esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
One limitation of this study is that there were three separate sets of researchers who collected data, which could provide a source of error. However, all were trained by the PI, and good interrater reliability has been demonstrated for the PIG model [43]. Another limitation of this study is increased susceptibility to a transverse plane error caused by the presence of only a single lateral thigh marker, as dictated by the PIG model, when a marker cluster on the thigh may have been superior when determining Cardan angle calculations for the frontal plane of the knee and transverse plane of both the knee and hip. This was in part controlled by the addition of medial femoral condyle and medial malleoli markers. Researchers posit that standard deviations for transverse plane data at the hip and tibiofemoral were often greater than those of other planes due to the biomechanical model that was used. Additionally, skin artifacts caused by the normal displacement of skin from the bony landmark during movement and occlusion of the ASIS marker at the deepest point of the squat are significant limitations when using adhesive markers for 3D motion analysis requiring mathematical algorithms and the use of Nexus Software to control. Moreover, these data were derived from a convenience sample of a young, healthy, population and may not be representative of a patient population or the population at large. Lower extremity skeletal abnormalities of the hip and knee were not screened for and may have contributed to the results. In future research, it may be important to screen for femoral version and varus/valgus alignment at the tibiofemoral joint.  
Future research would benefit from more diverse sample populations with respect to age and injury history. It would also be beneficial for future research to address the correlation between OHDS performance differences and injury occurrence and explore gender differences and the differences in foot and ankle bony anatomy. Lastly, although [PERSON] et al. and others address the correlation between FMS(tm) OHDS scores and kinematic, as well as kinetic, data, it would be beneficial for more specific data to be available on the foot and ankle [10].
# Physical Activity Programmes in the Treatment of Addictions: A Systematic Review
## 3 Results
### Association between Mood, Physical Activity Programmes and Addiction  
With regard to the relationship between physical activity, mood and addiction, many of the works included in this review show relevant results. In particular, three studies [23; 27; 30] reported that the physical activity programmes reduced the stress of their participants, improving their adherence to the treatment of addiction. Moreover, two studies [26; 29] found that, with physical activity programmes, their participants improved their depressive states and, consequently, their commitment to the treatment for quitting the addiction.
# Recent Developments in Materials Science for the Conservation and Restoration of Historic Artifacts
## 3 Advanced Polymers for Artifact Stabilization  
### Advanced Polymers--Harnessing Their Properties for Cultural Heritage Conservation  
Advanced polymers represent a class of materials designed and engineered to exhibit superior performance characteristics compared to traditional materials. Their properties can be tailored to suit specific conservation needs, offering benefits such as versatility, reversibility, and environmental adaptability. The adaptability of advanced polymers ranges from being used in adhesives, consolidants, and protective coatings to more nuanced applications like gap fillers or as binders in restoration processes. Their properties can be finely tuned to provide the desired interaction with specific substrates. Recent developments in polymer chemistry have unveiled methods for creating formulations that can be crafted to exhibit particular behaviors under certain conditions, catering to both structural and aesthetic requirements of artifacts [38]. At the same time, advanced polymers can be engineered to bond effectively with a wide variety of materials, including wood, stone, textiles, and metals, with this compatibility being crucial for conservation efforts that often involve multi-material assemblies [39].  
A critical aspect of using polymers in conservation is the ability to modify or reverse treatments, thus answering one of the basic requirements of conservation science. Advanced polymers can be engineered to be soluble in mild solvents or possess thermal reversibility to facilitate removal without compromising the artifact's integrity; on the contrary, classical recipes (such as Paraloid, but also other classical polymeric materials) are only chemically reversible. This aspect could be an issue for the treatment of porous materials, in which the polymers can penetrate the support material structure in depth. Engineered polymers are capable of releasing specific substances over time, such as corrosion inhibitors or fungicidal agents, ensuring prolonged protection without ongoing maintenance [40; 41]. Many advanced polymer applications involve non-invasive methods that minimize disruption to the original material. This is particularly important when dealing with fragile artifacts where traditional methods may cause damage.  
The shift toward sustainable development has led to the innovation of eco-friendly polymers, including biopolymers and biodegradable options that offer reduced environmental impacts without sacrificing performance [42], an aspect that will be presented in a separate section. Surpassing many synthetic counterparts, eco-friendly polymers are designed to minimize ecological footprints, being often characterized by non-toxic degradation processes, making them suitable for use in environmentally sensitive conservation sites [43]. Using such alternatives, the developed materials can also be recycled or repurposed after their initial use, contributing to a circular economy within the conservation field.
## 3 Digital Twin for HEMS
### Research Methodology  
As discussed, the primary aim of this research is to propose and validate an innovative framework that integrates DT technology into HEMSs. The framework is designed to address key challenges in residential energy management, ensuring the system can adapt to diverse home configurations and varying IoT deployments. Providing intuitive user interfaces with actionable insights for energy optimization. Reducing computational and operational demands through standardized DT models. By bridging the physical and digital domains, the proposed framework enables real-time simulations, predictive analytics, and precise energy monitoring, empowering users to adopt sustainable energy practices while optimizing energy consumption.  
To achieve the outlined scientific aim, this study adopts a systematic methodology comprising the following steps:  
1. Literature Review: Conducted a comprehensive review of existing HEMS and DT frameworks to identify gaps, establish the novelty of the proposed framework, and define objectives for its development.
2. Framework Design and Development: Developed a DT-enabled HEMS framework, focusing on objectives such as real-time visualization, spatial segmentation, energy tracking, and predictive analytics.
3. Case Study Implementation: Applied the framework to a real-world scenario, simulating a residential building project. The case study incorporates IoT data and spatial modeling to validate the practical functionality of the framework.
4. Simulation and Performance Testing: Conducted simulations to assess the framework's performance in achieving scalability, usability, and resource efficiency. Metrics such as energy consumption patterns, environmental monitoring accuracy, and system responsiveness were evaluated.
5. Validation and Comparison: Benchmarked the results against traditional HEMS approaches to demonstrate the added value of the DT integration in terms of enhanced user experience, energy optimization, and scalability.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Feature/ Method** & **Non-Intrusive IoT Setup** & **Pre-Designed DT Framework** & \begin{tabular}{c} **Real-Time** \\ **IoT-DT T-Duration** \\ **Integration** \\ \end{tabular} & **Scalability** & \
\begin{tabular}{c} **Ease of** \\ **Installation** \\ \end{tabular} &
\begin{tabular}{c} **Cost-** \\ **Effectiveness** \\ \end{tabular} & **Interoperability** \\ \hline [PERSON] et al. (2017)[13] & & & ✓ & ✓ & & & \\ \hline [PERSON] et al. (2020) [14] & & & ✓ & ✓ & & \\ \hline [PERSON] et al. (2020) [15] & & ✓ & ✓ & ✓ & & ✓ \\ \hline [PERSON] et al. (2023) [18] & & ✓ & ✓ & ✓ & & ✓ \\ \hline [PERSON] et al. (2022) [20] & & ✓ & ✓ & ✓ & & \\ \hline [PERSON] et al. (2023) [21] & & ✓ & ✓ & ✓ & & ✓ \\ \hline [PERSON] et al. (2024) [26] & & ✓ & ✓ & ✓ & & ✓ \\ \hline This Paper & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of features and methods across related works.  
This structured methodology ensures a systematic approach to validating the framework's scientific and practical contributions to the field of residential energy management.
## 4 Experimental Setup and Results
### Visualization Analysis  
The motivation of GRM roots in the potential bias of the vanilla network, which attempts to capture the most prominent characteristics of lesions and may ignore the sub-clinical ones. To demonstrate whether the GRM can remit the issue or not, Figure 5 compares the visualization results between the vanilla network and the one applied with GRM on five retinal diseases cases. The first row represents a relatively simple sample that contains abnormals in the center of the image. It can be found that both of the two networks have precisely identified the lesions. For the sample shown in the second row, it can be seen that the vanilla network biases to the right-most lesions and neglects the abnormals located in the center. On the contrary, the GRM has accurately discovered most of the lesions. Similar results can be observed in the third sample. For the fourth sample containing complicated lesions, the vanilla network biases the  
Figure 4: Convergence comparison between the vanilla ResNet-50 and ResNet-50 applied with the proposed GRM on the retinal disease, pneumonia, and glaucoma tasks, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Network** & **GRM** & **Cutout** & **Mixup** \\ \hline \multirow{4}{*}{Retinal diseases} & Inception-V3 & **96.7** & 94.3 & 95.8 \\ \cline{2-5}  & ResNet-50 & **96.3** & 94.1 & 92.8 \\ \cline{2-5}  & DenseNet-121 & **96.0** & 95.1 & 95.6 \\ \cline{2-5}  & ViT & 92.6 & 92.0 & **92.8** \\ \hline \multirow{4}{*}{Preumonia} & Inception-V3 & **92.8** & 89.2 & 91.2 \\ \cline{2-5}  & ResNet-50 & **94.2** & 89.7 & 91.5 \\ \cline{2-5}  & DenseNet-121 & **92.1** & 92.0 & 91.0 \\ \cline{2-5}  & ViT & **91.8** & 91.1 & 89.1 \\ \hline \multirow{4}{*}{Glaucoma} & Inception-V3 & **91.6** & 89.0 & 91.4 \\ \cline{2-5}  & ResNet-50 & **90.6** & 87.3 & 89.4 \\ \cline{1-1} \cline{2-5}  & DenseNet-121 & **90.0** & 89.6 & 88.8 \\ \cline{1-1} \cline{2-5}  & ViT & **88.5** & 86.2 & 86.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of accuracy (%) between the GRM and related methods on the three medical image analysis tasks.  
right-bottom areas, while the GRM has precisely identified intricate lesions. In the fifth sample, both networks found the most distinguished lesion on the right side, whereas only the GRM has identified the nearby subtle lesions.  
These visualization results demonstrate two points. First, the DNNs are object detectors that attempt to discover the abnormalities in the input image. It performs well in those images that contain prominent characteristics, such as the sample shown in the first row in Figure 5. Second, the vanilla DNNs may fail to capture the prominent and subtle lesions simultaneously for the image comprised of complicated features. With the help of GRM, the DNNs can efficiently utilize the context information and show much better performance than the vanilla network.  
Figure 5: Qualitative comparison between the vanilla ResNet-50 and the ResNet-50 applied with the proposed GRM on five retinal disease cases. Each row denotes a case.
## 2 Materials and Methods
### Improvements in YOLOv5s Network Model
#### 2.2.1 ECA Attention Mechanism  
The ECA-Net captures cross-channel interactions with one-dimensional convolution instead of fully connected layers. The mechanism follows a principle of localized cross-channel interactions in which each channel only interacts with its k neighboring channels, and k is adaptively determined based on the number of channels. A one-dimensional convolution efficiently implements the process, which generates attention weights based on dependencies between channels. As illustrated in Figure 2, the ECA attention module initially conducts global average pooling on the feature maps of H \(\times\) W \(\times\) C, compressing each feature map of H \(\times\) W \(\times\) 1 into a single value, thereby yielding an output array of 1 \(\times\) 1 \(\times\) C. This process calculates the average response of each channel, capturing its global information. Subsequently, a 1D convolution with a kernel size of k is employed, effectively replacing the two fully connected layers found in the SE-Net.  
The convolution kernel size of the ECA-Net is derived as an adaptive function of the number of input channels, as described in Equation (1). This equation calculates the logarithm of the channel count (\(c\)), adds the offset (\(b\)), and divides by \(r\) to adjust the scale. The resultant value (\(k\)) is obtained by taking the absolute value to obtain a non-negative number and then converting the non-negative number into the nearest odd number. The \(odd\) in the equation denotes taking the nearest odd number. This approach effectively captures channel dependencies while maintaining a low level of computational complexity. It reduces the parameter count and the computational cost but preserves performance compared to the SE-Net.  
\[k=\varphi(c)=\left|\frac{\log_{s}c+b}{r}\right|_{odd} \tag{1}\]  
Here, \(b\) and \(r\) are constants, typically set to \(b=1\) and \(r=2\). Each channel's weight is determined via the sigmoid activation function following the convolution operation. Convolution-shared weights are employed to further enhance network performance. This method efficiently captures the information of locally interacting channels while reducing the network's parameters. The shared-weights approach is calculated in Equation (2).  
\[\omega_{i}=\sigma(\sum_{j=1}^{k}W_{i}^{j}Y_{i}^{j}),\;Y_{i}^{j}\in\Omega_{i}^{k} \tag{2}\]  
Here, \(\sigma(\cdot)\) represents the sigmoid activation function. \(W_{i}^{j}\) denotes the \(j\)th local weight matrix within the \(i\)th grouped weight matrix of the \(c\) channels, and \(Y_{i}^{j}\) is derived similarly. The final step involves multiplying the obtained weights by the original input feature map to generate a feature map with attention weights. The ECA-Net can amplify the weights of the effective channels, thus minimizing the loss of important information during the convolutional dimensionality reduction process.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 4 Results
### Result of a Generated Key Pair  
In this experiment, a key pair with a public key and a private key is generated in a logger using the OpenSSL library, and the private key is maintained in the wallet of the logger. The public key associated with the generated private key is delivered to the verifier by the key exchange scheme. The key pair is in PEM format, which is used to store and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Type** & **Name** & **Specification** \\ \hline \multirow{4}{*}{Object detector} & \multirow{2}{*}{SW} & \multirow{2}{*}{Jetson Nano Orin} & \multirow{2}{*}{8G byte (Memory)} \\ \cline{3-3} \cline{5-5}  & & & OS & Ubuntu 20.04.6 LTS \\ \cline{3-5}  & & & Language & Python 3.8.10 \\ \cline{3-5}  & & & & PyTorch 2.1.0 \\ \cline{3-5}  & & & Libraries & \begin{tabular}{c} Torchvision 0.16.1 \\ \end{tabular} \\ \cline{3-5}  & & & YOLOv8 \\ \hline \multirow{4}{*}{Logger} & \multirow{2}{*}{SW} & \multirow{2}{*}{HW} & Raspberry PI 4 & 8G byte (Memory) \\ \cline{3-5}  & & & WebCam & \
\begin{tabular}{c} Logitech Webcam \\ c270 \\ \end{tabular} \\ \cline{3-5}  & & & OS & Raspbian \\ \cline{3-5}  & & & Language & gcc 12 \\ \cline{3-5}  & & & Libraries &
\begin{tabular}{c} OpenCV 4.10.0 \\ \end{tabular} \\ \cline{3-5}  & & & & Curl 7.1 \\ \cline{3-5}  & & & OpenSSL 1.1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental setup for the designed system.  
Figure 7: HW device: Raspberry Pi (**left**) and Jetson Nano Orin (**Right**).  
transmit cryptographic keys. The PEM format is a text-safe format which is encoded using Base64. We apply the secure PEM format for transmitting the public key from the logger to the verifier over the network. Figure 8 shows the key pair.
# Gated Recurrent Attention for Multi-Style Speech Synthesis
## 4 Experiments and Results
### Tacotron2 with Global Style Tokens  
The dimensionality of the character embedding generated by the Tacotron text encoder was set to be 512. The proposed GRA and the conventional LSA were compared for style modeling. The decoder and the postprocessing networks in [4] were used to generate the mel-spectrograms from the input text sequence. The reference encoder in the GST model was composed of 6 convolutional layers followed by a single-layer of 128-unit unidirectional GRU. Instead of encoding 10 randomly initialized 256-dimensional tokens [3], we encoded 32,512-dimensional tokens into a style embedding vector to have the same dimensionality with the character embedding, by applying a content-based multi-head tanh attention [36]. The Tacotron2-GST model was trained with a batch size of 16 for 200 epochs on one NVIDIA M40 GPU. The learning rate was \(10^{-3}\) initially and exponentially decayed from 40k to 100k iterations by \(10^{-5}\). To facilitate the reproduction of the experiment, implementation details not mentioned in this paper are the same with an open source [37] Tacotron2 with GSTs. To synthesize the audio from mel-spectrogram, we used Parallel WaveGAN vocoder [38]. The Parallel WaveGAN was trained on the three speakers in the M-AILABS US dataset and a subset of VCTK corpus [39], which consists of all speech clips of 12 selected speakers.
## 4 Formulating the Expression of the Total Number of Transmissions and Receptions
### Formulating \(N_{r}^{p}\)
#### 4.2.1 Calculating \(\mathbf{N_{r1}^{q}}\)  
As shown in Figure 6, the nodes in shadow area \(\mathbf{S_{3}}\) would receive the packet. Thus, \(\mathbf{N_{r1}^{q}}\) is calculated as follows:  
\[\mathbf{N_{r1}^{q}}=\mathbf{N_{r}^{q}}\times N\times\frac{\overline{\mathbf{S_{3}}}}{S}. \tag{36}\]In the above formula, \(\overline{S}_{3}\) can be expressed as follows:  
\[\overline{S}_{3}=\pi r_{t}^{2}-\overline{S^{\prime}}_{2}. \tag{37}\]  
Because the nodes are uniformly distributed and \(0<d(O,O^{\prime})\leq r_{t}\), the probability \(p\{d\leq x\}\) equals  
\[F_{2}(x)=p\{d\leq x\}=\frac{\pi x^{2}}{\pi r_{t}^{2}}=\frac{x^{2}}{r_{t}^{2}}. \tag{38}\]  
Thus, the PDF \(f_{2}(x)\) is  
\[f_{2}(x)=F^{\prime}{}_{2}(x)=\frac{2x}{r_{t}^{2}}. \tag{39}\]  
Combining Equations (31), (37) and (39), we obtain  
\[\overline{S}^{\prime}{}_{2}=\int_{0}^{r_{t}}\biggl{(}2r_{t}^{2} \arccos\frac{x}{2r_{t}}-\frac{x}{2}\sqrt{4r_{t}^{2}-x^{2}}\biggr{)}\frac{2x}{r_ {t}^{2}}dx=\biggl{(}\pi-\frac{\sqrt{3}}{4}\biggr{)}r_{t}^{2}. \tag{40}\]  
\[\overline{S}_{3}=\pi r_{t}^{2}-\overline{S^{\prime}}_{2}=\pi r_{t}^{2}- \biggl{(}\pi-\frac{3\sqrt{3}}{4}\biggr{)}r_{t}^{2}=\frac{3\sqrt{3}}{4}r_{t}^{ 2}. \tag{41}\]  
Thus, we obtain  
\[N_{r1}^{q}=N_{t}^{q}N\frac{3\sqrt{3}}{4}r_{t}^{2}. \tag{42}\]
## 5 Coverage Analysis
Moreover, it is possible to provide coverage at longer distances (more than 50 km) for both LoRa and NB-IoT technologies when rough terrain features (e.g., hills, forests, mountains) permit receiving signals via unobstructed line-of-sight (LOS) paths. \[PL_{Rural}=PL_{Urbm}-4.78\times\left(\log_{10}f\right)^{2}+18.33\times\log_{10} f-40.98. \tag{6}\]0 Connection probability for LoRa and NB-IoT under Hata path loss model. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Antenna Height (m)** & **Antenna Gain (dBi)** \\ \hline Vehicle/Machinery & 2 & 2 \\ \hline LoRa GW & 5 & 5 \\ \hline LTE BS & 25 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The antenna parameters used in the XIRIO online tool.
## 4 Hybrid Check Matrix Construction (HC) Algorithm
### Outer Iterations of Non-Binary LDPC-CPM System in WSNs  
Non-binary LDPC-CPM system optimizes system performance through iterative demodulation and decoding, so the number of iterations is one of the key factors that determine system performance. The influence of the number of external iterations on system performance is examined below. In simulation, the bit length is 1536 bit and the code rate of LDPC is 2/3 and the number of iterations inside the LDPC decoder is fixed to 5 times. Figure 12 is the effect of the number of outer iterations on the BER performance of the 8-ary LDPC-CPM system. As can be seen from Figure 12, with the increase of the number of outer iterations, the BER of the system decreases and tends to converge and with the increase of the signal to noise ratio, the effect of the outer iteration on the BER is becoming more and more obvious. In the 0.5-1.2 dB region, the number of outer iterations has a very small effect on the BER but after 1.2 dB, the BER decreases rapidly with the increase of the number of iterations and converges after a certain number of iterations. After 6-8 iterations, the gain of the continuous iteration is very small. In order to reduce the delay of the system and reduce the complexity of hardware implementation, the number of outer iterations of the non-binary LDPC-CPM scheme is usually set to 8 times.  
Figure 11: The influence of different code rate on the performance of 8-ary LDPC-CPM system.  
Figure 12: The influence of outer iterations on the performance of 8-ary LDPC-CPM system.
## 2 Materials and Methods
### Aerosol Chamber Setup  
The experiments were conducted using a custom-built aerosol chamber (0.56 m \(\times\) 0.52 m \(\times\) 0.42 m) [66] with two 3D printed material holders attached. Two medical nebulizers (VixOne Small Volume Jet Nebulizer, Westmed, Tuscon, AZ, USA) were placed in the chamber. One of the nebulizers was used to generate polydisperse NaCl aerosols. The aerosolization was stopped when the particle concentration reached 900 to 1200 particles/cc, measured by the Aerodynamic Particle Sizer (APS 3321, TSI, Shoreview, MN, USA). The second nebulizer controlled the RH inside the chamber set to RH of 30%, 50%, or 70%. The two mixing fans inside provided homogeneous particle distribution in the chamber [2; 66]. A 3D rendering of the aerosol chamber and the position of these components are shown in Figure 2(left).  
Attached to the aerosol chamber are two sample holders: one holds the textile sample, and the other is empty, serving as a reference channel. Magnetic clips were used for alignment, and two binder clips were used to ensure an airtight seal, which was verified before each experiment. Particle-laden air from the aerosol chamber was aspirated through the material holder at a rate of 1 L per minute (LPM) from the APS. An additional make-up flow was provided by the building vacuum line at flow rates from 0.1 LPM to 3 LPM to reach the designed face velocity [50]. The make-up air was filtered by an in-line high-efficiency particulate air (HEPA) filter. A differential manometer (UEi EM201B) measured the pressure drop. The APS was connected to the holders through anti-static tubing to minimize deposition through electrostatic attraction.  
Figure 1: Microscopic images of the tested materials. Photos are taken at 2\(\times\) magnification and are shown with a 1 mm scale.