esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation Through Anomaly Detection Algorithms
#### 5.2.2 Random Forest  
Random Forest [29] is an ensemble learning method that constructs multiple decision trees and merges their results to improve classification accuracy. This algorithm is effective for handling complex non-linear relationships between water quality parameters (such as pH, turbidity, TDSs, and temperature) and their corresponding quality labels (normal, borderline, and abnormal). By aggregating predictions from several trees, Random Forest reduces the likelihood of overfitting and ensures more stable, reliable classifications. It  
Figure 18: Isolation Forest AUC.  
is particularly useful for water quality monitoring, where the data might be noisy and the relationships between features are not always linear. Additionally, Random Forest can provide feature importance rankings, helping to identify which parameters are most influential in determining water quality. Figure 19 presents the Random Forest AUC.  
In this study, the feature importance for water quality classification was evaluated using the Random Forest model. Based on the experimental results, the impact of four key water quality parameters (pH, TDSs, turbidity, and temperature) on water quality classification is ranked as follows (from highest to lowest feature importance score) in Figure 20:  
* Turbidity: 0.42;
* TDSs (total dissolved solids): 0.30;
* pH: 0.18;
* Temperature: 0.10.  
The order of importance of the features showed that turbidity and TDSs were the two most important factors affecting the detection of water quality anomalies. This result indicates that these factors are closely related to changes in water pollution. On the other hand, pH and temperature have relatively less influence, but they are also indispensable factors.  
Figure 19: Random Forest AUC.  
Figure 20: Feature importance scores from Random Forest model.
## 3 Proposed FFT Kernel Architecture
### Algorithm and Architecture Overview
For each set of 128-sample data streams, the proposed MRM\({}^{2}\)DF kernel was prepared to process six-stream 128-point FFT. Figure 8 presents the detailed timeliness diagram of MRM\({}^{2}\)DF hardware operations and signal flow chart based on the radix-2/8/8 algorithm. The inclusion of GI duration in the time available for processing OFDM FFT is an efficient approach. However, for conventional MDC/MDF FFT structures operating at the input sample rate [20; 21; 22; 25; 26], the operations were generally idle during GI clock cycles, thereby reducing hardware efficiency. By contrast, the proposed MRM\({}^{2}\)DF scheme used all 160 clock cycles, including GI\({}^{\prime}\) (Figure 8), for 128-point FFT operations. Figure 6: (**a**) Block diagram of proposed MRM\({}^{2}\)DF FFT kernel; (**b**) module-associated operations. Figure 8 illustrates that BF2 and W\({}_{128}\) multiplications were performed concurrently for all six data streams. BF8 and W\({}_{64}\) multiplications were performed on eight stream-by-stream, based on a time unit of eight cycles (i.e., a time slot (TS)) for the operation of each stream. The radix-2/8/8 signal flow chart reveals that only half of the BF2 outputs (i.e., BF2 subtraction terms) were multiplied using W\({}_{128}\) twiddle factors. This observation indicated that W\({}_{128}\) multiplication was performed only during some of the 160 cycles (A- or B-portion mcl in green or red colors). This observation ensures that the non-occupied duration is available for W\({}_{64}\) multiplication by using the resource-shared CMU. Moreover, using the TS unit (eight cycles) for BFS/W\({}_{64}\) operations per stream allowed the efficient use of GI\({}^{\prime}\) for the processing of additional streams. In the aforementioned example, GI\({}^{\prime}\) had 32 cycles (i.e., T\({}_{2}\)), and T\({}_{3}\) was half of T\({}_{2}\). Thus, six TS units (T\({}_{4}\)) were available for BF8/W\({}_{64}\) operations for six-stream 128-point FFT. Similar schemes can be applied to other GI\({}^{\prime}\) situations by evaluating T\({}_{1}\)-T\({}_{4}\) terms in Figure 8. Table 1 lists T\({}_{1}\)-T\({}_{4}\) parameters associated with various operating modes of 128-point FFT. In the 64-point FFT configuration (Figure 6), the proposed scheme provided only BF8/W\({}_{64}\) operations based on the BF8/W\({}_{64}\)-related portions of the timing schedule (Figure 8). \begin{table}
\begin{tabular}{c c c c c c} \hline
**GI\({}^{\prime}\) Clock** & **No. of Streams** & **T\({}_{1}\) Clock** & **T\({}_{2}\) Clock** & **T\({}_{3}\) Clock** & **T\({}_{4}\) Clock** \\
**Cycles** & **(max.)** & **Cycles** & **Cycles** & **Cycles** & **Cycles** \\ \hline
32 (I/4 symbol) & 6 & 160 & 32 & 16 & 48 (6 TS) \\
16 (I/8 symbol) & 5 & 144 & 16 & 8 & 40 (5 TS) \\
0 (None) & 4 & 128 & 0 & 0 & 32 (4 TS) \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters for various operating modes. Figure 7: Operation flow for the generation of 128-point FFT block based on 1024-point MDF FFT.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 2 Materials and Methods
### Identification of Linkage between Meteorological and Hydrological Drought and Lag Time
#### 3.1.2 Analysis of Hydrological Droughts  
Figure 4 depicts the temporal variations of hydrological drought periods in sub-basins 1 and 2 at different time scales, indicating the occurrence of hydrological drought over the last three decades. Hydrological drought, like meteorological drought, was more sensitive to short-term thoughts than long-term droughts. Drought events occurred continuously from 1983 to 2015, with the driest period for the study area being 1998-2004. Drought patches were also randomly observed in 1993, 1998-2004, and 2009. Sub-basin 1 was stressed by hydrological drought, and the number of drought events was high from 1985 to 2004, with a slight wet condition from 2005-to 2015. However, in the case of sub-basin 2,  
Figure 3: Difference in meteorological drought (SPI-12) severity at both sub-basins representing the spatial variations across the Soan river basins.  
Figure 2: Temporal variations of meteorological drought periods during 1983–2015 in sub–basins 1 and 2.  
the results showed that the frequency of hydrological drought events was increasing in the basin from 1983 to 2015. Figure 5 shows the illustration of the difference in hydrological drought severity (SDI-12) for two sub-basins and indicates notable variations across the two sub-basin. Furthermore, based on the MK test, a statistically significant trend was observed for downstream sub-basins 2 with a Z-value of 1.83, whereas, upstream sub-basin 1 showed opposite behavior with an insignificant statistical trend. However, there still exists an increasing trend of drought at upstream sub-basin 1.
## 3 MPTCP Congestion Control Background
### Cubic  
The Cubic algorithm [40] controls congestion window growth using a gradual and smooth cubic function. It also includes a hybrid slow start mechanism to boost window growth at the start and a fast recovery mechanism to increase the window rapidly after a loss. When implemented in MPTCP, Cubic operates on each subflow independently.  
The increase in the congestion window size in Cubic is calculated as  
\[\text{CW}=S(T-D)^{3}+CW_{\text{max}} \tag{4}\]  
where  
\[D=\sqrt[3]{\frac{CW_{\text{max}}\cdot(1-\gamma)}{S}}. \tag{5}\]Here, \(\gamma\) is the multiplicative decrease factor and \(S\) is the scaling constant. \(T\) and \(CW_{\max}\) are the time elapsed from the previous window reduction and congestion window size before the last reduction, respectively.
## 4 The Blood-Brain-Barrier
### Transport across the BBB  
The BBB plays a role not only as a barrier to cells and solutes, but also as a carrier for selective drug molecules. Potential routes for the transport and permeation of small molecules and biomacromolecules across the BBB do exist (Figure 4), primarily through either paracellular or transcellular transport [57]. On the one hand, small hydrophilic molecules can cross the BBB and reach the brain through an aqueous pathway via paracellular transport. On the other hand, small lipophilic compounds enter the brain tissue through transcellular diffusion, a nonsaturable pathway, which could be optimized by modifying the physicochemical properties of the drug.  
There is a correlation between the lipid solubility of a drug and the rate at which it enters the CNS. Factors such as molecular weight (<400-500 Da) and hydrogen bonding capacity (<8-10 hydrogen bonds) compromise drug access to the brain [58]. However, there are several other examples of drugs entering the CNS that are not affected by these properties [50].  
Moreover, tight junctions limiting paracellular permeability potentially isolate the brain from many ionic nutrients, such as glucose and amino acids which are needed for brain metabolism and, therefore, the BBB endothelium also contains many specific solute transporters to allow carrier-mediated transport (CMT) of these substances. Indeed, the endothelial cells present in the BBB express transport proteins on their surface for a wide range of molecules, thereby mediating their brain influx and efflux [42].  
Regarding large molecular weight solutes such as selective peptides, proteins and larger macromolecules, the mechanisms for crossing the BBB and entering the CNS consist of binding to specific receptors on the cell surface of endothelial cells via endocytosis. This process of endocytosis can be either through receptor-mediated transcytosis (RMT), providing the main route to which these large molecular weight solutes cross the BBB and enter the brain, or through adsorptive-mediated endocytosis (AMT) or pinocytosis [57].  
In summary, the movement of molecules and drugs through the BBB is either passive or active. The former is driven by a concentration gradient from plasma to brain, with more lipophilic compounds entering more easily; the latter being facilitated by active transporters in the endothelial cell membranes [42]. However, the BBB is an obstacle for drug delivery to the brain. Therefore, increasing efforts are currently ongoing to overcome the limitations encountered in the BBB for delivery of therapeutics [52].
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Screening Models
#### 3.1.7 Nas  
The Natural Attenuation Software (NAS) serves as a screening tool developed to assess the effectiveness of various remediation methods, complemented by supervised natural attenuation [397]. NAS enables the estimation of remediation timeframes for monitored natural attenuation (MNA) [398], during which pollutant levels decrease toacceptable levels, processes addressed by NAS encompass sorption, NAPL dissolution, and biodegradation. The efficacy of NAS has been validated through successful testing at multiple sites, including NAES Lakehurst, NJ, USA (natural source degradation), Seneca Army Depot, NY, USA (source dredging), and NSB Kings Bay, GA, USA (chemical oxidation of the source zone) [399; 400; 401; 402].
# In-Depth Co-Design of Mental Health Monitoring Technologies by People with Lived Experience
## 2 Materials and Methods  
### Consultation Context  
The co-design consultation process informed the parallel development and implementation activities undertaken by members of the Flinders University Digital Health Research Lab (DHRL) on two DMHIs (digital mental health interventions). First, it involved the pilot implementation of the Actionable In-time Insights (AI2) project, a digital public health records monitoring tool designed to enable clinicians to proactively intervene in medication and appointment non-adherence with psychosocial support [16; 17]. Second, it involved MindTick--an app designed to help track outcomes and undertake therapeutic tasks in-between sessions with a therapist [54]. During this study, the Consumer and Carer Advisory Group was consulted about the underlying design principles for consumer and carer engagement with these services. Some of the design principles that were identified as a result were implemented within these services, while others are intended to inform the design of future applications.  
AI2 (also known as CareMappr) processes the health records data contained in the Australian My Health Records database, such as the Medicare benefits schedule (MBS) data records collection of health events that qualify for a public health benefit, as well as prescriptions listed under the prescription benefits schedule (PBS) data scheme. New databases are being added to this records base, such as the PD database of dispensed prescriptions.  
Once these data (sourced from the databases outlined in the previous paragraph) are gathered, the software processes that information using a set of system rules (such _as: if x event happens, raise an alert_). These algorithms will automatically flag potential anomalies that are associated with relapse risk, such as unfulfilled scripts or missed GP appointments.  
MindTick, on the other hand, is a generic information recommendation and real-time nudging mobile application for health consumers. The app is designed to be modified and adjusted for different use cases or clinical contexts. Developed in Flutter, the app interface is compatible with both Apple iPhones and Android phones. Depending upon the condition being treated, the application provides treatment information and prompts consumers to fill out surveys about their treatment outcomes. Using Google Cloud, the consumer-documented results are processed and presented in a clinician's dashboard view. By enabling proactive consumer involvement in recovery and wellness maintenance efforts and encouraging shared decision-making, the MindTick suite of apps is designed to help improve consumer outcomes.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 3 System Analysis  
The forwarding capabilities of the individual links that comprise the selected path have a large effect on the end-to-end delivery time of data bundles, which in a space DTN are affected by long disruption times and variable loss patterns. In this context, the Licklider transmission protocol (LTP) [24] is used as the convergence layer adapter (CLA) to achieve reliable and unduplicated single-hop bundle transmissions, often with 100% red part blocks (i.e., with reliable service requested for the entire data block). The LTP implements data reliability through the retransmission of lost segments. Because of the long propagation delays in space, such retransmissions can significantly increase the latency of the single-hop bundle transmission times (i.e., the link service time). The standard routing method for a space DTN uses estimations of the service time to decide the paths for the bundles [4]. However, it assumes a basic estimation method that lacks accuracy for lossy channels. In this section, an improved estimation method for the single-hop bundle service time is proposed and analyzed by quantifying its impact on optimal flow routing for the minimum end-to-end bundle delivery time.  
The analysis presented in this section seeks to quantify the steady state performance of a set of flows over a DTN that is represented by the directed graph \(G=(\mathcal{V},\mathcal{E})\), where \(\mathcal{V}\) is the node set and \(\mathcal{E}\) is the edge or link set. The network demand is expressed by an arbitrary number of \(w\) flows, where each flow \(w\) is characterized by a source-sink node pair \(s_{i},t_{i}\) and a bundle flow rate \(\lambda_{i}\): \(\mathcal{F}=\{\{s_{1},t_{1},\lambda_{1}\},\{s_{2},t_{2},\lambda_{2}\},\ldots, \{s_{w},t_{w},\lambda_{w}\}\}\). The optimal routing of these flows depends both on the link features and the service time estimation method.  
To this end, the quantification of both the the service time and the response time for the bundle transmissions over a lossy channel are needed. Note that the latter metric includes the former one in addition to the buffering time. These metrics were analyzed in a preceding work [5], and only the essential results are presented here for completeness. These results then support the proposed service time estimation method to be used for bundle routing, along with analysis of the optimal routing performance.
## 5 Experiments
### Evaluation Metrics
#### 5.1.1 Flame Image Generation Evaluation Metrics  
We use Structural Similarity (SSIM) as one of the evaluation criteria for image generation quality [33].  
\[\text{SSIM}(X,Y)=\frac{(2\mu_{X}\mu_{Y}+c_{1})(2\sigma_{XY}+c_{2})}{(\mu_{X}^{ 2}+\mu_{Y}^{2}+c_{1})(\sigma_{X}^{2}+\sigma_{Y}^{2}+c_{2})}, \tag{15}\]  
where \(mu_{X}\) and \(mu_{Y}\) represent the mean pixel intensities of images \(X\) and \(Y\), respectively; \(\sigma_{X}^{2}\) and \(\sigma_{Y}^{2}\) represent the pixel-intensity variances of \(X\) and \(Y\), respectively; \(\sigma_{XY}\) represents the covariance of images \(X\) and \(Y\); and \(c_{1}\) and \(c_{2}\) are two variables for stable division.  
The Frechet Inception Distance (FID) score is a metric that calculates the distance between the feature vectors of real and generated images [34]. A lower score indicates that the two sets of images are more similar or that their statistics are more similar. The FID score is 0.0 in the best case scenario, indicating identical images.  
\[\text{FID}(X,Y)=||mu_{X}-mu_{Y}||^{2}+Tr(C_{X}+C_{Y}-2*sqrt(C_{X}*C_{Y})), \tag{16}\]  
where \(mu_{X}\) and \(mu_{Y}\) represent the feature means of the real and generated images and \(C_{X}\) and \(C_{Y}\) represent the covariance matrices of the actual feature vectors and the generated feature vectors.  
The Learned Perceptual Image Patch Similarity LPIPS can also evaluate the distortion of generated images. LPIPS essentially calculates the similarity between activations of two image patches in a predefined network. LPIPS can be defined as follows:  
\[\text{LPIPS}(X,Y)=\sum_{l}\frac{1}{H_{l}W_{l}}\sum_{h,w}||w_{l}\odot(\hat{X}_{ hw}^{l}-\hat{Y}_{hw}^{l})||_{2}^{2}, \tag{17}\]  
where \(H_{l}\) and \(W_{l}\) are the height and width of the input features in layer \(l\) of the predefined network, respectively. \(W_{l}\) is the weight of each feature in layer \(l\). \(\hat{X}_{hw}^{l}\) and \(\hat{Y}_{hw}^{l}\) represent the normalized features of pixel \((h,w)\) in layer \(l\). \(\odot\) multiplies the feature vector at each pixel by the feature weight.
## 1 Introduction
Meanwhile, this also represents a substantial leap forward in the technology for secure satellite communications. To better position our contribution, Table 1 provides a comparative analysis of our proposed scheme against several existing physical layer security techniques. As illustrated in Table 1, our work is unique in its integration of three orthogonal domains for security enhancement. This multi-domain approach creates an exceptionally large key space and achieves a synergistic security gain, overcoming the limitations of single-domain techniques. The structure of this paper is as follows: Section II outlines the principles of PAPM, MP-WFRFT, and DM algorithms. The strategies proposed in this paper are presented in Section III, which encompasses the principles of variable polarization modulation, an analysis of MP-WFRFT parameter impact on bit error ratio (BER) performance, and the fusion algorithm of MP-WFRFT and DM. Simulation results and discussions are displayed in Section IV. Finally, Section V concludes the paper.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 4 System Architecture
### Hardware Architecture and System Integration
#### 4.1.1 Core Hardware Components  
The hardware platform integrates five core components in a novel configuration optimized for concurrent audio capture and motor control (Table 1) (Figure 3):  
ESP32 microcontroller: Dual-core 240 MHz processor with dedicated I2S peripherals, configured for asymmetric processing with Core 0 handling real-time motor control and Core 1 dedicated to audio acquisition and compression. Selected for its balanced combination of processing power, power efficiency, and peripheral capabilities.  
INMP441 MEMS microphone: A 24-bit I2S digital microphone with 61 dB SNR, providing high-quality digital audio through a direct I2S interface. Unlike analog microphones  
Figure 2: Hardware implementation and physical layout.  
requiring additional amplification and ADC stages, this digital approach minimizes signal degradation and electromagnetic interference susceptibility.  
L298N motor driver: Dual H-bridge configuration enabling bidirectional PWM control with dedicated noise filtering circuits. Supports both directional control and speed modulation with generous current capacity (2 A per channel) ensuring reliable operation under load.  
DC motors (2\(\times\)): These are 6 V, 200 RPM motors with 300 mA nominal current, powering the tank-style chassis. Identified as primary sources of electromagnetic interference at characteristic frequencies (120 Hz, 250 Hz).  
LiPo battery system: This is a 7.4 V, 2200 mAh power source with dedicated regulation pathways for motor and audio subsystems, incorporating separate regulation to minimize noise coupling.
# AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving
## 1 Introduction
The under-fitting of positive samples and over-fitting of negative samples make the model unable to thoroughly learn the features of objects, which affects the detection performance. Figure 1: Aspect ratio distribution of different objects in KITTI. Different objects have different aspect ratio ground truths in traffic scenes. For example, cars have wide boundary boxes, and pedestrians have thin boundary boxes, relatively. In this paper, an aspect-aware anchor-free detector called AspectNet is proposed to solve the above problems. It supervises the object width and height by adding an aspect prediction head at the end of the detector, which can learn different distributions of aspect ratios between different objects. Simultaneously, a new sample definition method and a loss function are proposed to alleviate the problem of sample imbalance. The contributions of this paper are as follows:  
1. A novel aspect-aware anchor-free detector is proposed to fit the distribution of the aspect ratios of different objects to alleviate the influence of variable scales on model robustness. 2. The sample definition method is improved to alleviate the problem of positive and negative sample imbalance. 3. A loss function is designed to strengthen the learning weight of the center point of the network, which solves the imbalance problem. The remainder of this paper is structured as follows: In Section 2, recent advances in object detection methods are discussed. Section 3 contains information, including specific details and improvement methods. In Section 4, the implementation of the proposed method and its comparison with existing approaches are discussed. In Section 5, the proposed method is summarized, and the future research direction is presented.