esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
The following abbreviations are used in this manuscript:  
AP & Average precision  
APL & Average precision for large objects  
APM & Average precision for medium objects  
APS & Average precision for small objects  
CIoU & Complete Intersection over Union  
CSP DarkNet & Cross Stage Partial Networks  
DIoU & Distance Intersection over Union  
E-ELAN & Extended efficient layer aggregation network  
EIoU & Efficient Intersection over Union  
FN & False negative  
FP & False positive  
FPN & Feature pyramid network  
GFLOPs & Giga floating-point operations per second  
GIoU & Generalized Intersection over Union  
GPU & Graphics processing unit  
IOU & Intersection over Union  
MPConv & Max-pooling convolution  
PANet & Path Aggregation Network  
R-CNN & Region-based convolutional neural network  
ResNet & Residual Network  
SIoU & Scylla Intersection over Union  
SSD & Single Shot MultiBox Detector  
SPP-Net & Spatial pyramid pooling network  
TP & True positive  
UAV & Unmanned aerial vehicle  
VGG & Visual Geometry Group Network  
WIoU & Wise Intersection over Union  
YOLO & You Only Look Once
# Exploring CP Array Antenna Characteristics and Its Applications to a Deployable Structure
## 2 Design and Experimental Results  
### Power Amplifier Module Design  
Before proceeding with the detailed antenna design, it is crucial to clarify the input signal characteristics from the PA modules, as they directly influence the interface requirements of the transmitting antenna such as the type or dimensions of the cables. In the current development phase, KERI has its own PA module design for ground and airborne environments and has successfully conducted WPT tests. Although the mechanical characteristics of these PA modules, such as size and weight, may differ from those required for a 27U CubeSat application, reviewing the input signal to the transmitting antenna remains valuable.  
Figure 2 presents a photograph of the developed PA module on the evaluation board along with its measured output gain characteristic at the center frequency of 5.8 GHz. The two-stage GaN-HEMT MMIC PA, designed using a 0.25 \(\upmu\)m process from WIN Semiconductor, operates at 5.8 GHz with a fully matched 50 \(\Omega\) characteristic. The alumina substrate-matching circuit is designed considering wire bonding and parasitic elements from the device assembly package. All components were extracted using HFSS 3D EM simulation and incorporated into the ADS Momentum. The measured 1-tone test results at the center frequency of 5.8 GHz are shown in Figure 2b, indicating a gain of 24.5 dB to the continuous wave (CW) or pulsed input signal with an output power of 47.6 dB. A drain efficiency (DE) of 44.9% is obtained under an operating voltage of 28 V. As depicted in the photograph of the evaluation board and its measured results, the PA module effectively generates power and has been tested as part of a WPT system in both ground and airborne environments. Furthermore, it has the potential to be further developed in the near future to meet the requirements of WPT systems for space applications. Please be aware that the detailed design schematic inside the PA module and additional measured results are not included in this manuscript as they are beyond the scope of the transmitting CP antenna design.
## 3 Model and Algorithm Design
### Model Optimization Strategies
#### 3.2.1 Data Augmentation and Regularization
For the task of monitoring driver characteristics, the target function can be expressed as follows:  
\[L=\alpha L_{classification}+\beta L_{regression}+\gamma L_{regularization} \tag{20}\]  
where \(L_{classification}\) is the classification loss used to classify the driver's state, such as fatigue and non-fatigue states; \(L_{regression}\) is the regression loss for predicting physiological signals, such as the heart rate and respiratory frequency; \(L_{regularization}\) is the regularization term to prevent overfitting; and \(\alpha\), \(\beta\), and \(\gamma\) are hyperparameters for the respective loss components, which are tuned based on experimental results.
# Applying CRISPR Technologies for the Treatment of Human Herpesavirus Infections: A Scoping Review
## 3 Results
### Overview of CRISPR Strategies Used to Target Different Human Herpesviruses
#### 3.2.5 CRISPR Systems Targeting the Integrated Latent Human Herpesvirus 6A Genome  
So far, only one study [37] employed the CRISPR-Cas9 system to remove the integrated HHV-6A genomes from the host telomeres of HHV-6A-infected cells. Prolonged Cas9 expression in HHV-6A-infected HEK293T cells upon lentiviral transduction of Cas9 together with plasmid transfection of multiple gRNAs targeting the noncoding region of the direct repeat regions (DRs) between the DR1 and the DR6/7 locus reduced the HHV-6A genome copies by about 70% compared to the control. Transient Cas9 expression through transfection with a CRISPR-Cas9-expressing plasmid decreased the HHV-6A genome in 65% of the cells, further decreasing in up to 80% of the cells when a second round of the transient plasmid delivery was performed 3 weeks later. The study also demonstrated the ability of the CRISPR-Cas9 system to excise the inherited chromosomally integrated HHV6A (iciHHV-6) genomes in approximately 57% of transfected smooth muscle cells (SMCs) [37].
# Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges
## 2 Research Methodology
### Trends in Path Planning Optimization  
The recent studies show a focus on UGV optimization in path planning, evidenced by approximately 57% of the scientific papers published in 2023 and 2024 addressing this topic. The International Federation of Robotics (IFR) identifies the top applications for professional service robots as transportation and logistics, hospitality, agriculture, professional cleaning and search and rescue applications [16]. Figure 2 shows that unit sales data from 2022 and 2023 reveal that transportation and logistics lead the market, with sales increasing from 84,000 units to 113,000 units. Hospitality and agriculture also showed growth, while professional cleaning and search and rescue applications remained relatively stable between the two years. This data underscores the increasing adoption of service robots across various sectors.  
Figure 1: Evaluation of the literature search procedure in three steps (PRISMA). Source: authors, 2025.
## 2 Spatial Domain Chaotic Watermarking
### Host Image Shuffling and Watermark Embedding  
The chaotic watermark was the Boolean sequence generated from the chaotic logistic map iterations. The indexing function came from sorting the chaotic sequence. The indexing matrix of pixel permutation was based on the generated chaotic sequence. The generated column indexing sequence was used for shuffling the pixels of the host image. The bitwise scrambling was conducted between the chaotic watermark and the shuffled host image. XOR binary operation (A\(\oplus\) B or Exclusive OR) was conducted to embed the chaotic Boolean vector or chaotic watermark into the four Least Significant Bits (LSBs) at each target pixel on the host image such that initial chaotic watermarking was made. By joining corresponding bits in binary, the chaotic watermarked image was produced in the spatial domain. For each component (R, G, B) of the true color digital image of size of M \(\times\) N in the spatial domain, the same chaotic watermarking scheme was applied. Shuffling may increase sensitivity to various attacks and enhance security. Similar to watermark embedding process, XOR binary operation can be conducted once again for potential watermark extraction.
# Maximizing the Capacity of Edge Networks with Multicasting
## 3 System Architecture
### Multicast Tree  
In contrast to the one-to-one unicast transmission mode, multicasting follows the one-to-many transmission mode. A separate link for each source-destination pair need not be established and the content copy and distribution only happens at the branches of the multicast tree.  
During cooperative transmission, the relays form a multicast tree with the SBS where the content is located at the root node, and the leaf nodes are the SBSs near users that have requested the same content. In the multicast tree, if the source node or relay node has multiple child nodes, it can send content to all child nodes concurrently. Therefore, multicast can use fewer hops to achieve the same purpose, which, compared to unicast transmission, greatly reduces the transmission distance. In other words, the advantage of multicasting is that the content is only transmitted once to multiple receivers on the repeated path of multiple links. This greatly reduces the average transmission distance during dense link transmission, which indirectly affects the system capacity. In the edge network, SBSs have limited storage and computing capabilities and most tasks/services rely on cooperation between SBSs. This indicates that there are many cross-links in the network. Using multicasting to build a multicast tree for content transmission can reduce the transmission delay, optimize the network performance and increase the network capacity.  
We next analyze the generalized representation of system capacity and calculate the average transmission distance in different cases.
###### Abstract  
This paper proposes a composite channel virtual time reversal mirror (CCVTRM) for vertical sensor array (VSA) processing and applies it to long-range underwater acoustic (UWA) communication in shallow water. Because of weak signal-to-noise ratio (SNR), it is unable to accurately estimate the channel impulse response of each sensor of the VSA, thus the traditional passive time reversal mirror (PTRM) cannot perform well in long-range UWA communication in shallow water. However, CCVTRM only needs to estimate the composite channel of the VSA to accomplish time reversal mirror (TRM), which can effectively mitigate the inter-symbol interference (ISI) and reduce the bit error rate (BER). In addition, the calculation of CCVTRM is simpler than traditional PTRM. An UWA communication experiment using a VSA of 12 sensors was conducted in the South China Sea. The experiment achieves a very low BER communication at communication rate of 66.7 bit/s over an 80 km range. The results of the sea trial demonstrate that CCVTRM is feasible and can be applied to long-range UWA communication in shallow water.  
long-range UWA communication; shallow water; vertical sensor array; composite channel; virtual time reversal mirror +
Footnote †: journal: Sensors
# Robust Detection of Background Acoustic Scene in the Presence of Foreground Speech
## 5 Results and Discussion
### Performance Evaluation
#### 5.3.3 Complexity Considerations  
In the previous session, the comparisons were made based on the small- and large-footprint model. However, ensemble methods that combine different models increase the system complexity. It is important for a fair comparison to be made with the knowledge of system complexities. In Table 5, the system complexity of all the extensions of the small-footprint model and the large-footprint model is displayed. When considering ensemble methods, \(NF\_ote\_1\), \(NF\_ote\_2\), and \(M\_vote\) double the model size while \(M\_NF\_vote\) and \(Meta\_learner\) triple the model size. Here, we do not consider the complexity of cruise-net since it can also be replaced by other speech enhancement systems.  
In Figure 3, the performances of both small- and large-footprint \(ResNet_{F}\) baseline and the corresponding best systems are plotted. It can be observed that the proposed methods improve the foreground speech robustness for both the small- and large-footprint models. It should be noted that the small-footprint \(Meta\_learner\) shows similar performance to the large-footprint \(ResNet_{F}\) baseline, which indicates that though relatively compact, the proposed \(Meta\_learner\) can efficiently fuse different models.
## 3 Saudi Dialect Identification Approach
### Saudi Dialect Data Collection Phase  
Previous studies have shown that isolated words and individual phonemes can be successfully used for dialect identification [29,30]. Accordingly, a dialect corpus that mostly consisted of words or very short sentences was built for the purpose of this study.  
In fact, the Saudi regional dialect is divided into four main categories [31]; these are:  
* Hejazi: The dialect spoken by native speakers in the west of Saudi Arabia, which includes the Makkah and Al-Madinah regions.
* Najdi: The dialect language spoken by native speakers in central Saudi Arabia, which includes the Riyadh and Al-Qasim regions.
* Janoobe: The dialect language used by native speakers in the south of Saudi Arabia, which includes the Aseer region, Najran city and Jazan city.
* Hasawi: The dialect language used by native speakers in the east of Saudi Arabia, which includes the Al-Hasa region and Al-Dammam city.  
Therefore, the four Saudi regional dialects were collected from Arabic social media data, such as blogs, discussion forums, and reader commentaries, given that the language of such social media is typically dialect language. Data were also collected from Twitter, based on the number of dialect regional hashtags over the period from January 2020 to May 2020, which included _zjadlenghi zjadlenghi zjadlenghi zjadlenghi_  
Table 1 shows the statistics for the collected dataset. As displayed, there was a total of 3768 dialects sentences.
# Comparative Analysis of Discrete Subtraction and Cross-Correlation for Subpixel Object Tracking
## 2 Materials and Methods
### Experimental Setup  
As we said above, the aim of this manuscript is to show the capabilities of the Discrete Subtraction Method in comparison to an interpolated cross-correlation function to track subpixel displacements in real experiments. To this end, a target was attached to a micrometric linear stage, and a sequence of the moving target was acquired. Then, all frames of a sequence were compared with the first one by using both methods here discussed. Frame-to-frame displacements and positioning errors were analyzed and discussed.  
As we explained in the Introduction, the tracking methods have been tested with four different targets, which are shown in Figure 1. These targets were printed and attached to a rectangular plate that was put into a continuous linear motorized stage (DDSM100/M from Thorlabs), with a minimum incremental movement of 500 nm and repeatability of \(\pm 1.5\) mm.  
For the test, the platform horizontally displaced the object a total distance of 4 mm at an average speed of 0.4 mm/s. In order to guarantee uniform illumination, the whole setup was placed inside a portable photo booth with led stripes and reflective walls. Also, the camera parameters were adjusted until the squared test was uniformly black (therefore the background was not white but gray), and this setting was maintained in the following captures for all the other tests. Finally, we would like to underline that for all objects, the image was printed in black ink, but all frames were inverted before any further processing.  
The sequence was captured with a Basler acA800-510 \(\upmu\)m camera, with a spatial resolution of 800 \(\times\) 600 px and 4.8 \(\times\) 4.8 \(\upmu\)m pixel size. To stabilize the temperature of the camera, a heat sink was attached to it through thermal grease. In Figure 2, we show a picture of the whole setup, including the camera and the photo booth.  
Subpixel displacement between two consecutive frames has been achieved by setting the camera at 5 m from the target, together with the camera's temporal resolution. The supporting screen is squared with a size of 150 mm \(\times\) 150 mm thus giving a px to mm ratio of 1.434 px/mm. The temporal resolution was fixed at 300 fps, so a total of 4000 frames were acquired in each shot. According to the speed of the motorized stage and the camera parameters, the displacement between two consecutive frames is 0.0019 px, or, equivalently, 1.3 \(\upmu\)m.
## 2 Aviation Fuel Emissions
### Aviation UHC Emissions
The EI-UHC of both blends was lower than that of diesel and decreased with higher blend ratios, attributed to the higher fuel oxygen content in biodiesel. They also note that engine load/brake power had a greater impact on EI-UHC than biodiesel blending [151]. In a test report by engine and aircraft manufacturers [152], GE found that adding Bio-SPK to conventional aviation fuel slightly reduces NOx emissions (1-5%) but increases CO and UHC emissions by 5-9% and 20-45%, respectively. The increase in CO and UHC emissions is primarily due to changes in the carbon-to-hydrogen ratio of the fuel, which lowers peak flame temperature, as well as changes in spray quality and flame position. Rolls Royce's tests in the annular rig indicated that CO, NOx, and UHC emissions from Syntroleum Fischer-Tropsch fully synthetic aviation kerosene and JP-8 fuel are similar. Pratt and Whitney's tests on a Pratt and Whitney Canada small turbofan engine showed no significant differences in UHC, CO, and NOx emissions between Jet A-1, 100% Neste oil, and their 50:50 blend. Table 3, below, summarizes information and comparative conclusions on UHC emissions from these studies. The UHC emission performance of fuel is determined by both engine conditions and fuel properties. EI-UHC is predominantly influenced by engine conditions, with the main point of contention being the composition and properties of SAFs. The closer SAFs are to conventional aviation fuels, the smaller the differences in EI-UHC. Otherwise, the physical and chemical properties of SAFs will complexly affect the entire process of atomization, combustion, and emissions, leading to either lower or higher EI-UHC.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 2 Methodology
### Adaptation Methodology
#### 2.3.2 Regularization Methods  
Our framework incorporates four key components designed to improve adaptation robustness:  
* Dropout Strategy: We introduce a systematic approach to dropout regularization during adaptation, exploring how increased network stochasticity affects the model's ability to generalize from limited adaptation data.
* Class-Balanced Learning: To address the uneven distribution of conversations across different speaker counts in the CallHome corpus, we implement a weighted sampling strategy during adaptation. This approach compensates for the natural imbalance where conversations with 2-4 speakers are significantly more frequent than those with 5-7 speakers. For example, only 10% of the conversations in the corpus contain 6-7 speakers, while over 60% feature 2-3 speakers. Our weighted sampling technique gives proportionally higher importance to underrepresented speaker counts during the adaptation process, ensuring the model receives sufficient exposure to all conversation types despite their imbalanced representation in the dataset.
* Subsampling Rate Adjustment: We address conventional temporal resolution constraints through reduced subsampling during adaptation. Typically, EEND models apply a subsampling factor of 10 to the input feature sequence due to computational constraints. Our approach aims to preserve finer temporal details that may be crucial for accurate speaker transition detection.
* Label Refinement: One of the key issues when using manually labeled data for speaker diarization is to address potential imprecisions in manual annotations. In order to address this issue, we implement a Gaussian kernel-based smoothing methodology. For each speaker \(s\), we define the original binary label sequence \(\mathbf{y}_{s}=[y_{s,1};\ldots;y_{s,T}]\) and apply a localized smoothing operation: \[\tilde{\mathbf{y}}_{s}=\mathbf{y}_{s}*\mathcal{G}\] (2) where \(*\) denotes convolution and \(\mathcal{G}\) represents a three-frame Gaussian kernel: \[\mathcal{G}(t)=e^{-\frac{t^{2}}{3\sigma^{2}}},\quad t\in\{-1,0,1\}\] (3) This operation creates smooth transitions at speaker change points while preserving label integrity in stable regions.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 8 Vulnerabilities in HE
### Administrative and Cultural Vulnerabilities in HE
#### 8.1.6 Password Security  
Good password hygiene is vital to protect information. However, the number one cause of hacks was the use of stolen credentials in 2018 according to Verizon [63], and password stuffing attacks was the number one attack vector against HE in 2019 [64]. Password stuffing is an attack-type that exploit lists of known usernames and passwords to obtain unauthorized access. When examining the password security at the six Turkish universities, the authors found various practices [4]: all of the participants were severely lacking in password policies for regular and remote users. In comparison, all password policies needed improvement to become compliant. One of the leading causes of security incidents at the Norwegian university was compromised user accounts [9; 58]. The findings from the socio-technical root cause analysis in [PERSON] et al. [45] supports the Verizon findings: the primary root-causes of compromised passwords were password reuse across multiple services, weak password strength, and low generic awareness. [PERSON] and [PERSON] also describe password problems as prominent in HE [50]. Academic accounts get exploited by malicious actors for many purposes, such as harvesting research articles, industrial espionage, and leveraging the university infrastructure to attack third parties [8; 9; 10].
# A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
## 3 Simulation Results  
The LHCP version of the antenna was simulated using the full-wave software, ANYS HFSS. Figure 9 shows the simulated results of the reflection coefficient and axial ratio as a function of the frequency.  
From Figure 9, we can see that the antenna is matched in the operational frequency of 5.8 GHz, with a bandwidth of 130 MHz. From the behavior of the \(|S_{11}|\), we can see the presence of two resonant frequencies, which is typical for these kind of antennas where two orthogonal modes are excited. It is necessary to identify the modes and corroborate their orthogonality. For that, the simulated electric field vector is shown in Figure 10. From the simulated results, we can clearly see that the first resonant mode with a resonance frequency around 5.3 GHz is orthogonal to the second mode generated around 6.0 GHz; we expect that around 5.8 GHz, both modes will have a similar intensity, but with orthogonal vectors. We can confirm this behavior with the simulated axial ratio, shown in Figure 9b, where we can see that around 5.8 GHz, the axial ratio value reaches a minimum of 0 dB, confirming the CP nature of the structure.  
Finally, Figure 11 shows the gain radiation pattern on two cut planes of the antenna (\(x-z\) and \(y-z\)). The obtained radiation pattern is suitable for UAV applications in terms of its beamwidth, exhibiting a maximum of 6.7 dBi.  
Figure 9: Simulation results for the LHCP implementation. (**a**) Reflection coefficient \(|S_{11}|\) as a function of the frequency. (**b**) Axial ratio as a function of the frequency.  
Figure 11: Simulated gain radiation pattern at 5.8 GHz for the LHCP implementation.  
Figure 10: Simulated electric field distribution in the center of the DRA and a cut-side view. (**a**) Side view at 5.3 GHz. (**b**) Side view at 6.0 GHz. (**c**) Top view at 5.3 GHz. (**d**) Top view at 6.0 GHz.