esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the IMBPC HVAC Solution Hardware and Software  
### Hardware  
IMBPC requires information about the external weather and the inside climate of the rooms under control, as well as their forecast over a Prediction Horizon (_PH_). As such, it needs devices for acquiring the variables needed to characterize the weather and the climate. While the latter will be described in the following section, we shall concentrate here on the measurement and forecast of global solar radiation (_SR_), atmospheric air temperature (_TA_) and relative humidity (_RH_).  
For this purpose, the authors designed and tested an intelligent weather station [10], which, besides being energy-autonomous, offering wireless communications based on the IEEE 802.15.4 standard and measuring the three variables mentioned earlier, provides the forecasts of these variables over a user-specified _PH_. The sampling time, as well as the prediction interval, are user-defined, with the latter being a multiple of the former.  
Two forecasting methods are available in the intelligent weather station: a nearest neighbour (_NN_) algorithm and an artificial neural network (ANN)-based approach. The former is essentially a pattern matching algorithm, which searches, among \(d\) full days, for the closest (in the Euclidean sense) \(n\) neighbours, which will be averaged to compute the predictions [11]. The execution of the NEN is completely local to the device. The ANN-based approach uses Radial Basis Function Neural Networks (RBFNNs), whose design is done externally by a Multi-Objective Genetic Algorithm (MOGA) [12].  
For more information on the design, operation and results of the intelligent weather station, the reader is invited to consult [10].
# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
## 3 Materials and Methods
### Classification
#### 3.2.4 LSTM  
LSTMs are a type of Recurrent Neural Network (RNN), as they also have chained recurrent modules. However, the different LSTM cells are more complex than those in standard RNNs. The specific structure is represented in Figure 4, where every rectangle represents a fully connected layer with their correspondent activations sigma (\(\sigma\)) and \(tanh\). Input data in the timestep \(t\) is represented by \(x_{t}\). Similarly, the current cell state and outputs are represented by \(C_{t}\) and \(h_{t}\).  
The current cell state, \(C_{t}\), depends on minor linear interactions related to the previous cell state, \(C_{t-1}\). The LSTM gates are formed by a sigmoid (\(\sigma\)) layer and a pointwise multiplication so the outputs are kept between 0 (discarded information) and 1 (valid information). The forget gate, \(f_{t}\), operation is in Equation (2), where \(W_{f}\) represents the correspondent weight matrix for that gate and stays unaltered through time. This calculation determines which information is required to be kept based on the previous output and the current information, plus \(b_{f}\) which represents a bias. Similarly, \(i_{t}\) is obtained with the same process and different weights and bias, \(W_{i}\) and \(b_{i}\), as seen in Equation (3). This gate is known as the input gate; it selects which values get updated. The output \(i_{t}\) gets combined with a vector of candidate values, \(\tilde{C}_{t}\), obtained with a \(tanh\) layer, weights \(W_{C}\) and bias \(b_{C}\), as observed in Equation (4).  
Figure 3: Scheme for CNN layer.  
The previous cell state \(C_{t-1}\) updates resulting in Equation (5). \(C_{t}\) then a _tanh_ pushes it to values between \(-1\) and \(1\) before getting multiplied by the output of another sigmoid gate. This part leads to the final output as formulated in Equations (6) and (7), using weights \(W_{o}\) and bias \(b_{o}\). This process is done recurrently as many times as timesteps there are.  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{2}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{3}\]  
\[\tilde{C}_{t}=tanh(W_{\text{C}}\cdot[h_{t-1},x_{t}]+b_{\text{C}}) \tag{4}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\tilde{C}_{t} \tag{5}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{6}\]  
\[h_{t}=o_{t}\times tanh(C_{t}) \tag{7}\]  
To implement a multilayered LSTM, the output sequence of the LSTM cell in a given timestep, \(h_{t}\), is returned and fed into the next layer. Figure 5 is an unrolled two-layered LSTM, where \(T\) represents the maximum number of timesteps. The last layer does not require retrieving all the hidden cell outputs but only the output in the last timestep. In the case of Figure 5, the final output corresponds to \(h_{T}^{\prime}\).  
As a result, there are two hyperparameters to set:  
* Hidden neurons (\(n\)): number of hidden neurons in the LSTM cell gates.
* Hidden layers (\(L\)): number of LSTM layers to connect.  
Figure 4: LSTM cell composition.
## 7 Conclusions  
In this paper, we analyze the performance of a mixed PLC-FSO communication system operating over a turbulence channel in the presence of pointing errors at the FSO link and impulsive-background noise at the PLC link. Under different system parameters, the results demonstrate that the outage probability and BER performance improve as the effects of pointing error, turbulence, or impulsive-background noise decrease. More specifically, results on various system parameters show that severe pointing error and turbulence condition on the FSO link can lead to degradation of the outage probability and average BER performance. Similarly, high values of impulse noise probability and noise power in PLC link also leads to poor performance. Also, the diversity order of the proposed system only depends on the values of \(\alpha,\beta,\zeta,r,\phi\), and \(\phi^{\prime}\). Our study also shows that the simulation results coincide with the analytical curves at various SNR values.  
From the derived analytical results, the performance of proposed system can be thoroughly investigated to show the impact of various critical system parameters. Consequently, from the trends of the simulation and analytical results, the PLC transmission link is shown to be a promising backhaul solution for the FSO link and integration of both PLC and FSO links can provide an efficient indoor to outdoor transmission technique.  
All authors discussed the contents of the manuscript and contributed to its presentation. [PERSON] designed and implemented the proposed scheme, analyzed the simulation results and wrote the paper under the supervision of [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Research Program through the National Research Foundation of Korea (NRF-2019R1A2C1005920).  
The study did not involve humans or animal studies.  
The study did not report any data.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AWGN Additive white Gaussian noise  
FSO Free-space optical  
PLC Power line communication  
Figure 6: Outage probability versus \(\tilde{\gamma}_{\text{FSO}}\) with various pointing error conditions and detection modes.
###### Abstract  
The current success rate of CubeSat missions, particularly for first-time developers, may discourage non-profit organizations to start new projects. CubeSat development teams may not be able to dedicate the resources that are necessary to maintain Quality Assurance as it is performed for the reliable conventional satellite projects. This paper discusses the structured life-cycle of a CubeSat project, using as a reference the authors' recent experience of developing and operating a 2U CubeSat, called qbee50-LTU-OC, as part of the QB50 mission. This paper also provides a critique of some of the current poor practices and methodologies while carrying out CubeSat projects.  
CubeSat; miniaturized satellite; nanosatellite; small satellite development
# Automatic Short Text Summarization Techniques in Social Media Platforms
## 5 Approaches to Summarization
### Extractive Summarization  
Extractive summarization involves the selection and extraction of important sentences or phrases directly from the original short text to create the summary. In this approach, the chosen sentences are taken as they appear in the original text, without any modifications. The selection process relies on ranking the sentences based on their importance and relevance to the main content of the text. One of the advantages of extractive summarization is that it preserves the exact wording of the sentences from the original text, ensuring grammatical correctness and maintaining context accuracy. Additionally, it avoids potential issues that may arise from generating new sentences that might not be contextually appropriate or could introduce factual inaccuracies. However, extractive summarization has some limitations. It may result in disjointed or redundant summaries, as the selected sentences might not flow together coherently. Moreover, it can struggle with handling overlapping information in the text, leading to certain critical content being repeated in the summary. These challenges highlight the need for careful sentence selection to create concise and coherent summaries that effectively represent the main content of the original text. Extractive summarization is widely used in various NLP applications, including automatic short text summarization (ASTS). Here are some common extractive approaches in short text summarization:  
* Frequency-based methods: Frequency-based methods assign importance scores to sentences based on the frequency of important words or phrases in the text. Sentences with a higher frequency of essential terms are considered more important and are selected for the summary.
* TF-IDF (Term Frequency-Inverse Document Frequency): TF-IDF is a widely used technique that evaluates the importance of a word in a document relative to a corpus of documents. Sentences containing important terms with high TF-IDF scores are selected for the summary.
* TextRank algorithm: TextRank is a graph-based ranking algorithm inspired by PageRank. It treats sentences as nodes in a graph and computes the importance of each sentence based on the similarity and co-occurrence of sentences. High-scoring sentences are chosen for the summary [25].
* Sentence embeddings: Sentence embeddings represent sentences as dense vectors in a high-dimensional space, capturing semantic information. Similarity measures between sentences are used to rank sentences, and the most similar ones are included in the summary.
* Supervised machine learning: In supervised approaches, models are trained on annotated data with sentence-level labels indicating whether a sentence should be included in the summary. The model then predicts the importance of sentences in new short texts and selects the most important ones for the summary [26].
# Estimation of Tibiofemoral and Patellofemoral Joint Forces during Squatting and Kneeling
## 2 Materials and Methods
### Experimental Set-Up  
A complex arrangement of measurement equipment was used to obtain all measurement parameters synchronously. An eight-camera Vicon system (Oxford Metrics Group, Oxford, UK) sampled the three-dimensional positions of the relevant anatomical landmarks at a frequency of 100 Hz. A standard lower-limb marker set as proposed from _Plug-in Gait Module_ (version 1.7, Oxford Metrics Group, Oxford, UK) was applied to obtain the three-dimensional position and orientation of the pelvis, thigh, shank, and foot. The markers were placed on Velcro padded straps, which were tightly fastened at the subject's leg  
Figure 1: External forces to be considered in the analysis of (**a**) deep squatting, (**b**) upright kneeling, and (**c**) deep kneeling.  
and pelvis region. The markers on the shoes were directly attached using double-sided adhesive tape.  
The ground contact forces at the foot and knee of the right leg were measured by two force plates (type: 9286A, Kistler Instruments, Winterthur, Switzerland) flush-mounted in the floor. For kneeling, the measurement area of the relevant force plate was covered with a standard kneepad cushioning material to protect the knee from the hard surface.  
The thigh-calf contact force was detected by a pressure-sensitive pad (dimensions: 250 mm \(\times\) 120 mm, Paromed, Neubeuern, Germany) containing 24 piezo-resistive sensors (each with a respective area of 5 cm\({}^{2}\)) and fixed with elastic straps to the dorsal surface of the thigh. The resultant contact force was assumed to act normal to the bisector of the tibial and femoral longitudinal axes at the center of pressure with respect to the knee joint center and was updated at intervals of 20 ms. A threshold limit of 40 N was applied to the resultant pad force as an indication of thigh-calf contact.  
In parallel, the surface electromyography (EMG) of the quadriceps muscle was recorded at 1000 Hz using two channels-rectus femoris and vastus medialis. Following band-pass filtering (\(f_{\rm c}\) = 20/500 Hz), the raw signals were RMS-filtered (\(\Delta t\) = 0.2 s) and normalized with respect to a maximum voluntary contraction (MVC) (EMG-DSP V1.0, Biomed, Jena, Germany). The channel indicating the higher %MVC values was selected to represent the activity of the quadriceps muscle.
# Aromatic Potential of Diverse Non-Conventional Yeast Species for Winemaking and Brewing
## 3 Results and Discussion
### Microbeer
#### 3.2.1 Fermentation Performance  
Multifactorial ANOVA shows that the quantity of residual sugars is affected by the adjunct strains, but the levels of ethanol were similar in all fermentations (Table 5). In more detail, most of the yeast combinations showed good fermentation performance, consuming practically all the sugars present in word (Table 5). However, there were some exceptions to this pattern: small sugar amounts (5-8 g/L) were left by non-conventional yeasts _H. guilliernondii_ CBS 2567, _Z. kombuchaensis_ CBS 8849 and _Z. rouxii_ CBS 9716 when used in combination to WS34/70 in proportion 5:1 and _T. microellipsoides_ CBS 6641 in proportion 20:1 (Table 5). Different sugar consumption efficiency was already seen in previous works by these strains [11; 21], especially by _Z. rouxii_, which is not able to properly ferment maltose [7]. Surprisingly, _T. microellipsoides_ CBS 6641 left more residual sugars when used in higher proportion, indicating an interaction between yeasts regarding their fermentation abilities already seen in mixed fermentations by other authors [17]. These differences in sugar consumption were not translated into significant differences in ethanol yield, which was around 6% in all the fermentations (Table 5). This fact could be explained by the bifurcation of metabolic flux to glycerol production by certain strains which would use part of the sugars to this end, thus reducing the expected ethanol yield and as a strategy to cope with hyperosmotic stressful conditions, which has been extensively studied in _Saccharomyces_ species [25; 26].
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
## 2 Materials and Methods
### Data Analysis  
The collected data were analysed using SPSS (SPSS Statistics Premium 29, IBM, New York, NY, USA) and Minitab v19 (Minitab LLC, State College, PA, USA) for assessment of the accuracy and precision. Consistency with a normal distribution was assessed using the Ryan-Joiner test in Minitab to address if data should be analysed with parametric or non-parametric methods. To assess the accuracy, the mean values for each dataset were compared against the corresponding reference value obtained digitally from the master STL by performing a one-sample \(t\)-test. In addition, the magnitude of error for each dataset was also investigated using the absolute value of the difference between the reference value and each measurement. To assess the precision, the coefficient of variance (CoV) was calculated using the formula below.  
\[CoV=\frac{\sigma}{\mu}\]  
(\(\sigma=\) standard deviation, \(\mu=\) mean).  
The overall study design is summarised in Figure 3.  
Figure 2: Assessment of (**a**) intercanine width (ICW), (**b**) intercolor width (IMW), and (**c**) arch length (AL) of the upper arch using the master STL file of 3D virtual orthognathic surgical splint for reference values.
## 2 Sensor Configuration
The thickness tolerance is \(\pm 2\) \(\upmu\)m, while the surface requirements are given by, e.g., flatness \(\leq\lambda/10\), parallelism \(<30\)\" and surface quality specified by a scratch and dig of 20-10. According to the MO specifications for the cover-glass, the thickness of both, the beam splitter plate and the compensation plate is 1.75 mm, respectively. To avoid an additional mount with inaccuracy due to manufacturing  
Figure 2: Photograph of the custom-built MA with demounted adjustment head for better view of the used piezo actuator. tolerances, these two elements are merged while the beam splitter coating is between the elements. Respecting the mounting position of the reference mirror within the optical path, small dimensions of the mirror are needed to avoid obscurations. In addition, a mounting position as close as possible to the MO is chosen for the reference mirror to place it next to the pupil plane. As a result the shading is reduced as much as possible. The reference mirror is sinusoidally deflected by a miniature multilayer piezo actuator (PI PICMA PL0 xx) [27]. With compact dimensions of \(2\,\mathrm{mm}\times 2\,\mathrm{mm}\times 2\,\mathrm{mm}\) this component yields a peak-to-peak amplitude of approximately \(2.2\,\mathrm{\SIUnitSymbolMicro m}\). A small piece of coated silicon wafer acting as flat reference mirror is placed on the piezo actuator (see Figure 2). The piezo actor deflects the reference mirror in axial direction with an oscillation frequency of \(1\,\mathrm{kHz}\), due to the need of a sufficient elongation for an error-free signal evaluation. Note that the oscillation frequency is limited by the used piezo controller (Thorlabs MDT694B). The electrical connection wires of the piezo actuator are led out of the housing. Therefore, a bracket for the piezo is designed with two bars of the same width as the diameter of the wires to avoid additional obscuration. However, a high bending strength is necessary to suppress additional vibrations. The bracket is made of a glass bead reinforced polyamide (PA12-GB) and is manufactured by a selective laser sintering (SLS) process. This manufacturing method provides high accuracy in combination with low cost. The piezo generates a high waste heat during operation, so that these bars must be able to dissipate a high amount of heat. The operation at an excitation frequency of \(1\,\mathrm{kHz}\) is possible without any impact on the performance of the piezo actuator. However, a mount made of copper might be necessary for use with much higher excitation frequency in order to achieve a better heat management.
## 3 Results and Discussion
### Optimization of the Extraction Process Parameters
It is encouraging to compare our browning index results, which reached an absorbance of approximately 0.2, with the results reported by [PERSON] et al. [52], who investigated the influence of the roasting degree on the antioxidant activity and genoprotective effect of instant coffee samples. Furthermore, our browning index values are consistent with those of [PERSON] et al. [34] who evaluated the quality of spent coffee grounds obtained from the most commonly used coffee beverage preparation methods. It can, therefore, be assumed that the analyzed spent coffee grounds are an abundant plant source of melanoidins. The optimal conditions for the extraction yield of antioxidant bioactive compounds were determined based on the combination of predictor values that jointly optimized the fitted response. The tested SCG extracts showed the best results in terms of tested analytical responses with an organic solvent concentration of 64.9%, and an SMR of 50.6 mL/g SCG d.m. In order to check the predictability and adequacy of the developed models, validation tests were performed. SCG extracts were newly prepared under the aforementioned optimal conditions. The results predicted by the models and the actual values of the validation tests are shown in Table 7. It was observed that the deviations between the actual and approximated values were within \(\pm\)5%. The low level of deviations proved the mathematical models' viability in the examined range.
# A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing
## 4 Radar Coordination  
The orderly collaboration between radars depends on reasonable time-slice management and workflow management; in this work, these were designed in accordance with the radar parameters in Table 1.  
We configured the equivalent-time sampling rate and the real-time sampling rate of the receiver to be 16 GSPS and 125 MSPS, respectively. Accordingly, the receiver was able to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Proposed Radar** \\ \hline Netted radar system scan rate & 32 fps \\ Center frequency of radar system & 1 GHz \\ Equivalent sampling frequency (\(F_{S}\)) & 16 GSPS \\ Real-time sampling frequency & 125 MSPS \\ Sampling points (\(N\)) & 16,384 \\ Average times (\(N_{A}\)) & 32 \\ ADC Resolution & 16 bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of the proposed radar system. ADC: analog to digital converter.  
Figure 4: The antennas.  
obtain \(N\) = 16,384 sampling points for one UWB A-scan in 512 us, equaling 128 pulse repeat periods. To improve the echo's SNR, \(N_{A}\) = 32 UWB A-scans were required for human respiration detection, and the corresponding time consumption was about 17 ms. In addition, the wireless data transmission for transmitting the radar echoes and the reference signal for pulse compression with a network speed of 100 Mbps required about 6 ms. Thus, the required minimum time slice was about 23 ms. In practical terms, to retain the time margin for wireless transmission and the network clock module, each time slice was set as 32 ms.  
To understand the orderly collaboration between radars, the workflow timing is presented in Figure 5.  
1. _Start Up and Netted:_ As the initialization stage, the host establishes the WIFI network and permits radars access. Radar IDs are distributed by the host for data acquisition, clock synchronization and self-positioning.
2. _Idle:_ All components turn to the standby mode, waiting for the \"start command\".
3. _Network Clock Synchronization:_ The network clock synchronization is executed firstly after receiving the \"start command\".
4. _Self-positioning:_ The relative positions between radars are measured by their self-positioning modules once.
5. _Time Slice Allocation:_ The host verifies the validity of the radars' topology and allocates time slices to radars.
6. _Electromagnetic Detection:_ According to the network clock synchronization and the time slice allocation, each radar illuminates the target area and receives the echoes within their time slices in turn.
7. _Data Transmission:_ The echoes are uploaded to the host.
8. _Result Output:_ After 16 s of sampling echoes, the host calculates the target's position and displays the results on the human-machine interface.