esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results And Analysis
### Analysis with Perceptual and Non-Perceptual Manipulations
For the second part of the test, for each of the manipulations listed in Table 4, 500 manipulated signals, their hash value and their BER were obtained. The average BER for each manipulation is presented in Table 7. According to these results, for perceptual manipulations the BER value (in average) is lower than 5%, while for non-perceptual manipulations it is higher (in average) than 25%. In addition to the BER average, it is important to know the complete behavior of the data for each type of manipulation. Figure 5 shows the histograms of BER values for the manipulations where the hash differs. As can be seen in Figure 5, the first three histograms are highly concentrated around the first bin, with more than 60% of the data. Only a few BER values are in the last bin (\(>\)0.20). This behavior is highly desirable for the perceptual hash, as it allows a threshold to be set for separating recordings with different content from perceptually similar files. On the other hand, in non-perceptual manipulation (Figure 5d), most BER values are in the last bin (\(>\)0.20), which means that the hash value has changed a lot, and it will not be easy to discern whether the contents match. \begin{table}
\begin{tabular}{c c} \hline \hline
**Manipulation** & **Average of BER Values** \\ \hline FLAC & 0.00\% \\ MPEG-4 AAC & 4.79\% \\ Volume & 0.99\% \\ Re-sampling & 2.75\% \\ Re-quantization & 0.00\% \\ LPF & 26.44\% \\ Noise & 41.62\% \\ Cutting & 33.45\% \\ Copy-move & 33.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of perceptual and non-perceptual tests for 500 signals. Figure 5: Perceptual tests distribution.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
###### Abstract  
The low earth orbit (LEO) constellation network has become a promising approach to provide global communication services, due to its advantages in wide global coverage, low transmission delay, and convenient networking. However, the instability of the intersatellite laser terminal and the high relative speed between adjacent satellites cause frequent network topology changing problems for data routing. In this paper, a disruption tolerant distributed routing algorithm (DTDR) is proposed, where the satellites calculate the alternate path for transmission when the network topology changes, which improves the performance of packet loss. Specifically, each satellite maintains the intersatellite link (ISL) information within a specified number of hops. When an ISL state changes within the specified number of hops, the corresponding satellite calculates and switches to the detour path. Furthermore, the traffic is balanced through the detour process. Various simulations were constructed and show that the proposed algorithm outperforms the existing algorithm in terms of packet loss ratio and transmission delay.  
LEO satellite network; distributed routing algorithm; disruption tolerant; packet drop rate; load balance Article
# DNA Nanodevices as Mechanical Probes of Protein Structure and Function
## 3 DNA Nanoscaffolds for Interrogating Ligand Spacing, Valency, and Confinement
Two sets of researchers in  
Figure 4: Controlling protein spacing, valence, and confinement with DNA objects. (**A**) DNA “nanocaliper” for spacing proteins at distances of 100 nm (left) and 40 nm (right). Reprinted with permission from ref. [26], Copyright 2014 Springer Nature. Insets show negative-stain TEM images of the calipers. (**B**) Icosahedral DNA cage for presenting the eOD-GT8 antigen (in yellow) to create synthetic vaccines. Reprinted with permission from ref. [27], Copyright 2020 Springer Nature. (**C,D**) DNA origami templates for controlling the spacing and stoichiometry of molecular motors like dynein. Reprinted with permission from ref. [37], Copyright 2012 The American Association for the Advancement of Science. (**C**) or myosin Reprinted with permission from ref. [38], Copyright 2015 Springer Nature. (**D**). (**E,F**) DNA origami rings for attaching FG-Nup proteins to mimic the nuclear pore complex. Reprinted with permission from ref. [29], Copyright 2018 Springer Nature. Reprinted with permission from ref. [28], Copyright 2018 American Chemical Society. (**G**) Negative stain TEM images (top) and cartoons (bottom) of rings with zero or 48 handles for FG-Nup proteins. Reprinted with permission from ref. [28], Copyright 2018 American Chemical Society. 2018, one led by the [PERSON] and Dekker groups [29], and one by the [PERSON] and Lusk labs [28], used a rigid DNA origami ring to mimic the nuclear pore complex. The goal of both teams was to understand the collective assembly and pore blocking function of proteins called FG-nups, by confining them to a cylindrical volume \(\sim\)40 nm in diameter (Figure 4E-G). The use of DNA origami also allowed for a tunable incorporation of proteins (from 32-48), and independent modulation of their orientation (i.e., pointing \"in\" vs. \"out\" of the ring, with only the former being mimetic of the nuclear pore). It is hard to envision another system that can control both the nanoscale area/volume available to proteins with high precision, but also the number of proteins attached to that volume, and the direction in which those proteins are oriented relative to it. The DNA origami rings balance flexibility (to create a circular structure) with rigidity (to prevent its deformation), while allowing for a stoichiometrically-defined number of proteins via the DNA handles used. Using a similar ring-like structure that encircled a liposome and could position soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins, the [PERSON] and [PERSON] groups were able to determine that only one to two SNAREs were sufficient for membrane fusion [39]. This work was particularly notable because the DNA nanostructure controlled both the number of the proteins, but also their proximity to a liposome, helping recreate a complex biological arrangement that would not have been possible without the rigidity and shape control intrinsic to the origami, as well as the multiple chemical functionalities that the structure can present.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Sequence Length Analysis Results  
The analysis of sequence length was conducted by running several tests. Based on these tests, three sequence length values were defined: sequence_length = 50, sequence_length = 75, and sequence_length = 115. Once these values were determined, the four DL models were executed. The results of these tests are presented in Table 9.  
Table 9 presents each DL algorithm's training accuracy, test accuracy, and training times with different sequence lengths. During the training phase, it was observed that GRU and BiGRU consistently yielded the highest evaluation accuracy. However, in the evaluation phase, the best results were obtained with GRU and LSTM, while the lowest performance was consistently observed with BiLSTM. Regarding the training times for each DL algorithm in our model, it was noted that GRU was trained in 297 s and LSTM in 289 s. Consequently, it was determined that the optimal sequence length is 115.
## 5 Conclusions  
This work has identified a growing interest around the application of developments in OCT configurations of LCI to the industrial domain for in situ measurement. However, due to the wealth of knowledge accrued over the past 30 years of progress in the field, an in-depth structured collation of theory with an industrial application mindset was required.  
As a result, this work for the first time presents an end-to-end holistic framework for the development of OCT configured LCI systems, linking the journey of light reflected from the object surface to the definition of the m measurand. This includes the breakdown of achieved signal contributions, practical operating limits from measurement range due to sampling rate and sensitivity fall-off, lateral resolution variation across the region of measurement, motion effects on SNR/introduction of spurious artefacts and dispersion mismatch compensation.  
In addition to identification of factors impacting the measurement signal, a simple practical approach to component selection resulting from critical operating qualifiers is also given and finally a comprehensive step by step dataflow approach to digital signal processing techniques for optimising the quality and robustness of achieved system measurements.  
Some general limitations of OCT configured spectral domain LCI measurements which still require improvement include: achieving a high lateral resolution over a large work volume, difficulty in measuring surfaces with large gradients, limited penetration depth through scattering mediums, relatively small operating range, and signal strength dependent on scatterers reflectively, impacted by motion blur and suffering from sensitivity fall-off.  
This work will equip the reader with the necessary underlying theoretical and practical information to apply the developments that have been made in OCT/ LCI to industrial applications for in process metrology.  
Figure 9: **(a)** FFT of original dataset, **(b)** FFT of zero-padded dataset, **(c)** improving resolution through fitting a 2 nd order polynomial to the FFT peak, **(d)** comparing maximum peak detection versus a polynomial peak fitting against OPD.  
**Author Contributions:** Conceptualization, T.H., L.J., J.P., P.K.; methodology, T.H.; software, T.H.; validation, T.H.; formal analysis, T.H.; investigation, T.H.; resources, J.P., P.K.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON], J.P., P.K.; supervision, [PERSON], J.P., P.K.; funding acquisition, P.K., J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors wish to acknowledge support of the UK Engineering Physical Science and Research Council (EPSRC) for funding this work (Grant Nos. EP/M020746/1, EP/L01498X/1).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 ESTCube-Luna Preliminary Mission Requirements and Platform Design Solutions
### System Engineering
The COM includes a high-speed communication (HSCOM) system, designed for data downlink and radio ranging, with X-band patch antennas on the spacecraft and repurposed radio telescopes in Ventspils International Radio Astronomy Center (VIRAC) in Irbene, Latvia. The VIRAC antennas will be limited in availability; therefore, we cannot assume this functionality for everyday operations. The low-speed communication (LSCOM) solution has an ultra-high frequency (UHF) turnstile antenna on the spacecraft and Yagi-Uda antennas at the ground station in Toravere, Estonia. The named ground stations are available within the current ESTCube-LuNa consortium. In case of insufficient data transfer, we shall look for ground segment partnerships on the other side of the Earth. The preliminary concept of operations is shown in Figure 11.  
Since DSNs are also used for navigation, we propose an alternative optical navigation solution as part of the ANS. Triangulated Celestial Navigation (TCN) or the \"tuna-can navigation system\" is equipped with five 120\({}^{\circ}\) field-of-view (FoV) cameras for maximal coverage of the entire sky. Four cameras are placed in two tuna cans on the opposite side from the E-sail payload. The fifth navigation camera is placed on the E-sail side along with the tether camera, which can double as a star tracker. The ANS includes a powerful on-board computer for image processing with FPGAs. The ESTCube-LuNa spacecraft operates in spin-stabilised mode provided by attitude thrusters (ATs) and reaction wheels (RWs). The EPS includes four deployable panels in the X-configuration for balanced power production during yearly seasons and assuming the fixedness of the E-sail spin plane. Two diagonal deployable panels would be enough to provide the same power, but in such a case, the deployed panels would tend to be aligned with the spin plane, which complicates attitude dynamics. The four-panel X-configuration keeps the 2 \(\times\) 4 U side aligned with the spin plane. Batteries and other heavier components are placed near the E-sail payload for increased arm length from the tether attachment point to the ATs. The current spacecraft design fits in 2 \(\times\) 4 cubesat units (8 U) with an estimated mass of less than 15 kg, which was assumed in the experiment simulations in Section 2. The preliminary mass budget is given in Table 3.  
Figure 11: An early ESTCube-LuNa concept of operations: initial ideas and concepts for communications and navigation. Artwork credit: [PERSON], [PERSON], [PERSON] and [PERSON].
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 3 Attitude Determination Methods
### Multiplicative Extended Kalman Method
#### 3.2.3 Gain  
The Kalman gain is used to give different weights to the measurements and the current estimate of the state. This is the weight assigned to the prediction or the observation and is defined by \(K_{k}\) as (Equation (18))  
\[K_{k}=P_{k}^{-}H_{k}^{T}(\mathbf{\hat{x}_{k}^{-}})[H_{k}(\mathbf{\hat{x}_{k}^{ -}})P_{k}^{-}H_{k}^{T}(\mathbf{\hat{x}_{k}^{-}})+R_{k}]^{-1} \tag{18}\]  
where \(H_{k}(\mathbf{\hat{x}_{k}^{-}})\) is the observation model at time \(t_{k}\). \(R_{k}\) is the measurement-error covariance matrix at \(t_{k}\). \(P_{k}\) is the state error covariance at \(t_{k}\). \(H_{k}(\mathbf{x_{k}^{-}})\) is the observation matrix and is defined by (Equation 19):  
\[H_{k}(\mathbf{x_{k}^{-}})=\begin{bmatrix}A(\mathbf{\hat{q}^{-}})B_{\mathbf{\theta }}\times&|&0_{3\times 12}\\ A(\mathbf{\hat{q}^{-}})S_{\mathbf{\theta}}\times&|&0_{3\times 12}\\ A(\mathbf{\hat{q}^{-}})N_{\mathbf{\theta}}\times&|&0_{3\times 12}\end{bmatrix} \tag{19}\]The operator \(\times\) is such that (Equation (20))  
\[\mathbf{x_{1:3}}\times=\begin{bmatrix}0&-x_{3}&x_{2}\\ x_{3}&0&-x_{1}\\ -x_{2}&x_{1}&0\end{bmatrix} \tag{20}\]  
where \(A(\mathbf{q})\) is the attitude matrix in Equation (21):  
\[A(\mathbf{q})=\|\mathbf{q}\|^{-2}((q_{4}^{2}-\|\mathbf{q_{1:3}}\|^{2})I+2\mathbf{q_{1:3}}\mathbf{q_ {1:3}}^{T}-2q_{4}[\mathbf{q_{1:3}}\times])) \tag{21}\]
###### Abstract  
Perceived exertion (RPE, RPE\({}_{\text{dur}}\)) and wellness scores (Hooper) are common methods to assess the training load and readiness in football. However, in professional football, there is a lack of data concerning the application of these tools in microccycles of different durations. Therefore, the purpose of this study was to explore the correlations between (a) RPE, RPE\({}_{\text{dur}}\), and weekly external load (EXLD) and (b) Hooper and EXLD within microccycles of different durations. Independent of the microcycle's length, RPE and RPE\({}_{\text{dur}}\) correlated with the EXLD. In MIC5, RPE and RPE\({}_{\text{dur}}\) correlated with the EXLD, except for accelerations \(>\) 3 m/s\({}^{2}\) (ACh) and distance \(>\) 25.5 km/h (SPvh). In MIC6, RPE and RPE\({}_{\text{dur}}\) correlated with the EXLD, except Ach (with RPE). In MIC7 and MIC9, RPE and RPE\({}_{\text{dur}}\) correlated with the EXLD, except RPE with ACh. For Hooper variables, independent of the microcycle duration, SRN and FTG had more correlations in comparison to SLP; the same finding was observed in MIC7 and MIC9, but SRN and SLP in MIC5 and MIC6, respectively, had no correlations. Multiple regression analysis showed that for RPE, the main predictors are total distance (TD), metabolic power distance \(>\) 50 w/kg (PWvh), and ACh, which can explain 31% of the variance, but for RPE\({}_{\text{dur}}\), 58% can be explained by DUR, TD, and PWvh. For SLP, the predictors FTG and SRN can explain a very small percentile of the variance (1%, 4%, and 6%, respectively). The main findings were the multiple correlations between RPE, RPE\({}_{\text{dur}}\), Hooper, and microccycles of different durations; most of them were found for long durations. This tendency is similar to that described in the literature for microccycles of the same duration. (a) EXLD has a stronger correlation with RPE\({}_{\text{dur}}\) than with RPE, and (b) FTG and SRN are the best predictors of the EXLD during the upcoming training session. It is recommended that fitness coaches use these tools to assess and forecast the training load; thus, they will be able to prepare more effective sessions during microccycles of different durations.  
internal load; microcycle length; RPE; Hooper questionnaire
## 2 Materials and Methods
### Questionnaire Analysis (Statistics)  
For the statistical analysis of questionnaire evaluation, Matlab(r) 2017a and RStudio (Integrated Development Environment for R, version 1.1.463, RStudio Team, 2018) software were used. Separate analyses were conducted for each part of the study since the goal in each analysis was different.  
For the first part, to check if the compared base rowing models (BIFC, LIAC) significantly differed in terms of realism based on the qualitative evaluation with the Questionnaire-1, the following linear mixed effect (_lme_) model was used.  
\[Q_{i}\ \sim\ BaseModel+(1\ |\ Expert)\] ( _lme-1_)  
where \(Q_{i}\) is the dependent variable that represents the questions in the questionnaire, and \(i=1,\ \ldots,\ 10\) is the index of each question. _BaseModel_ is the categorically independent variable with two levels defined as BIFC and LIAC. Since the models were presented twice in the training blocks, both rating values were used for each rowing model for every \(Q_{i}\) in the _lme-1. Expert_ is defined as the random factor since ratings in both models belonged to one participant.  
A separate analysis was conducted for the effect of individualization in the second part of the study. To examine if the individual adjustment of forces yielded a significant improvement in the realism ratings in Questionnaire-2 for the rowing on the simulator, _lme-2_ was constructed as follows.  
\[Q_{i}\ \sim\ Configuration+(1\ |\ Expert)\] ( _lme-2_)  
where \(Configuration\) is the categorical independent variable with two levels: the configurable base model, i.e., LIAC, and the individually configured model, i.e., IND. Since the configured model IND was rated once with Questionnaire-2 (after the individual adjustment in Training-6), the mean of two ratings for the LIAC model was retrieved from the first part of the study (Questionnaire-1) in _lme-2_.  
For both lme models, \"_lmerTest_\" package was used to check the significance by retrieving the \(p\)-values. To check the main effect of _BaseModel_ in _lme-1_ and _Configuration_ in _lme-2_, the \"_anova_\" method was used. Normality of the residuals from both lme models were visually inspected using Q-Q plots. Resultant \(p\)-values below 0.05 were regarded to show significance. The \(p\)-values between 0.05 and 0.1 were considered as 'trending towards significance.
## 4 Discussion
### Limitations  
Due to resource limitations, the interviews were conducted in English, which may have excluded patients and families from diverse cultural and linguistic backgrounds. The invitation to participate in this study was extended to all inpatient wards and units, except intensive care, coronary care, same-day care units, and emergency departments in both hospitals. However, we did not analyse data according to specific wards or units during their participation. Variations in ward-level nursing communication and culture could impact patient and family experiences. This study was conducted in only two hospitals and relied on participants' personal experiences, which may limit the transferability of the findings. Additionally, the low number of family members who participated in this study may also impede the wider application of the results. While there was a low number of family participants, the data they provided clearly indicates further work is required to engage family members in bedside handover.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Pseudo-Label Guided Learning  
The PGL framework is a critical component of the RASST model that enables effective semi-supervised learning for oriented object detection. The PGL framework leverages both labeled and unlabeled data to improve the model performance, particularly in scenarios where labeled data is scarce. By utilizing a teacher-student architecture, PGL refines pseudo-labels generated from unlabeled data, guiding the student model learning process. The PGL framework employs a teacher-student architecture, where the teacher model generates pseudo-labels for the unlabeled data, and the student model learns from both the labeled data and these pseudo-labels. The teacher model, denoted as \(T\), is updated as an Exponential Moving Average (EMA) of the student model, denoted as \(\mathcal{S}\). \(\mathcal{D}_{L}\) represent the labeled dataset, and \(\mathcal{D}_{u}\) represent the unlabeled dataset. The objective is to train the student model s using the labeled data \(\mathcal{D}_{L}\) and the pseudo-labeled data \(\mathcal{D}_{P}\) generated by the teacher model from \(\mathcal{D}_{u}\). The overall loss function used to train the student model is a combination of the supervised loss \(\mathcal{L}_{s}\) on the labeled data and the unsupervised loss \(\mathcal{L}_{u}\) on the pseudo-labeled data:  
\[\mathcal{L}=\mathcal{L}_{s}(\mathcal{D}_{L})+\lambda_{u}\mathcal{L}_{u}( \mathcal{D}_{P}), \tag{15}\]  
where \(\lambda_{u}\) is a weighting factor that balances the contributions of the supervised and unsupervised losses.
# Prefetching Method for Low-Latency Web AR in the WMN Edge Server
## 4 Experiments
### Experimental Results
#### 4.3.2 Comparative Analysis of the Network Traffic  
Figure 9 shows the results of measuring traffic when users use AR content. Figures 9a and 8b show the traffic results of the conventional method and the traffic measurement results of the proposed method, respectively. In the conventional method, a relatively large amount of traffic was generated in the initial stage of accessing a web page. Compared with the conventional method, the proposed method was distributed and generated less initial traffic. In the proposed method, additional network traffic was generated by prefetching additional content during service use. The probability of content prefetching increased because of the increase in the number of requests. Therefore, additional traffic showed that the bytes per second decreased gradually with an increase in the request order.  
Table 4 shows Mean and standard deviation of the AR content loading of the proposed method. The proposed method showed superior results in terms of initial latency and total latency compared with the conventional method. False prefetching indicates that the corresponding content is not prefetched when a content request occurs. High latency occurs because the content is provided after fetching. True prefetching indicates that the corresponding content is prefetched when the content is requested. The prefetched content is provided immediately.