esa-sceva/satcom-chunk-collection huggingface dataset:
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 2 Proposed Methods
### Preprocessing  
Prior to model training, audio data undergoes preprocessing to standardize inputs and minimize non-speech interference. The detailed preprocessing workflow is depicted in Figure 3.  
First, given the variability in individual voice volumes, we employ the Root Mean Square (RMS) normalization method [15] to adjust the audio corpus volume, mitigating potential model misjudgments due to volume discrepancies. The formula for RMS normalization is provided in (1):  
\[LN(i)=\sqrt{\frac{I\times 10^{(\frac{r}{20})}}{\sum_{i=0}^{I-1}s^{2}(i)}}\; \times\;s(i) \tag{1}\]  
In this process, \(LN\) represents the normalized audio, \(I\) denotes the number of signal samples, \(r\) is the target volume level in decibels (dB) set to \(-23\) based on the EBU R 128 standard [16], and \(s(i)\) indicates the audio samples. Subsequently, WebRTC Voice Activity Detection (VAD) [17] removes silent and non-speech segments, enhancing data quality.  
Next, Mel-Frequency Cepstral Coefficients (MFCCs) were extracted as the primary features, supplemented by Delta and Delta-Delta dynamic features to capture temporal variations and enhance expressiveness. As illustrated in Figure 4, the extraction process involves the following steps: the audio signal is first pre-emphasized with a coefficient of 0.96875 to enhance high-frequency components; it is then segmented into 25 ms frames (400 samples) with 50% overlap; each frame undergoes a 400-point Fast Fourier Transform (FFT) to convert it to the frequency domain, followed by mapping to 20 Mel filter banks; the  
Figure 3: Preprocessing flowchart.  
Figure 2: Data augmentation architecture diagram.  
filter bank outputs are logarithmically compressed and transformed via a Discrete Cosine Transform (DCT) to yield 13 static cepstral coefficients; and Delta and Delta-Delta features are then computed, resulting in a total of 39 coefficients per frame.  
To ensure consistent feature length, MFCC features shorter than 96 frames are zero-padded to reach 96 frames, while those exceeding 96 frames are retained in full to preserve both the integrity of short sequences and the rich time-frequency information of longer sequences. Subsequently, a sliding window with a step size of 1 frame segments the features into 80 fixed frame segments, as depicted in Figure 5. This process not only increases the data volume, but also enhances the model's generalization ability by exposing it to diverse data combinations.
# Automatic Selective Encryption of DICOM Images
## 3 The Proposed Approach
### Approach II: Automatic Selective Encryption of Multi-Frame DICOM Images  
#### 3.2.1 The Multi-Frame DICOM Object  
In general, the multi-frame DICOM object is a more compact illustration of a sequence of images (single-frame sequence), that can be transferred as a single entity (single DICOM object), and each frame is identified by a sequence number. The DICOM object attributes must be linked with the pixel data from all frames as one entity. Hence, a multi-frame DICOM object is very useful to save storage space. However, it imposes some limitations in comparison with the single-frame image series; in particular, all frames in the multi-frame  
Figure 6: Mammography DICOM image (**a**) and its corresponding histogram (**b**).  
DICOM object must have the same characteristics such as frame size, pixel depth, etc. The total number of frames in the DICOM object is specified by tag (0028, 0008) in the header.  
Historically, the multi-frame DICOM object has also not been widely supported by DICOM viewers and the PACS system [35]. Therefore, only a small number of research work has been conducted on how to secure the multi-frame DICOM object. Study [36] highlights the main difference between a multi-frame DICOM object and a single-frame one. It is noticeable that the network delay to store 10,000 single-frame DICOM is ten thousand times more than storing 10,000 multi-frame DICOM object. What is more, each frame requires multiple store requests, store, and store acknowledgement in the single-frame DICOM case, while the multi-frame DICOM object requires only one request, store, and store acknowledgement. In analogy to this, the presented approach in this section introduces a cascade encryption mechanism that is suitable for the storage procedure in [36], while reducing the processing time overhead.
# LightSeg: Local Spatial Perception Convolution for Real-Time Semantic Segmentation
## 4 Experiments
### Comparison with State-of-the-Art Methods  
In Table 4, our model was compared with other lightweight real-time semantic segmentation models, revealing leading results in both speed and complexity. Here, \"IM\" indicates that the model was pre-trained on ImageNet. Notably, our model achieved a 16% faster inference speed and a nearly 56% reduction in complexity compared to the previous state-of-the-art model RegSeg, while maintaining a similar MIoU performance. Furthermore, the model was deployed using TensorRT on Jetson NX, achieving a score of 115.7 FPS and outperforming most of the results in Table 4. Notably, the improvements in speed and complexity can be attributed to the design of our LSPBlock and UpHead modules, which effectively reduce computational and memory costs while maintaining segmentation accuracy. Figure 10 displays the visual results of LightSeg and Regseg under different scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **val MIoU \(\uparrow\)** & **Speed (FPS) \(\uparrow\)** & **Pre-Training** & **GPU** & **Resolution** & **GFLOPs \(\downarrow\)** & **Params \(\downarrow\)** \\ \hline MSFNet [32] & - & 41 & IM & RTX 2080 Ti & 2048 \(\times\) 1024 & 96.8 & - \\ SwiftNetRN-18 [33] & - & 39.9 & IM & RTX 2080 Ti & 2048 \(\times\) 1024 & 104 & 11.8 M \\ FasterSeg [34] & 73.1 & 163.9 & None & RTX 2080 Ti & 2048 \(\times\) 1024 & 28.2 & 4.4 M \\ BiSeNet1 [35] & 69.0 & 40.9 & IM & RTX 2080 Ti & 2048 \(\times\) 1024 & 14.8 & 5.8 M \\ BiSeNet2 [35] & 74.8 & 42.2 & IM & RTX 2080 Ti & 2048 \(\times\) 1024 & 55.3 & 49 M \\ DDRNet-23 [36] & 79.5 (79.1 \(\pm\) 0.3) & 37.1 & IM & RTX 2080 Ti & 2048 \(\times\) 1024 & 143.1 & 20.1 M \\ RegSeg [10] & 78.13 \(\pm\) 0.48 & 35 & None & RTX 2080 Ti & 2048 \(\times\) 1024 & 39.1 & 3.34 M \\ \hline LightSeg & 76.8 & 61.1 & None & RTX 2080 Ti & 2048 \(\times\) 1024 & 16.88 & 2.44 M \\ LightSeg \(\uparrow\) & - & 115.7 & None & Jetson NX & 2048 \(\times\) 1024 & 16.88 & 2.44 M \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with state-of-the-art methods on Cityscapes. IM means ImageNet. \(\uparrow\) indicates that the deployment uses TensorRT benchmark.  
Figure 10: Visual results comparison of LightSeg and Regseg on Cityscapes validation set under various scenarios. The visualization results clearly indicate the superiority of our network over RegSeg in accurately capturing elongated objects. Moreover, the inherent local spatial perception ability of LSPConv significantly enhances the segmentation performance for large-scale objects.  
As shown in Table 5, our proposed method, LightSeg, achieves mIOU of 77.1% on the challenging CamVid test set while operating at an impressive speed of 99.4 FPS. This performance improvement is significant when compared to the previous state-of-the-art (SOTA) model, DDRNet-23 [36], as our method achieves a 5% increase in speed. Moreover, in comparison to RegSeg, our approach demonstrates a substantial speed boost of 42%.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 3 Material: WLS200S Lidar  
The database analysed here was provided mainly by a 3D long range DWL, the WLS200S, developed by Leosphere [39] and located at Site 2. Another DWL, the WLS8-5 located at Site 1 is also used.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & **Coord.** & **Elev.** & **Elev.** & **Elev.** & & & **Meas.** & **Avalable** \\  & **(N, E)** & **(m ASL)** & **(m AVL)** & & & & **Geom.** & **Every** \\ \hline \hline \multirow{8}{*}{1} & & & & & Profiler Doppler Wind Lidar & & & \\  & & & & WLS8-5 (Leosphere) & & DD, FF, CNR & Z & 3 s \\  & & & & Radiosonde RS92-SGP (Vasaila) & T, RH, DD, FF & Z & 3 h \\  & & & & Ceilometer CT25K (Vaisala) & Cloud layer bottom & Z & 15 s \\  & & & & Net Radiometer CNR1 & & & \\  & & & & (Kipp and Zonen) & & & \\  & & & & Present Weather Detector & & & \\  & & & & PWD22 (Vaisala) & Visibility & L & 14 s \\  & & & & & Barometer PTB210 (Vaisala) & P & L & 1 min \\ \hline \multirow{2}{*}{2} & 45.9080 & \multirow{2}{*}{602} & \multirow{2}{*}{42} & Scanning Doppler Wind Lidar & & & Z & 30 min \\  & & & & WLS200S (Leosphere) & & & H & 10 min \\ \hline \multirow{2}{*}{3} & 45.9235 & \multirow{2}{*}{588} & \multirow{2}{*}{28} & TEOM-FDMS & & & \\  & & & & & (Thermo Fisher Sci.) & & \\ \end{tabular}
\end{table}
Table 2: Description of the sites shown in Figure 1 with their geographical coordinates, their elevation in metres Above Sea Level (m ASL) and in metres Above Valley Level (m AVL). The instruments used in this study are described along with the parameters measured, the measurement geometry (Z refers to vertical profile, H to horizontal profile and L to local measurement) and the period at which measurements are available.
## 2 Related Work
When a target is detected and needs to be sent to the sink, the data packets are forwarded by the working node to the neighboring grid head toward the sink. With an adaptive scheduling scheme, EEDD can support both target and sink mobility. PBDAS [20] is for grid-based WSNs with a single chain, simply formed by repeatedly linking the cell heads from the farthest row left to right then the next farthest row right to left until the nearest row of the BS. In PBDAS, choosing a cell head according to the energy level increases the lifetime and robustness of the WSN. In the diffusion-based protocol, Directed Diffusion [21] proposes a data centric dissemination protocol for sensor networks. Data generated by sensor nodes is named using attribute-value pairs so that a node requests data just by sending interest messages for named data. Data matching the interests is then drawn down toward that node. Intermediate nodes can cache and aggregate data. Directed Diffusion applies the technique of initial low-rate data flooding to establish a reinforced path from source to destination. Thus, data packets are then forwarded through that route instead of being broadcast. EADD [22] changes the node's forwarding moment that depends on each node's available energy, allowing the nodes with higher available energy to response more quickly than those with lower available energy. EADD solves uneven energy consumption problem to achieve balanced nodes' energy distribution and extend the network lifetime. [PERSON] _et al_. [23] proposed a gradient model based on one-phase Directed Diffusion. This model evenly distributes the energy load among all the nodes in the network. It also takes into account the hop count from the sink to an intermediate node to satisfy some real-time applications.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 5 Simulations and Results  
In order to evaluate its efficiency and scalability, ICENET is compared to CoAP, which was chosen as it represents one of the most popular protocols for IoT environments [51; 52]. From this point forward, we assume that the term \"query\" represents an information request for both evaluated protocols.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Performance of Uplink Communications with No Hidden Nodes
#### 5.3.2. CSMA/CA Statistics  
In order to analyze how the CSMA/CA algorithm affects goodput and message loss rate, detailed performance statistics have been obtained in each device. Table 3 shows statistical results for scenarios with four and 16 devices, obtained from three network load values: 10%, 50% and 200%. Values of statistics shown were calculated by averaging the results from all the devices in the network. Table 3 shows the following statistical results:  
* Successfully transmitted frames: percentage of data frames which have been transmitted by the device and acknowledged by the coordinator. This percentage refers to the total number of data frames that MAC layers in the device attempts to transmit.
* Channel access failures: percentage of dropped frames by MAC layers in the device because the CSMA/CA algorithm has failed due to the maximum number of backoffs (_macMaxCSMABackoffs_) being exceeded. This percentage refers to the total number of data frames that MAC layers in the device attempts to transmit.
* Frame transmission failures: percentage of dropped frames by MAC layer in device because the CSMA/CA algorithm has failed due to the maximum number of frame transmission retries (_macMaxFrameRetries_) being exceeded. This percentage refers to the total number of data frames that MAC layers in the device attempts to transmit.
* Collisions: percentage of transmitted data frames for which no acknowledgment frame has been received, due to transmission failure. According to simulation conditions, the only reason that can cause a frame transmission failure is a collision. This percentage refers to the total number of data frames transmitted by MAC layers in the device.  
Table 3 shows that the percentage of successfully transmitted frames decreases as both network load and the number of devices increase, which demonstrates the results of message loss rate shown in Figure 9. Values shown in Table 3 point out that in the absence of hidden nodes, all failures in the CSMA/CA algorithm are recorded in the column \"Channel access failures\", due to the _macMaxCSMABackoffs_ limit being exceeded. The number of frame retransmissions caused by collisions is not high enough to cause the CSMA/CA algorithm to fail due to the maximum limit of retransmissions (_macMaxFrameRetries_) being exceeded.  
Figure 8: Goodput as a function of network load, with no hidden nodes.  
Values shown in Table 3--as well as results obtained from the rest of network loads and scenarios--show that the percentage of collisions increases with both network load and the number of devices in the network. Nevertheless, collision rates with no hidden nodes stays at relatively low values, below 15% in all cases. In conclusion, when there are not hidden nodes in the network, the main cause of channel access failures is due to the transmission medium being sensed as busy when CCA function is called in the CSMA/CA algorithm.
# Advancing Few-Shot Named Entity Recognition with Large Language Model
## 5 Limitations of the Study  
Our proposed method achieves significant improvements in few-shot NER by leveraging the collaboration between small and large language models. However, several limitations warrant consideration for future refinement.  
To begin with, our approach uses cosine similarity as the confidence score to determine which predictions from the small language model require refinement by the large language model. Although cosine similarity is effective due to its scale invariance and standardized range--making it a practical choice for metric learning frameworks--it may not fully capture the uncertainty inherent in predictions. For instance, when the support set poorly represents the query set's distribution, cosine similarity might overestimate confidence in erroneous predictions or fail to reflect subtle uncertainties. Alternatives such as Monte Carlo Dropout [34] or Evidential Neural Networks [35], could provide richer uncertainty estimates by modeling prediction variability or distributional properties. Nevertheless, these methods require architectural or training modifications, which are beyond the scope of this study. Investigating such alternatives in future work could enhance the robustness of confidence estimation.  
Moreover, the threshold \(\theta\), which determines which predictions are refined by the LLM, is manually selected based on the performance of validation. While this approach proved effective in our experiments, it is time-consuming and may not generalize across datasets or domains. Therefore, an adaptive or automated threshold-setting mechanism could improve the method's scalability and practicality.
## 2 Materials and Methods
### In Vivo Reporter Gene Delivery via liveGT  
The Nanoplasmid(r) DNA encoding firefly luciferase served as the reporter gene, facilitating the measurement of bioluminescence and protein expression. Rats were anesthetized with 2-3% Isooflurane. Once anesthetized, bilateral shaving of the rats' sides was performed, followed by the application of ultrasound gel to one side. The rats were then positioned on a grounding plate, and four incisions were made on each flank to expose the skeletal muscle. Intramuscular injections of 50 \(\upmu\)L (2 \(\upmu\)g/\(\upmu\)L) Nanoplasmid(r) DNA encoding luciferase were administered at an angled position with a 25-gauge needle.  
A 1 cm\({}^{2}\) square monopolar platinum electrode designed for a more uniform electric field and increased gene delivery aligned with the grounding plate was placed in direct contact with the rat's skeletal muscle, and specific pulsing conditions were applied [18]. These _liveGT_ conditions included asymmetric biphasic pulses, as well as monophasic pulses, as can be seen in Figure 2. The first group received 32 trains of 32,367 pulses at 90 V/45 V with alternating 0.5 \(\upmu\)s/1 \(\upmu\)s pulse lengths (Figure 2C). The second group received 8 trains of 32,367 pulses at 90 V/45 V with 1 \(\upmu\)s/2 \(\upmu\)s pulse lengths (Figure 2B). The third group--identical to the first group in pulse length, number of pulses, and number of train pulses--was administered at an applied voltage of 50 V/25 V (Figure 2A). Additionally, a monophasic condition was applied, consisting of square wave pulses with 8 pulses of 150 ms each at an applied voltage of 90 V (Figure 2D). This pulsing parameter was sourced from previous work yielding high myocardial gene expression [19]. All experimental conditions were compared against injection-only sites that received pDNA and did not undergo pulsing. Following completion of pulsing or injection, the incisions were closed.
###### Abstract  
Over the past decade, global screening time has increased, a trend intensified by the COVID-19 pandemic, leading to the integration of screens into daily life. Studies have documented the adverse effects of prolonged screening on ocular health and binocular vision, such as dry eye syndrome, blurry vision, headaches, myopia, and visual fatigue. However, it remains unclear if prolonged screening affects the development of colour vision defects. **Objectives**: This study aimed to determine the relationship between (a) prolonged screening and acquired colour vision deficiencies and (b) COVID-19 infection and acquired colour vision deficiencies. **Methods**: A population of 50 individuals with normal trichromatic vision, aged 20 to 30 years, with an average daily screening time of 516.7 min, was evaluated. Participants were initially screened using the Ishihara 32-plate Test to exclude those with congenital colour vision deficiencies. The Farnsworth-Munsell 100 Hue Test (FM100H) and Square Root Total Error Score (\(\sqrt{\text{TES}}\)) were used to evaluate acquired colour vision deficiencies under standardized conditions. The dataset underwent dual analysis: (1) detailed statistical scrutiny and (2) comparison of \(\sqrt{\text{TES}}\) values with historical data from 1982, 1991, 2001, and 2002. **Results**: The global group had a \(\sqrt{\text{TES}}\) (Mean \(\pm\) SD) of 5.40 \(\pm\) 1.58, the COVID-19 subgroup 5.46 \(\pm\) 1.62, and the non-COVID-19 subgroup 5.32 \(\pm\) 1.51. No significant differences were found between the \(\sqrt{\text{TES}}\) values from this population and those reported in previous studies. Statistical analysis showed no significant correlation between gender and COVID-19 infection with \(\sqrt{\text{TES}}\) values. **Conclusions**: Neither screening time nor COVID-19 infection appears to significantly impact the occurrence of permanently acquired colour vision deficiencies in individuals aged 20 to 30 years.  
acquired colour vision deficiencies; prolonged screening; COVID-19 infection; Farnsworth-Munsell 100 Hue test +
Footnote †: journal: Applied Sciences
## 8 Summary of Findings and Conclusions  
The concept and design of ARCP are comprehensively explained with respect to the effectiveness of the crack induction strategy and the steel stress distributions. In the present study, two different reinforcement design layouts of ARCP are developed to reduce the amount of continuous longitudinal reinforcement up to about 45% to 63% relative to that of CRCP. Based on the steel stress distributions and design spacing of partial surface saw-cuts, the partial steel bar of 600 mm length is used in place of the continuous longitudinal steel bar across the pavement width at crack locations. The active crack control method in the form of partial surface saw-cuts at one of the top edges of the concrete slab is applied to induce cracks at the predetermined locations in ARCP. The crack initiation from the tips of saw-cuts and crack propagation across the pavement width indicate the appropriateness and effectiveness of active crack control for ARCP. The steel stress distributions in the partial steel bar of ARCP are quite similar to those in the continuous longitudinal steel bar of CRCP. The early-age crack induction in ARCP using partial surface saw-cuts is identical to that of CRCP under the same prevailing external environmental conditions. Based on these findings, it may be concluded that the concept of ARCP using partial surface saw-cuts as a crack induction strategy could be an effective solution to resolve the shortcomings of CRCP without compromising the fundamental behavior of CRCP. This concept of ARCP using partial surface saw-cuts is quite a new development for the rigid pavement structure. Therefore, more extensive experimental and field investigations, as well as numerical studies, are required by considering traffic loading to formulate a more rational pavement structure.  
Figure 23: Comparison of maximum steel stress distribution between CRCP and ARCP.  
**Author Contributions:** Conceptualization, M.K.; Writing--Original draft, M.K.; Supervision, H.D.B.; Writing--Review & editing, A.N., N.I., and P.D.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The funding for this research along with the article processing charges (APC) have been provided by the Higher Education Commission (HEC) of Pakistan under the grant of UESTP-HRDI initiative.  
**Acknowledgments:** In the corresponding author gratefully acknowledges financial support from the Higher Education Commission (HEC), Pakistan.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results  
As previously mentioned, the AztechSat-1 nanosatellite is an academic experiment in the form of a 1U CubeSat. Although it was equipped with an integrated GPS system, its high-power consumption requirements prevented its use during the mission. Consequently, the tracking of the satellite was achieved using NORAD Two-Line Elements (TLEs) shown in Table 3, which served as the primary element for satellite tracking throughout the mission.