheral nodes and to make them available via the Internet to assistive cloud platforms or directly to the end users. Although the set of tasks assigned to the sink/gateway node could be addressed by a microcontroller equipped with an additional conventional network interface (e.g., ethernet or Wi-Fi), this solution is rathermarginal in terms of performance and efficiency, and thus the adoption of a small single board computer (SBC) unit is preferable due to the connectivity, programming, and storage flexibility. Specific details on the design and implementation characteristics followed are given in Section 3.  
It should also be noted that the topology of the system being presented is a basic one, designed to facilitate the study of soil properties by scientists and to provide valuable data for better agri-production and resource preservation activity planning. Consequently, is not optimized for easy utilization by the average farmer, but this can be achieved by adding complementary visualization and information delivery services on the sink/gateway node and in the cloud.
# A New DGNSS Positioning Infrastructure for Android Smartphones
###### Abstract  
One's position has become an important piece of information for our everyday lives in a smart city. Currently, a position can be obtained easily using smartphones that is equipped with low-cost Global Navigation Satellite System (GNSS) chipsets with accuracy varying from 5 m to 10 m. Differential GNSS (DGNSS) is an efficient technology that removes the majority of GNSS errors with the aid of reference stations installed at known locations. The sub-meter accuracy can be achieved when applying the DGNSS technology on the advanced receivers. In 2016, Android has opened the accesses of raw GNSS measurements to developers. However, most of the mid and low-end smartphones only provide the data using the National Marine Electronics Association (NMEA) protocol. They do not provide the raw measurements, and thus do not support the DGNSS operation either. We proposed a DGNSS infrastructure that correct the standalone GNSS position of smartphones using the corrections from the reference station. In the infrastructure, the position correction is generated considering the GNSS satellite IDs that contribute to the standalone solution in smartphones, and the position obtained is equivalent to the solution of using the range-domain correction directly. To serve a large number of smartphone users, a Client/Server architecture is developed to cope with a mass of DGNSS positioning requests efficiently. The comparison of the proposed infrastructure against the ground truth, for all field tests in open areas, showed that the infrastructure achieves the horizontal positioning accuracy better than 2 m. The improvement in accuracy can reach more than 50% for the test in the afternoon. The infrastructure brings benefits to applications that require more accuracy without requiring any hardware modifications.  
GNSS; DGNSS; smartphone; android; infrastructure; accuracy +
Footnote †: journal: Sensors
# Quadcopters in Smart Agriculture: Applications and Modelling
## 2 Types of Quadcopters
### Commercial Quadcopters
#### 2.1.7 DJI Mavic 2 Pro  
The DJI Mavic 2 Pro, shown in Figure 8, has a three-axis gimbal, enabling it to capture stable images and videos. Additionally, it features omnidirectional obstacle sensing which enhances flight safety. The Mavic 2 Pro is found in the literature in many agricultural applications such as monitoring [86; 87; 88; 89; 90], plant health assessment [91; 92], chemical spraying [93], and irrigation [94]. For example, the authors in [94] employed the DJI Mavic 2 Pro to collect and transport water samples from the Luchenza River in the Traditional Authority of Chimaliro, Thyolo District, Southern Malawi. These samples were tested and analyzed for water quality, aiding in better irrigation practices. The approach proved to be more effective and practical compared to traditional methods.  
Figure 7: DJI Phantom 4 (photo by [PERSON], sourced from Unsplash under its free license [81]).
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 5 Results
### Comparison Results  
Finally, in this section, we compare the proposed approach with an extensively verified method, such as the inverse depth parametrization with EKF [19]. Specifically, we run the benchmark toolkit that is provided with Dataset 3, Bovisa 2008-10-04, both publicly-available [62]. The results obtained with the inverse EKF parametrization method were originally discussed in [47; 63]. Figure 16 presents the comparison results. Figure 16a represents the estimated solution for both methods. The proposed approach is represented in blue, the inverse parametrization method in red and the available GPS-ground data in black. Next, Figure 16b represents the same comparison, divided into \(X\) and \(Y\) coordinates.  
It is worth noting that this toolkit computes a transformation alignment for the estimated solution. As explained in [63], the inverse depth method does not estimate the scale factor. This is the reason why the authors compute a transformation alignment by means of an offline Levenberg-Marquardt optimizer. Under these conditions, the presented approach confirms a valid estimation for a proper scale, by introducing the first prior of the odometry into the filter. Then, a robust scale factor estimate is maintained. Consequently, the main outcome can be inferred from Figure 17, where error histograms are presented for the absolute position error. Both results are presented: with the alignment optimization enabled and disabled, in the toolkit options. Figure 17a,c represents the error histograms for the proposed approach for each case. Likewise, Figure 17b,d represents the error histograms for the inverse depth parametrization method, under the same conditions. It may be observed that our approach produces improved accuracy results, with average values under 3 m, independently of the alignment setup.  
Figure 16: _Cont._Figure 16: Estimation results in Dataset 3: (**a**) position estimation, \(XY\)(m). The proposed solution is presented in blue (dark blue: GPS areas; light blue: rest); the inverse EKF solution in red; the GPS-ground truth in black; (**b**) evolution in \(X\)(m) and \(Y\)(m).  
Figure 17: \(Cont\).
# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 2 Image Denoising Method Based on Probability Statistics
### Noise Screening and Processing
#### 2.4.1 Noise Screening  
The output, M, of the buffer data pool is first reproduced into two copies, one for processing and one for evaluation. Through the relationship between the buffer data queue and the statistical data queue, the value of output M after modularization is the value of the center point of the statistical data queue. Therefore, M is first modulated and then the number of its values that have the same value as in the statistical data queue is counted, and the ratio is processed with the total number of values in the statistical data queue to generate the data frequency value. When comparing data frequency with a preset data frequency threshold, if the evaluated data frequency is not lower than the preset data frequency threshold, M is a valid pixel block. When the data frequency is evaluated to be lower than the preset data frequency threshold, M is noise. This is shown in Figure 8.  
Figure 6: Buffered data queue.  
Figure 7: The general process of noise processing.
## 3 Results  
Almost half (46%) of the patients had the typical presentation of \"hippocampal\" amnestic dementia (Table 1). Additionally, 23.5% and 15.3% fulfilled the criteria of mixed or atypical presentations, respectively, as described in the IWG-2 criteria [15]. However, the remaining 15.3% had unusual presentations, including non-logopenic PPA fulfilling current criteria for semantic and non-fluent agrammatic (PNFA) PPA subtypes [31], corticobas \begin{table}
\begin{tabular}{c c c} \hline \hline
**Clinical Presentation** & **Percentage** & **95\% CI** \\ \hline Typical AD (”hippocampal” amnestic) & 45.92\% & 33.49–61.44\% \\ Mixed disease & 23.47\% & 14.88–35.22\% \\ Mixed with cerebrovascular disease & 13.27\% & 7.06–22.68\% \\ Mixed with ischemic disease & 8.16\% & 3.52–16.09\% \\ Mixed with amyloid angiopathy & 5.10\% & 1.67–11.91\% \\ Mixed with Lewy bodies & 10.20\% & 4.89–18.77\% \\ Atypical presentations & 15.31\% & 8.57–25.25\% \\ Frontal (FTD-like) & 7.14\% & 2.87–14.72\% \\ Logopenic PPA & 7.14\% & 2.87–14.72\% \\ Posterior cortical atrophy & 1.02\% & 0.03–5.69\% \\ Unusual presentations & 15.31\% & 8.57–25.25\% \\ Non-logopenic PPA & 6.12\% & 2.25–13.33\% \\ Semantic PPA & 5.10\% & 1.67–11.91\% \\ PNFA & 1.02\% & 0.03–5.69\% \\ With NPH & 4.08\% & 1.11–10.45\% \\ CBS & 4.08\% & 1.11–10.45\% \\ PSPS & 1.02\% & 0.03–5.69\% \\ \hline \hline \end{tabular}  
* CI: confidence interval, FTD: frontotemporal dementia, PPA: primary progressive aphasia, PNFA: progressive non-fluent aphasia of the agrammatic type, NPH: normal pressure hydrocephalus, CBS: corticobasal syndrome, PSS: progressive supranuclear palsy syndrome.  
\end{table} 
Table 1: Clinical presentations of the patients with the AD CSF biomarker profile. criteria for probable corticobasal degeneration [32], Richardson syndrome fulfilling criteria for probable progressive supranuclear palsy (PSP) [33] or NPH diagnosis [34] (Figure 1). Studied groups did not differ significantly in respect to sex, disease duration, and scores in neuropsychological tests (Table 2). In the present cohort, 59% of patients had early (presenile) onset disease. Patients of the mixed group were older, had a higher age of symptom onset, and showed lower rates of early onset disease (<65 y) compared to the other groups. Figure 1: Pie chart of the clinical phenotypes of patients with an AD biochemical profile.
## 3 Analysis and Discussion
### Analysis of Time-Frequency Characteristic Parameters
#### 3.2.1 Influence of Signal Length, Frame Length and Frame Shift  
In speech signals, the frame length is usually set at 25 ms, and frameshift is taken to be 40-60% of the frame length when crafting MFCCs. However, the frequency of those relative sampled signals is usually 8 kHz or 16 kHz, which is quite different from the GM sampling frequency (100 Hz) which is used here. Therefore, the signal length, frame length, and frameshift will impact the hand-crafted features, influencing the model's performance. Thus, we additionally take four different truncation lengths (30.74 s, 40.97 s, 40.98 s, and 40.99 s) and the corresponding four sets for frame length and frameshift ((0.06 s, 0.03 s), (0.06 s, 0.04 s), (0.05 s, 0.02 s), and (0.06 s, 0.02 s)), respectively. Through the same preprocessing, we obtain four new datasets whose sample sizes are \(1024\times 12\), \(1024\times 12\), \(2048\times 12\), and \(2048\times 12\), respectively. A total of 60 networks (15 network structures) are selected for testing. The dropout and learning rates of all networks are set at 0.5 and 0.001, respectively, and the results are shown in Figure 8. It can be seen that retaining more energy and a suitably small frameshift during preprocessing improves network performance. Frame length and frameshift have a more significant influence on the task than truncation duration, they are the more critical control factors. Furthermore, the verification accuracy on the GRU network is not significantly different when feature matrices have the same size. Moreover, network performance can be improved by about 1% by increasing the feature matrix size.  
In summary, for the time-frequency features used in this study, frame length and frameshift are the controlling factors that significantly affect the network's performance. A frame length of 0.05 s and a frameshift of 0.03 s are recommended.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 1 Introduction  
### Background and Scope  
The success of cutting-edge algorithms and models in the fields of computer and robotic vision is heavily dependent on the availability of enormous and varied annotated datasets [1]. However, because of the difference between the source and target domains, applying these models to real-world circumstances frequently results in a severe performance hit. The domain shift problem is the term used to describe this occurrence [2]. The generalization capacities of computer and robotic vision systems have been improved through domain adaptation approaches, which have emerged as viable strategies to address this problem by allowing knowledge transfer across many domains [3].  
Variations in data distribution, illumination, ambient settings, and sensor properties between the source and destination domains cause the domain shift issue [4]. Traditional deep learning-based models and computer vision algorithms are naturally vulnerable to such changes, demanding robust and adaptable approaches to provide consistent performance across many domains [5]. In this study, we explore the complexities of domain adaptation in computer and robotic vision to determine how well they work to solve the domain shift problem [6]. This research examines a wide variety of domain adaptation techniques, including time-tested methodologies and cutting-edge deep learning-based approaches, all of which are designed to address the difficulties provided by domain differences.
This paper also presents an exhaustive review of the non-line-of sight (NLoS) signal's effects on the UWB positioning system and discusses the existing ML algorithms used to classify or mitigate the positioning error caused by NLoS signals and the main challenges for further work. 5. Finally, this work surveys and discusses the emerging state-of-the-art ML-based research efforts in solving the challenge associated with NLoS effects for the UWB presented and summarizes the existing popular ML algorithms for UWB IPS NLoS classification and mitigation, such as k-NN, SVM, DT, NB, and NN. This paper is organized as follows: Section 2 describes the techniques used for localization in IIoT. Section 3 describes the characteristics of UWB. Section 4 explains UWB IPSs and the principles of localization. Section 5 provides the definition of the NLoS signal and importance of applying ML algorithms for the UWB system and presents the existing ML algorithms and their comparisons. A complete section on future research directions, challenges, and limitations is discussed in Section 6. Finally, the summary and conclusions are presented in Section 7.
## 2 Background and Related Concepts
### BYOD Risks  
The expanding adoption of BYOD raises notable privacy and security concerns. According to a report by Hewlett-Packard [13], employees now utilize multiple mobile devices at work, creating a situation where their activities are invisible and untraceable to the IT department. Consequently, this trend poses various challenges and security threats for enterprises.  
Firstly, the need for clear security expectations is a primary challenge, resulting in costly consequences, particularly when inexperienced personnel are entrusted with data security. Moreover, phishing attacks pose a serious risk by compromising employee devices and company information. Using unsecured Wi-Fi networks outside the office further amplifies security risks, as highlighted by Kaspersky Lab [23]. Additionally, malware infections and unauthorized app installations pose additional threats [23,24]. Lastly, the BYOD trend raises concerns about employees accessing social media during work hours, potentially violating company policies.  
These risks associated with BYOD significantly impact organizational security, giving rise to various challenges. These challenges include implementing security policies across different BYOD operating systems and devices, effectively managing numerous BYOD devices within the organization, securing BYOD devices, tracking their activities, and monitoring their usage outside of work hours [24]. To address the above challenges, access control policies and technologies play a crucial role in mitigating risks and meeting the privacy requirements of the BYOD environment.  
As a result, this study aims to investigate appropriate access control solutions and privacy requirements within the three layers of the BYOD security architecture. The objective is to effectively tackle the privacy and security issues associated with BYOD.
## 2 Materials and Methods
### Animal Study  
The overall experimental plan is present in Figure 1. One-hundred-and-eight female Sprague Dawley (SD) rats aged 11 weeks reared on standard chow were obtained from the Small Animal Production Unit at Massey University (Palmerston North, New Zealand) and the study was conducted in the same animal facility as approved by the Massey University Animal Ethics Committee (MUAEC protocol/approval 16/112). The animal room environment was set at \(22\pm 1\)\({}^{\circ}\)C, with 45-55% humidity, and a 12/12 light-dark cycle throughout the study. The rats were singly housed in conventional cages with heat-treated aspen wood shavings as bedding. After a one-week acclimatization period, the rats were randomized into test groups and fed one of four diets (Specialty Feeds, Glen Forrest, Western Australia): (1) normal control diet (ND) containing 5% total fat (from soy oil), 5% sucrose, and 15% total protein (from casein); (2) normal control diet supplemented with GSM (ND + GSM) containing 5% total fat (84% from soy oil/16% from GSM), 5% sucrose, and 15% total protein (66% from casein/33% from GSM); (3) high-fat high-sugar diet (HFHS) containing 30% total fat (50% from soy oil/50% from lard), 30% sucrose, and 15% total protein (from casein); and (4) high-fat high-sugar diet supplemented with GSM (HFHS + GSM) containing 30% total fat (49% from soy oil/49% from lard/1% from GSM), 30% sucrose, and 15% total protein (66% from casein/33% from GSM). After 8 weeks on the experimental diets (age 20 weeks), ninety-six rats were subjected to surgery: half of them were ovariectomized and the rest were left with ovaries intact as sham control animals. The surgical procedure and anesthesia were performed as described previously [28]. These surgically manipulated rats were on their experimental diets through to the end of the study (age 48 weeks) before termination and biological samples were collected. Twelve rats from a previous study which did not undergo any surgery that were fed the normal control diet were included in the data set in order to compare knee joint scoring between rats of different age. These were sacrificed at 26 weeks of age and knee joints were collected for histopathological slide preparation. All rats were euthanized by exsanguination following anesthesia.  
Figure 1: Experimental schedule for obesity-induced osteoarthritis in the rat model with greenshell mussel. DXA = Dual X-ray energy absorptiometry; OVX = ovariectomy.
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 5 Experiment Results
### Experiment Results on Developing Ensemble-Learning-Based Models
#### 5.2.2 Performance of Ensemble-Learning-Based Hyperparameter Tuning  
This phase presents the outcomes of implementing hyperparameter tuning after conducting the training and validation processes on the Random Forest model, along with insights from comparisons with two alternative algorithms, XGBoost and CatBoost.  
Figure 11 vividly illustrates the most optimal confusion matrix obtained during the five-fold training and validation phase for the Random Forest model, following the application of hyperparameter tuning using GridSearchCV. This specific confusion matrix was derived from the rigorous five-fold cross-validation process. Impressively, the incidence of prediction errors remains notably low, with only eight occurrences, underscoring the model's impressive accuracy in correctly classifying the majority of data points.  
Figure 12 presents the most optimal confusion matrix resulting from the five-fold training and validation phase applied to the XGBoost model following hyperparameter tuning through GridSearchCV. This particular confusion matrix was constructed through the rigorous five-fold cross-validation process. Impressively, the number of prediction errors remains quite low, with just nine instances, highlighting the model's accuracy in correctly classifying the majority of data points.  
Moving to Figure 13, it shows one of the most optimal confusion matrices achieved during the five-fold training and validation phase for the Catboost model, subsequent to hyperparameter tuning via GridSearchCV. Again, this confusion matrix was a product of the five-fold cross-validation process. Notably, the incidence of prediction errors is minimal, with only 12 occurrences, underscoring the model's ability to accurately predict the majority of data points.  
Figure 11: The confusion matrix of Random Forest at fold 5 after tuning.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 6 Test Setup
### Results-Exp-B  
In this experiment, four distinct interference signals were positioned inside a car and switched on sequentially. The sensor node was relocated to obtain different measurements, with distances of 3 m, 6 m, and 9 m between the sensor and the interference signals. An RF model with 1000 trees was trained on these data. It can classify the distances, as demonstrated in Figure 18.  
The confusion matrix in Figure 18 shows that the RF model could accurately differentiate between the distances from the sensor to the interference signals, achieving a perfect accuracy of 100%. These results demonstrate that ranging is possible with ML. Therefore, they are a stepping stone to achieving localization with ML.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**AI Model** & \begin{tabular}{l} **Training Time** \\ **for the Whole Model \({}^{*}\)** \\ \end{tabular} & \
\begin{tabular}{l} **Inference Time** \\ **per Sample \({}^{*}\)** \\ \end{tabular} &
\begin{tabular}{l} **Accuracy** \\ \end{tabular} & \(\mathbf{F_{\beta=2}}\) **Score** \\ \hline RF (1000 trees) & 17.2 s \({}^{\dagger}\) & 0.071 ms & 0.949 & 0.948 \\ ResNet18 (32 epochs) & 29:56 min = 1796 s & 19.16 ms & 0.960 & 0.959 \\ \hline \hline \end{tabular}  
* The training and the inference were performed on an _Apple MI Max_, 10-core CPU, using tsaí: 0.3.1, fastai: 2.7.9, and pytorch: 1.12.1. \({}^{\dagger}\) The best values are shown in green and bold.  
\end{table}
Table 2: Performance and training speed comparison between RF and ResNet classifiers.  
Figure 17: Comparison between confusion matrices of (**a**) RF and (**b**) ResNet. Darker colors indicate more allocation of the predicted and actual class combinations: ideally, the matrix should only have values on the diagonal.