esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing the Cost of Implementing Filters in LoRa Devices
## 3 Simulation Results  
The performance of different LoRa communication systems with additive white Gaussian noise (AWGN) channels was evaluated in Matlab. The pulse shaping and matched SRRC filters both have an upsampling/downsampling factor of \(L=4\), roll-off factor of \(\beta=0.10\), and 81 coefficients. In terms of quantization, fractional bit-precisions of 2, 4, 6, 8, and 10 were studied to quantize both the basic chirp samples generated at the transmitter and the conjugate basic chirp samples used for dechirping in the receiver. Additionally, a system was also tested without quantization to act as a reference.
## 4 Strategy Description
### Popularity Estimation  
From the analysis in Section 3, caching nodes can gain a higher cache hit ratio when caching contents with a higher inflow request rate. We consider that the content popularity varies from caching nodes because the network position and the user distribution may change the inflow request rate of contents. There has been some research focused on popularity estimation [22; 23]. However, the process of popularity estimation should be lightweight and simple due to the requirement of line-speed forwarding in ICN.  
In caching node \(i\), we record the count of served requests of content \(k\) as \(r_{i,k}\) and the count of served requests as \(r_{i}\). Based on these, we can simply estimate \(p_{i,k}\) as \(r_{i,k}/r_{i}\). Furthermore, each node is responsible for recording the minimum popularity and corresponding content within the cache. The process of popularity estimation is triggered by the request packet and is depicted in Algorithm 1. If a caching node has free cache space, its minimum popularity is set as 0.  
```
1 caching node \(i\) receives a request for content \(k\);
2 ifthe destination NA of the request is node \(i\)then
3\(r_{i,k}=r_{i,k}+1,r_{i}=r_{i}+1\);
4 ifcontent \(k\) is the content with minimum popularitythen
5 find the new content \(k^{\prime}\) with minimum popularity;
6 record content \(k^{\prime}\) and the minimum popularity;
7
8 end if
9
10 end for
```  
**Algorithm 1**Popularity Estimation
# Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory
## 2 Literature Review  
One of the first studies to attempt to predict cryptocurrency using non-linear neural networks--conducted by [PERSON], [PERSON], and [PERSON] and published in 2020--found that the non-linear models were preferable to linear models as the former's error was lower than the latter, giving more accurate predictions. This study also found that some elements of the cryptocurrency's historical data were not helpful in prediction, such as the volume [9].  
Another study released in 2022 by [PERSON], [PERSON], and [PERSON] sought to expand the prediction beyond merely yielding a direction; instead of reporting whether the price would go up or down, the model would report \"the magnitude of the price change\" [10]. The study leveraged not only historical price data for Bitcoin, but also Twitter posts to glean public sentiment about the currency. An important distinction of this study is that it used a bidirectional LSTM, which is a model composed of two LSTMs: one for historical price data and one for Twitter posts. Its accuracy of 64.18% was achieved by using 450 days of historical data [10].  
Furthermore, a 2022 study by [PERSON], [PERSON], and [PERSON] used COVID-19 data, such as the number of cases and deaths, to determine whether cryptocurrencies such as Bitcoin would rise or fall in price. The study made use of the Romano-Wolf algorithm to test this correlation [11]. In particular, it deemed Bitcoin to be very highly correlated with deaths due to COVID-19, correspondingly fluctuating 90.50% of the time [11].  
Lastly, a 2019 study by Azari utilized an autoregressive integrated moving average (ARIMA) approach to predict the price of Bitcoin [12]. In this study, the model's effectiveness was evaluated by examining the mean-squared error; the findings reported a very low level of prediction error [12].  
Scientific papers such as these have proven that using artificial intelligence and neural networks yields accurate results in predicting cryptocurrency. However, as many parameters can be tweaked in this prediction goal (such as using volume data of the currencies,using extraneous data such as COVID-19 statistics, or utilizing different forms of neural networks), we aim to determine whether it is possible to gain accurate predictions using only close price data and an LSTM. We also explore whether the LSTM is more accurate than the ARIMA model. Our research hypothesis is that the LSTM will provide a more accurate model, in that its predictions will produce a smaller root-mean-squared error (RMSE).
# GIS-Based Optimal Route Selection of Submarine Cables Considering Potential Seismic Fault Zones
## 4 Discussion
However, a non-linear increase can be easily seen for greater displacements. The linear behavior is attributed to the elastic material properties that have been utilized for the numerical analyses. Conversely, the non-linear relationship of the cable deformations for large net fault offsets could be explained considering the non-linearities of the PSI FE. The resulting strains have been compared to an indicative strain limit threshold equal to 0.1%, which has been derived from the studies of [PERSON] et al. [74] and [PERSON] et al. [32]. It can be observed that strains greater than 0.1% have been developed for a bedrock offset of D \(\geq\) 16 cm, regardless of the type of the cable. The proposed numerical model can be successfully utilized to investigate the structural response of a submarine cable subjected to the geohazard of fault rupture. In particular, it can be easily observed from Figure 4a,b that the numerical FE model developed from the authors presents a very satisfactory agreement with the numerical models and the full-scale experiment of [PERSON] et al. [32]. Although small discrepancies are observed for relatively low lateral soil displacements, the proposed numerical model tends to be slightly closer to the experimental results, compared to the numerical model that was developed by [PERSON] et al. [32]. Finally, it is important to mention that the multidisciplinary topic of the present study is characterized by several uncertainties that are difficult to be accurately quantified. Undoubtedly, a significant volume of geodata is required for a reliable and accurate representation of the routing analysis criteria. However, the accurate mapping of off shore earthquake-triggered geohazards constitutes a demanding geological procedure. Earthquake-triggered geohazards cannot be precisely evaluated, since data related to the topography, geology and geomorphology of the seabed are limited. Such geodata are usually derived from time-consuming geophysical surveys and potentially-inaccurate digitized geological maps of the seabed. Moreover, the quantitative assessment of offshore earthquake-triggered geohazards and the consequent distress of crossing CLs is characterized by various epistemic and aleatory uncertainties. For instance, seismic fault zones are unrealistically represented within the GIS environment as linear entities, with starting and ending points and specific dimensions without an accurate mapping of critical factors, such as slip potential, path propagation, secondary faults, etc.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 2 System Model
### Channel Model  
Letting \(\mathbf{x}=[x_{1},x_{2},\cdots,x_{N_{t}}]^{T}\) be the transmitted signal vector, \(\mathbf{H}\) represent the \(N_{r}\times N_{t}\) MIMO channel matrix and \(\mathbf{n}=[n_{1},n_{2},\cdots,n_{N_{t}}]^{T}\) denote the additive noise vector, the received signal vector \(\mathbf{y}=[y_{1},y_{2},\cdots,y_{N_{t}}]^{T}\) is obtained by  
\[\mathbf{y}=\mathbf{H}\mathbf{x}+\mathbf{n}, \tag{1}\]  
and the corresponding channel matrix \(\mathbf{H}\) can be expressed by  
\[\mathbf{H}=\left[\begin{array}{ccc}h_{11}&\cdots&h_{1N_{t}}\\ \vdots&\ddots&\vdots\\ h_{N_{t}1}&\cdots&h_{N_{t}N_{t}}\end{array}\right], \tag{2}\]  
where \(h_{rt}\) (\(r=1,2,\cdots,N_{t};t=1,2,\cdots,N_{t}\)) denotes the direct current (DC) channel gain between the \(r\)-th PD and the \(t\)-th LED. Assuming that each LED follows the general Lambertian radiation pattern and only the line-of-sight (LOS) transmission is considered, \(h_{rt}\) is calculated by [29]  
\[h_{rt}=\frac{(l+1)\rho A}{2\pi d_{rt}^{2}}\text{cos}^{m}(\varphi_{rt})T_{s}( \theta_{rt})g(\theta_{rt})\text{cos}(\theta_{rt}). \tag{3}\]  
In Equation (3), \(l=-\text{ln}2/\text{ln}(\text{cos}(\Psi))\) denotes the Lambertian emission order, with \(\Psi\) being the semiangle at half power of the LED; \(\rho\) and \(A\) represent the responsivity and the physical area of the PD, respectively; \(d_{rt}\) is the distance between the \(r\)-th PD and the \(t\)-th LED; \(\varphi_{rt}\) and \(\theta_{rt}\) are the emission angle and the incident angle, respectively. \(T_{s}(\theta_{rt})\) is the gain of optical filter, and \(g(\theta_{rt})=\frac{n^{2}}{\text{sin}\Phi}\) is the gain of optical lens, where \(n\) and \(\Phi\) are the refractive index and the half-angle field-of-view (FOV) of the optical lens, respectively.  
Moreover, the additive noise in typical OWC systems consists of both shot and thermal noises, and it is reasonable to model the additive noise as a real-valued zero-mean additive white Gaussian noise (AWGN) [8]. Letting \(N_{0}\) denote the noise power spectral density (PSD) and \(B\) be the signal bandwidth, the power of the additive noise is given by \(P_{n}=N_{0}B\).
## 3 Results and Discussion
### Characteristics of the Amine Immobilized GMA-g-NWF and IRP  
#### 3.2.1 Surface Morphological Analysis  
Imaging onto the amine immobilized GMA-g-NWF and IRP samples that functionalized with the amines were carried out in order to observe the changes in terms of morphology before and after crosslinking process, respectively. The images obtained shown in Figure 2. The average diameter of the pristine sample was around 12.42 \(\upmu\)m. The GMA-g-NWF sample obviously increases almost up to 13.44 \(\upmu\)m. The increase in the diameter of the pristine sample after the grafting was due to the coating of the poly-GMA thin layer [27]. Moreover, the average diameter of GMA-g-NWF samples increases further after functionalization with a different aliphatic amine. The average diameter obtained for EDA-GMA-g-NWF, DETA-GMA-g-NWF, TETA-GMA-g-NWF, and TETA-GMA-g-NWF was around 14.07, 18.91, 13.10 and 13.83 \(\upmu\)m, respectively. This indicates the incorporation of the amine group on the epoxy group of GMA via the functionalization process [25]. Among four types of an aliphatic amine, DETA had shown the highest gain in term average diameter. On the other hand, after the crosslinking process, all the IRP samples a slight reduction in average diameter in comparison to amine immobilized GMA-g-NWF. The average diameter obtained for EDA-IRP, DETA-IRP, TETA-IRP, and TETA-IRP was around 12.45, 12.48, 13.06 and 13.71 \(\upmu\)m, respectively. Typically, when the polymer is subjected to radiation, two types of reaction, namely crosslinking and degradation, will occur [28]. The polymers mutually shrunk due to bonding of the polymer chains in the initial stage of radiation crosslinking, resulting from decreasing in average diameter after the crosslinking process. The differences in average diameter EDA-IRP, DETA-IRP, TETA-IRP, and TETA-IRP were insignificant. Moreover, enormous weight loss also was not observed after radiation treatment which indicates that no occurrence of chain scission during the process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time (min)**} & **EDA** & **DETA** & **TETA** & **TETA** \\ \cline{2-5}  & **AD** & **AD** & **AD** & **AD** \\  & **(mmol/g\({}^{-\text{ad}}\))** & **(mmol/g\({}^{-\text{ad}}\))** & **(mmol/g\({}^{-\text{ad}}\))** & **(mmol/g\({}^{-\text{ad}}\))** \\ \hline
15 & 2.89 & 2.41 & 1.61 & 1.21 \\ \hline
30 & 3.80 & 3.16 & 2.64 & 2.15 \\ \hline
45 & 3.82 & 2.97 & 2.52 & 2.36 \\ \hline
60 & 3.86 & 3.11 & 2.56 & 2.36 \\ \hline
90 & 3.87 & 3.20 & 2.59 & 2.41 \\ \hline
120 & 3.91 & 3.16 & 2.61 & 2.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of reaction times on the amine density at for 70% amine: 30% isopropanol, 60 \({}^{\circ}\)C \(D_{\text{g}}=104.86\%\).  
Figure 2: _Cont._
# A Procedure to Prepare Sand-Clay Mixture Samples for Soil-Structure Interface Direct Shear Tests
###### Abstract
A high diversity of sample preparation methods is available in the literature [45; 46; 51; 52; 53; 54; 55], such as dry pluviation [49; 55; 56], dry tamping [7; 8; 9; 57; 58], wet tamping [55; 59; 60], and slurry deposition [45; 46; 47; 48; 51; 52; 53]. The slurry deposition method can overcome the inherent problems of the other sample preparation methods, it can create homogeneous saturated sand or sand-silt/clay samples with respect to void ratio and particle size distribution [52]. In addition, soil sample is easy to be replicated by the slurry deposition technique, possessing similar fabric to natural soils at the same time. To our knowledge, so far in geotechnical studies, there is a clear lack of detailed procedures for the sample preparation of sand-clay mixtures, especially for interface direct shear tests. This paper presents a detailed procedure on how to prepare sand-clay samples for interface direct shear tests. This sample preparation procedure can be used to reconstitute clay samples for interface as well. It can provide samples with good homogeneity and shape, and ensure repeatable test results.
# A Neural Topic Modeling Study Integrating SBERT and Data Augmentation
## 1 Introduction
The data augmentation technique [20] uses simple random replacements, insertions, deletions, and other operations to enhance the robustness of text data. The keyword information is obtained through the TextRank algorithm [21], which efficiently and quickly extracts important words from a large amount of text or other materials. Therefore, our method combining EDA with keyword data augmentation is beneficial for overcoming the sparsity problem of text features. Next, we vectorize the augmented text data and input them into a BiLSTM-Att module to obtain the long-distance dependency information and overcome the influence of noisy words. Then, although the emergence of BERT has propelled the development of various NLP tasks, they still struggle to produce semantically meaningful embeddings for shorter language units such as sentences and phrases. The most common method for BERT sentence embedding is to take the average of the BERT output layer (referred to as BERT embedding) or use the output of the first token ([CLS] token). This common practice results in relatively poor sentence embeddings that are unsuitable for unsupervised clustering and other tasks [18]. Therefore, we use SBERT instead of BERT, which takes the entire sentence as the processing unit and uses Siamese networks and triplet networks to update weights, resulting in semantically meaningful sentence embeddings that are more suitable for tasks such as semantic similarity search and clustering. Furthermore, the SBERT model in this paper is trained on a large and diverse dataset and designed as a general-purpose model, achieving superior results compared to the sentence encoder in the baseline model ZeroShotTM across various tasks. Moreover, our SBERT is capable of generating high-quality semantic embeddings for short texts. Finally, we merge the information that has been enhanced through data augmentation and processed through the attention mechanism with the high-quality semantic feature information. Then, we feed the resulting feature information into a neural topic model to learn high-quality topics. We use the ProdLDA model [22] as our neural topic model, which is a topic model based on automatic-encoding variational inference. It optimizes the structure of the topic model to be more suitable for tasks such as topic mining. Experimental results show that our model produces coherent and high-quality topics on the English public dataset; specifically, our contributions are as follows:* We use an improved neural topic model combining SBERT and VAE for topic discovery tasks. * We propose a new data augmentation method of fused keywords to overcome the sparsity problem of text and thus learn more accurate topics. * We use the BiLSTM-Att mechanism to obtain context dependencies as well as to reduce the influence of noisy words (words irrelevant to the topic) on the model. * Experimental results show that our approach leads to improved topic coherence and obtains diverse and interpretable topics.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 5 Experiment Description
### Data Flow Generation
#### 5.2.2 DTNbox Approach  
As the DTNbox approach is particularly suited to bulk data, it is applied only to the Science data flow, from Lander to PDGS. Aiming at maximum communality, Science data is generated by DTNperf as in the direct approach, except that this time the destination is not the DTNperf server on the PDGS node, but an instance of the ION \"ppreceive\" application [11], running on the synchronized directory (DTNbox/filesToSynch/143/Science). Bpreceive saves the payload of received bundle in a new file (testfile1, testfile2, etc.). Then, at regular intervals (in our tests 5 s), updates containing new files are sent by DTNbox to the synchronized directory on PDGS. Here new files are moved to another directory by a script running in the background, which emulates the receiving application. At the end of experiment, a record of file creation times is extracted from this directory, to derive the file delivery times to compare with those of the direct approach.
# Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance
## 1 Introduction
### Reproducibility  
The challenges of reproducibility in machine learning, particularly in the context of high-dimensional data analysis, have been extensively examined. [PERSON], [PERSON], and [PERSON] [15] categorize reproducibility into three essential types: methods reproducibility, which ensures that technical procedures can be precisely replicated; results reproducibility, which guarantees that reimplementations of a method produce statistically consistent outcomes; and inferential reproducibility, the most crucial of the three, which ensures that conclusions drawn from data are robust and generalizable across different experimental conditions. [PERSON] et al. [16] built upon this framework, emphasizing its importance in the context of machine learning, where these forms of reproducibility are essential for validating research findings and ensuring that they are not mere artifacts of specific experimental designs.  
Moreover, factors such as data sampling, model initialization, and hyperparameter optimization introduce variability that can obscure true model performance. Addressing and modeling these sources of variability is essential for enhancing the reliability of machine learning benchmarks [17]. These principles are particularly related in the context of feature selection, where rigorous and reproducible methods are vital to ensure that the identified features genuinely contribute to the model's predictive power. Collectively, these approaches highlight the necessity of careful experimental design and robust evaluation criteria, which are indispensable for accurately determining the relevance of features in high-dimensional datasets.  
A/B testing, a widely used technique in experimental design and digital marketing, shares several key principles with reproducibility-focused methodologies. A/B testing involves comparing two versions of a product or feature (commonly referred to as A and B versions) to determine which performs better according to a predefined metric [18]. This process relies on statistical hypothesis testing to assess whether observed differences in performance are statistically significant, thereby informing decisions about which version should be adopted [19].
## 2 Materials and Methods
### Dataset  
The research was approved by the Ethics Committee of the University Hospital Kralovske Vinohrady Prague (EK-VP/4310120), where measurements were made. Each patient signed their informed consent to the research conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Grade** & **Characteristic** \\ \hline I & normal facial function in all areas \\ \hline II & slight weakness on close inspection \\ \hline III & obvious but not disfiguring \\ \hline IV & obvious weakness and disfiguring asymmetry \\ \hline V & only barely perceptible motion \\ \hline VI & no movement \\ \hline \hline \end{tabular}
\end{table}
Table 2: House-Brackmann (HB) scale for facial movements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(p\) & Kinect & Position & \(p\) & Kinect & Position \\ \hline
0 & 1104 & left eye, bottom & 11 & 849 & left eyebrow, centre \\
1 & 241 & left eye, top & 12 & 18 & nose tip \\
2 & 210 & left eye, inner corner & 13 & 8 & mouth lower lip, centre-bottom \\
3 & 469 & left eye, outer corner & 14 & 91 & mouth, left corner \\
4 & 346 & left eyebrow, inner & 15 & 687 & mouth, right corner \\
5 & 222 & left eyebrow, centre & 16 & 19 & mouth upper lip, centre-top \\
6 & 1090 & right eye, bottom & 17 & 4 & chin, centre \\
7 & 731 & right eye, top & 18 & 28 & forehead, centre \\
8 & 843 & right eye, top & 19 & 412 & left cheek, centre \\
9 & 1117 & right eye, outer & 20 & 933 & right cheek, centre \\
10 & 803 & right eyebrow, inner & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Indices of points of interest (facemarks), \(p\) is an internal index number.  
Figure 2: Points of interest illustration.  
Up to February 2022, the dataset contained 236 successful rehabilitations of 127 patients. More detailed information on the study cohort can be found in Table 4.  
The patients had been performing nine exercises using the mimetic muscles during the examination. The samples in Figure 3 are the amount of data that totalled around 2214 samples (236 measurements where each measurement included nine exercises). Time steps are expressed as the number of frames (sequences) recorded by the camera. Facial points are the number of features in every frame (time step) recorded from measurements that are the input of our network instead of images.  
Figure 3 shows both the grade score distribution (classes) and the time steps distribution of our dataset. Data size is a very important factor in training deep neural networks. Larger datasets can help deep networks learn the model parameters better. A deep neural network trained with small datasets generally exhibits a poorer performance than that of conventional machine learning methods [18].
# Crowdsourcing Framework for QoE-Aware SD-WAN +
## Network Model and Assumptions
### QoE Model  
The proposed framework uses a subjective crowdsourcing approach for QoE assessment. Crowdsourcing allows subjective measures based on both video-pair comparisons and MOS-based rating comparisons, with the flexibility to choose participants' demographics if certain demographics are required for specific results.  
Despite their scalability, crowdsourcing experiments lack supervision, which makes some results not fully trustable. Researchers should be able to identify trusted and untrusted participants. This can be achieved by designing the crowdsourcing campaigns on the basis of certain best practices. These best practices are majorly concerned with technical implementation aspects of the experiment, campaign, and test design, as well as a thorough statistical analysis of results [26]. Campaigns should be simple enough for participants to understand how the experiment is designed and what is required to complete it.