esa-sceva/satcom-chunk-collection huggingface dataset:
# Scalp-Implanted Ultra-Wideband Circularly Polarized MIMO Antenna for Biotelemetry Systems
## 3 Results and Discussion
### Circular Polarization Analysis
#### 2.3.2 Current Distribution  
Insights into the antenna's CP characteristics are provided by the distribution of surface currents [27]. Figure 6a shows the current distribution when antenna-1 is active and antenna-2 is passive. It is evident from the figure that the currents in the left-hand radiating patch rotate counterclockwise over time, which corresponds to RHCP. In contrast, Figure 6b illustrates the current distribution when antenna-2 is operational and antenna-1 is inactive. In this scenario, the currents flowing through the right-hand radiating patch revolve in a clockwise direction, indicative of LHCP. In conclusion, the designed MIMO antenna facilitates dual polarization modes and attains polarization diversity at 2.45 GHz. Specifically, antenna-1 is RHCP, while antenna-2 is LHCP.  
Figure 5: Effect of ground-plane gap width (Wg) on antenna ARBW.  
Figure 6: Surface current distribution over the patch of (**a**) antenna-1 at 2450 MHz and (**b**) antenna-2 at 2450 MHz.  
3. _Radiation Pattern_  
Figure 7a,b depict the simulation and measurement outcomes for the CP radiation patterns within the planes defined by \(\varphi=0^{\circ}\) and \(90^{\circ}\). These outcomes were documented during testing phases conducted within an anechoic chamber, utilizing the dual-antenna configuration. For the measurements, the implantable device was placed in a minced-pork-filled model. One port of the MIMO antenna system was connected to a 50 \(\Omega\) load resistor, whereas the other port was linked to a spectrum analyzer. A transmitter horn antenna, positioned 3 m from the test antenna, was rotated in \(1^{\circ}\) steps to collect the radiated power data.  
Figure 7a shows the radiation patterns for RHCP and LHCP of antenna-1 at 2.45 GHz, showing that antenna-1 exhibits LHCP. Similarly, Figure 7b shows that antenna-2 exhibits RHCP. Both figures reveal a cross-polarization discrimination (XPD) greater than 8 dB. Thus, the designed antenna achieves polarization diversity at 2.45 GHz by supporting dual polarization modes. Furthermore, the main radiation direction for both antennas is oriented away from the body. The simulated and measured peak gains of the antennas are \(-24.1\) dBi and \(-25.3\) dBi, respectively.
## 2 Application of BRBS in the Detection of Diabetic Nephropathy Biomarkers
### mRNA-21  
mRNAs are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression. mRNAs regulate over 60% of protein-coding gene expression. Therefore, changes in their expression are associated with many diseases, including liver diseases, kidney diseases, and diabetes [70; 71; 72]. Recently, some of the literature reported that mRNAs, such as mRNA-21, mRNA-223-3p, and mRNA-377, can serve as potential biomarkers for diabetic nephropathy [73; 74].  
[PERSON] et al. [75] designed a photoelectroactive aptasensor for the detection of mRNA-21. First, chitosan was used to wrap the CuInS\({}_{2}\)-modified ITO electrode surface, preventing the dissolution of CuInS\({}_{2}\) into the solution, and providing amino groups for subsequent electrode modification. Then, the carboxyl-modified DNA\({}_{2}\) reacted with chitosan and was coupled to the electrode via amide bonds. If the hairpin DNA is recognized by  
Figure 6: Representation for the engineering processes of EGFR/VEGF biosensors [69].  
mRNA-21, Exonuclease III (Exo III) will cleave the blunt 3\({}^{\prime}\) end of DNA1, triggering the target-cycled amplification process that releases DNA3, which can hybridize with DNA2 on the electrode. The DNA2-DNA3 duplex provides a binding site for TATA-binding protein (TBP) attachment. Since TBP induces an 80-degree bend in the TATA sequence by inserting amino acid side chains on the base pairs of the dsDNA sequence [76], this blocks electron transfer, causing a sharp decrease in photocurrent intensity. The decrease in photocurrent due to steric hindrance can be used to quantify microRNA-21. This strategy combines anti-interference photoelectroactive cathode materials, enzyme-assisted target cycle amplification, and TBP-induced signal shutdown, achieving remarkable amplification efficiency. The detection limit of mRNA-21, under optimized conditions, is as low as 0.47 fM, and the linear range is from 1.0 \(\times\) 10\({}^{-15}\) M to 1.0 \(\times\) 10\({}^{-9}\) M. Further research is needed to explore the application of this DNA amplification-based PEC platform in DNA, protein, and small molecule detection.
# An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0
## 3 Industry 4.0 Safety and Exposure Assessment Tools
### Tools for Exposure Assessment and Control
Currently, there is limited research on the use of smart technologies for the exposure assessment of airborne pollutants in the workplace. [PERSON] et al. investigated the use of AI algorithms for the correlation of physical and chemical data on airborne particulate matter (PM2.5 and PM10) with approximately 90% accuracy, compared with on-site gathered data [68]. However, according to their findings, data set training is vital to maintain a model's accuracy, since changes in workplace activities and variations in indoor environments could hinder its prediction abilities. [PERSON] et al. implemented the use of computational fluid dynamic (CFD) modelling for airflow pattern simulations coupled with on-site measurements for data set training of AI prediction algorithms for sufficient removal of airborne hazardous materials [69]. The developed predictive models allowed the design of airflow control, particle removal and residual particle concentrations, while offering further input on the estimation of energy consumption, and thus sustainable development strategies. The use of deep learning techniques is gaining increasing attention in an effort to correlate low-cost sensors and real-time data with airborne pollutant concentrations and flow patterns [75; 76]. A recent study by Imani utilized deep learning to estimate PM2.5 and PM10 concentrations by analysing moderate resolution imaging spectroradiometer (MODIS) satellite images. The neural network was trained on publicly available ground scenes captured by MODIS. It linked the intensity values of the satellite image bands with particulate matter measurements at various spatial locations, offering a straightforward and cost-effective mapping of PM2.5 and PM10 across large areas [77].
## 6 Results and Discussions  
This section explores the impact of our proposed solution on the users' perceived quality when both low and high traffic loads existed. Three different topology scales are used as depicted in Table 1. Iperf testing tool was also used to inject high and low traffic loads into the simulated network. Real-time DASH-based video flows is utilised to test the performance of the RL-based solution. DASH video is divided into 4-s chunks encoded into five discrete bit rates ranging from 260 Kbps to 2998 Kbps using FFmpeg version 4.3.2 with the H.264 codec, and segmented based on GPAC MP4 Box in order to create the DASH manifest and associated files. The video content streamed by multimedia providers is the \"Big Buck Bunny\" animation [49] with a 1920 \(\times\) 1080 pixels resolution and was cut into 5 min long. The selection of hosts that partake in the experiment has been constructed to enable the traffic flow to pass through the whole network topology. In the meantime, the study has utilised Wireshark as video traffic monitoring software in the end-user's device in order to capture the received video segments during video streaming. Once the streaming ended, the monitoring step on the client device is terminated and saved on the PCAP file. It is noteworthy that the video that was finally broadcast is not available; therefore, the PCAP file is used to collect video segments and recreate the processed video. Now that the processed video is ready, the objective study to determine the QoE metrics values is possible to be executed.
## 1 Introduction
multi-Scale Sampling Decoder can set an adaptively sized convolutional group according to the current feature map dimensions. This group comprises convolutional kernels of varying sizes, capturing the organ information of different dimensions. The evaluation of MINTFormer demonstrates superior segmentation accuracy and robust generalization capabilities compared to existing models, with notable advantages in reducing the computational complexity of medical image segmentation tasks. The principal contributions of the study are as follows:  
1. This study presents a novel encoder structure, the Heterogeneous encoder, designed to leverage the strengths of both MaxViT and CSWin. The heterogeneous encoder integrates the capacity of CSWin to capture global information with the aptitude of MaxViT to discern local information, thereby optimizing the advantages of both models. 2. We devised the Demodulate Bridge within a skip connection. The Demodulate Bridge is capable of filtering redundant textures in the feature maps output by CSWin and MaxViT while retaining the features extracted by the encoder. Then it combines feature maps with disparate focus to obtain feature maps that retain both local and global information. 3. In the decoder component, MINTFormer is equipped with a multi-Scale Sampling Decoder. This decoder employs a series of convolution groups, each comprising a set of convolution kernels of varying sizes. This approach enables the decoder to fully preserve organ information of different sizes during upsampling. This study is organized as follows: Section 2 introduces the related techniques incorporated in MINTFormer; Section 3 details the newly proposed MINTFormer; Section 4 presents the experimental results, demonstrating the superiority of MINTFormer; and Section 5 concludes this study. The source code will be available at [[https://github.com/fdiskdc/mintformer](https://github.com/fdiskdc/mintformer)]([https://github.com/fdiskdc/mintformer](https://github.com/fdiskdc/mintformer)) (accessed on 11 June 2025).
# Digital Health Technologies to Support At-Home Recovery of People with Stroke: A Scoping Review
## 4 Discussion
### Clinical Focus of Digital Monitoring  
Among the studies reviewed, mobility was the most frequently targeted domain. This is consistent with the critical importance of mobility for independence after stroke [36; 37]. Technologies such as the SAM and the SECOSP demonstrated high accuracy and reliability in mobility assessment. SAM effectively tracked stride counts in chronic hemiparetic stroke patients [26], while SECOSP outperformed conventional IMU-based systems [29]. Similarly, pressure-sensitive mats and accelerometers reliably measured transitional movements such as SiSt, offering valuable feedback for tracking recovery progress [30].  
Upper extremity function, another vital area of stroke rehabilitation, was addressed in several studies using technologies like SULAM, vibrotactile wristbands, and the SyMPATHy smart cup [28; 32; 33; 34]. These tools supported both monitoring and sensory-motor enhancement, with preliminary evidence supporting their effectiveness in promoting UE recovery [28; 33]. Accelerometer-based feedback increased patients' awareness of paretic limb use, although it did not always translate to objective functional improvement [27]. A recent scoping review confirmed that most wearable technology applications in stroke rehabilitation have focused on UE recovery, particularly hemiparetic arms, while LE monitoring remains understudied. Our findings align with this observation but expand on it by highlighting the monitoring and evaluation angle, not just intervention [38].  
Conversely, only a few studies targeted cognition and activities of daily living (ADLs) [31]. This is a notable gap, considering the high prevalence of post-stroke cognitive impairments and their impact on independence and quality of life [39]. TS, a sensor-based cognitive assistive technology, showed potential to enhance ADL performance by guiding users through daily tasks [31]. Given the relevance of cognition in functional recovery, more research is warranted in this domain [40].  
Additionally, physiological status monitoring received limited attention, though it holds promise for comprehensive at-home stroke care [41]. One study demonstrated the feasibility of using a wireless wearable device for continuous ECG and EMG signal tracking, even in environments where traditional monitoring tools are inaccessible [35]. This technology enables clinicians to remotely assess cardiac and muscular health, potentially reducing the need for frequent hospital visits [42].
# An Efficient Explainability of Deep Models on Medical Images
## 3 Materials and Methods
### CNN Models Used  
In this essential step of the proposed approach, as illustrated in Figure 5, the pre-trained CNN models for input image classification are performed which are VGG16, VGG19, Resnet50, EfficientNetB7, MobileNetV2 and Mixnet. This step requires a great deal of time and expertise to manually extract the relevant features from the images. However, with the development of CNN models, this step has become automated and more efficient.  
The input layer plays a crucial role in preparing the image for the classification process. It processes the raw image, applying normalization, resizing and other transformations to ensure a consistent representation for the proposed models. Next, the feature extraction layer is performed which is responsible for extracting significant information and features from the image. It can be composed of one or more convolution, pooling and activation layers. Convolution layers filter the input image to extract visual features such as contours, textures and patterns. Pooling layers reduce the spatial dimension of the extracted features, thereby reducing the number of parameters and making the model more robust to variations  
Figure 4: Different methods of data augmentation.  
in the position of objects in the image. Activation functions introduce non-linearity into the model, enabling it to capture complex relationships between features. Finally the output layer performs the final classification of the image. This layer assigns probabilities to each possible class, indicating the likelihood of the image belonging to each of these classes. It is usually composed of softmax neurons that normalize the output scores into probabilities.  
It is important to note that this architecture and the way the layers work can vary from one CNN model to another. Each model has its own set of layers and specific operations, which influence its ability to extract features and perform classification.
# Interconnected Government Services: An Approach toward Smart Government
## 4 Results--Model Evaluation
### A Model Based on Weighted Digraphs
The weights \(t_{ui}\) and \(t_{ie}\), for \(i=1,\ldots,9\), represent the time in milliseconds needed for data virtual collection and processing. Since the weights \(t_{ui}\), as well as the weights \(t_{ie}\), for all \(i=1,\ldots,9\), are very small, in regard to the time spent, we can assume that \(t_{ui}\approx t_{u}\) and \(t_{ie}\approx t_{e}\), for all \(i=1,\ldots,9\), where \(t_{u}=\max\limits_{1\leq i\leq 9}t_{ui}\) and \(t_{e}=\max\limits_{1\leq i\leq 9}t_{ie}\). It holds that \(t_{u},t_{e}\in\left[50,500\right]\). Therefore, equality (3) reduces to  
\[w(G_{2})\approx t+9(t_{u}+t_{e}) \tag{4}\]  
Considering the previous assumptions and observations, we can conclude that \(w(G_{2})\) approximately belongs to the interval \(\left[1.809\cdot 10^{5},9.09\cdot 10^{5}\right]\). By comparing (2) and (4), i.e., since \(w(G_{1})\in\left[8.1\cdot 10^{6},3.89\cdot 10^{9}\right]\) and \(w(G_{2})\in\left[1.809\cdot 10^{5},9.09\cdot 10^{5}\right]\), we obtain \(w(G_{1})\gg w(G_{2})\), which means that the proposed model is significantly cheaper regarding the time spent obtaining the documentation required for applying for child benefits than the existing one.
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 3 Software Platforms
### Animation Design Interface  
In the ADI, the coordinates of UAVs representing specific formations can be designed by users. The two main functions of the ADI are the formation drawing and the flight mission simulation.  
First, the longitude \(Lon^{0}\) and latitude \(Lat^{0}\) of each UAV is determined in the formation drawing function by drawing a bitmap or by Photoshop design bitmap, as shown in Figure 6a, where each dot represents the center of a UAV. The horizontal direction is set as the x-axis and the vertical direction as the y-axis in the bitmap. In addition, the height of the UAVs and the tilt angle along the x-axis or y-axis can be configured. In this function, the global TAP is solved off-line to obtain the optimal match based on the coordinates of the initial and target formations when the drawing is completed. The interface converts the coordinates of UAVs into way-point positions, which are saved into a text file for simulation and real-time control.  
The way-point information structure is shown in Figure 5, where R, G, and B, respectively, represent the red, green, and blue of the three primary colors which are used to control the light color of the UAV LED module. T is used to indicate the maintenance time of the formation, and only plays a role in the automatic execution mode of the formation control. Lon, Lat, H, respectively, represent the longitude, latitude, and relative ground height of the target point. Specifically, Lon and Lat can be calculated by  
\[Lon =Lon^{0}+\frac{x_{Lon}}{\triangle Lon} \tag{17}\] \[Lat =Lat^{0}+\frac{y_{Lat}}{\triangle Lat} \tag{18}\]  
where \(\triangle Lon\) is the longitudinal distance change in meters when the longitude changes by one degree under the same latitude. Similarly, \(\triangle Lat\) is the latitudinal distance change. It should be noted that the influence of the curvature of the earth is ignored in (13) and (14), since the entire UAV-LS area is small enough to be regarded as a plane.  
Second, the saved way-point information is loaded in the flight mission simulation function. The function is designed to simulate the UAVs-LS graphics in a virtual environment as shown in Figure 6b. To simplify the simulation process, each UAV moves to the target point in a straight line at the same speed. The user can view the simulation process from any perspective to examine the performance of the UAVs-LS.
# ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
## 6 Experiments  
In this section, we empirically evaluate the proposed ZenoPS system in various simulated settings. We test the performance of ZenoPS in both the synchronous and asynchronous mode, where the asynchronous experimental setting represents edge computing with flexible workers, and the synchronous experimental setting represents a traditional datacenter. Furthermore, we test the robustness of ZenoPS under various attacks in both synchronous and asynchronous modes.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 3 Test Methods and New Approach
### Input Window for Interpolation Algorithms  
The different interpolation methods were more stable and required less CPU time if the input was constrained to a time window. The window stretched from half window (HW) samples to the past and HW samples to the future. For the single-delay scenario, the same number of HW past samples, but only one future sample was included.  
For windowed WSI and Lagrange good results were achieved with HW = 20. For Kriging, HW was set to 25 after testing different values (Section 4.1). Multiplication with a window function was not necessary as Kriging inherently applies weighting of the input samples.
## 3 Methodology
### Overview of Proposed Method  
The real-time transmission of ship status data comprises three primary stages (see Figure 1). Initially, at the ship's end, the ship status data undergoes encoding to reduce the message length for transmission. This encoding process encompasses quantization, predictive coding, and arithmetic coding steps. Subsequently, the compressed message is transmitted via the Beidou system to the intended receiver. Finally, upon reception, the receiver decodes (recovers) the message, initiating a reverse arithmetic decoding step followed by another predictive coding step. It is noteworthy that all blocks, except for quantization, maintain a lossless nature.  
For a formal definition of the input, steps, and output involved in the encoding and decoding processes, refer to Algorithms 1 and 2 for detailed descriptions. Subsequent sections will elaborate on each step sequentially, providing in-depth insights into the methodologies.  
```
Input \(\mathbf{x}_{t}\in\mathbb{R}^{n}\): current data to be compressed \(\{\mathbf{y}_{\mathbf{r}}\}_{t=t-m}^{t-1}\in\mathbb{Z}^{n\cdot m}\): synced recent historical data \(P:\mathbb{Z}^{n\cdot m}\to\mathbb{Z}^{n}\) predictor \(Q:\mathbb{R}^{n}\to\mathbb{Z}^{n}\) quantizer \(A_{e}:\mathbb{Z}^{n\cdot m}\to\) binary string arithmetic encoder with pre-calculated frequency table Output \(c\): encoded message \(\mathbf{y}_{t}\gets Q(\mathbf{x}_{t})\) \(\mathbf{y}_{t}^{\prime}\gets P(\{\mathbf{y}_{\mathbf{r}}\}_{t=t-m}^{t-1})\) \(\mathbf{e}_{t,i}\leftarrow(\mathbf{y}_{t}-\mathbf{y}_{t}^{\prime})_{i}\bmod L _{i}\) \(c\gets A_{e}(\mathbf{e}_{t})\)
```  
**Algorithm 1** Encoding algorithm  
```
Input \(c\): encoded message \(\{\mathbf{y}_{\mathbf{r}}\}_{t=t-m}^{t-1}\in\mathbb{Z}^{n\cdot m}\): synced recent historical data \(p:\mathbb{Z}^{n\cdot m}\to\mathbb{Z}^{n}\) pre-trained predictor \(A_{d}:\) binary string \(\to\mathbb{Z}^{n}\) arithmetic decoder with pre-calculated frequency table Output \(\mathbf{x}_{t}^{\prime}\in\mathbb{R}^{n}\) approximation of the original data \(\mathbf{e}_{t}\gets A_{d}(c)\) \(\mathbf{y}_{t}^{\prime}\gets P(\{\mathbf{y}_{\mathbf{r}}\}_{t=t-m}^{t-1})\) \(\mathbf{y}_{t,i}\leftarrow(\mathbf{y}_{t}^{\prime}+\mathbf{e}_{t})_{i}\bmod L _{i}\) \(\mathbf{x}_{t}^{\prime}\leftarrow\mathbf{y}_{t}\)
```  
**Algorithm 2** Decoding algorithm