esa-sceva/satcom-chunk-collection huggingface dataset:
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
## 3 Theoretical Background  
We divided our experiment into several stages, which are outlined as follows: (1) data acquisition from a 5G system; (2) data preprocessing; (3) feature extraction; (4) the grouping of similar features in throughput traces into clusters; (5) training a separate LSTM model for every clustered trace; and (6) the prediction of the next throughput value, including data postprocessing to ascertain the final forecasts. Figure 1 illustrates the workflow of our data processing and forecasting. In the following sections, we discuss the individual stages in more detail.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Prediction Criterion** & **Options** \\ \hline Methods & Statistical vs. machine learning \\ Traffic type & Aggregated traffic vs. single flow \\ Traffic aggregation & Low vs. high \\ Time steps & Single vs. multiple steps ahead \\ Dataset & Spatio-temporal vs. temporal \\ Measurements & Multivariate vs. univariate \\ Traffic capture point & Base station vs. UE \\ \hline \hline \end{tabular}
\end{table}
Table 1: Criteria of our predictive model (underlined) compared to other research.  
Figure 1: The experiment workflow. We preprocess the captured network traffic and extract its features. To generate the final prediction, we cluster the features and apply LSTM predictive models to each cluster separately.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Carbohydrate Polymers
#### 4.1.8 Pectin  
Pectin is a heteropolysaccharide that can be extracted from various kinds of fruits but commonly from the peels of citrus fruits. Pectin has been extensively used as a substitute for expensive biomaterials in the encapsulation of probiotics owing to its abundance, affordable price, anionic and non-toxic character, biocompatibility, biodegradability, bioadhesives, antimicrobial, and antiviral properties. Pectin possesses excellent gelling, thickening, and water-binding properties. In addition, it can form emulsions at low concentrations, making it suitable to be incorporated into spray-drying techniques. Pectin has been used with maltodext as probiotics (_L. casei_ [PERSON], _L. casei_ Immunitas, and _L. acidophilus_ Johnsonii) carrier in the production of probiotic enriched orange powder [24]. The combination of pectin and maltodext effectively enhanced the stability of probiotics during the spray-drying process. Pectin is resistant to gastric and intestinal enzymes but can be rapidly fermented by gut microbiota, thus facilitating the controlled release of probiotics in the gut. It is also an effective prebiotic that can enhance growth, increase acid tolerance, and improve the survival of encapsulated probiotics [75,80,81]. However, due to the high solubility of pectin in the aqueous medium, the bead produced by pectin shows limitations in its rate of diffusion and release of probiotics. Pectin beads have high porosity, low thermal stability, and mechanical strength. During the gelation of pectin, sucrose content was observed to increase. Therefore, pectin-based beads were not recommended for patients with diabetes [81].
# Enhancing Weather Forecasting Integrating LSTM and GA
## 1 Introduction
Since, weather data involve complex, non-linear interactions between various variables, such as, wind speed or direction, LSTMs are capable of modeling these non-linear relationships more effectively than traditional linear models, but also can effectively capture long-term dependencies in sequential data, like the temperature (2018; 2018). The accuracy of LSTM forecasts can be influenced by various factors. [PERSON] et al. (2018) highlight the impact of classical time series pre-processing methods, with seasonal-component removal being particularly effective, the importance of data quality, model architecture, and hyperparameter tuning. The use of genetic algorithms (GA) to optimize the parameters of LSTM forecasting methods has been shown to significantly improve their accuracy. The authors in (2018; 2019) both demonstrate the effectiveness of this approach in short-term load forecasting, with the first achieving a small mean absolute percentage error and the second improving prediction accuracy by 63%. [PERSON] et al. (2019) extend this to medium- and long-term forecasting, highlighting the ability of the LSTM model, optimized with GAs, to capture the characteristics of complex time series and reduce forecasting errors. These studies collectively underscore the importance of using GAs to enhance the accuracy of LSTM forecasting methods. With this in mind, the main objective of this work was to analyze short- to medium-term weather forecasting. To this end, combined methods of regression and LSTM techniques were proposed and later evaluated to assess their accuracy and efficiency. Therefore, the main contributions of this work are (1) estimation of historical data considering regression techniques; (2) short- and medium-term weather prediction using LSTM networks; (3) evaluation of the model's forecasting task considering specific metrics. The subsequent parts of the paper are organized as follows: Section 2 presents some work already developed regarding the proposed theme; Section 3 describes the sites under study, the methodology, presenting the methods considered, their mathematical formulation, the evaluation metrics used, and the case studies considered; Section 4 presents the main results obtained, discussing the relevant points according to the results of accuracy and efficiency of the evaluation metrics; finally, Section 5 presents the main conclusions of the paper and future work.
# Exploring Graph and Digraph Persistence
## 2 State of the Art
### Persistent Homology for (Di)graphs  
Data frequently come in the form of graphs or digraphs, and researchers have often been attracted by the possibility of applying persistence to the analysis of networks. The classical strategy consists in building a filtered simplicial complex from a weighted graph and then computing a persistent homology. This can be achieved in several ways. Of course, the most direct way is to consider the graph itself as a one-dimensional simplicial complex (or CW complex if loops and multiple edges are allowed). This was carried out, e.g., in [23], where a connection between Laplacian, persistence, and network circuit graphs was drawn. Another paper [24] addressed persistence, using the notion of discrete curvature for the evaluation of graph generative models. The clique complex is also a very natural way of transforming a graph into a possibly multidimensional complex. This latter construction was leveraged in [25] for the study of deep neural networks, in [26] for the analysis of the dynamics of scientific knowledge, in [27] for understanding the breakdown and survival of democracies, and in [28; 29] (among many other works) for the representation and study of the human connectome. The persistent homology of clique-community complexes was applied to the study of general types of complex networks in [30; 31]. Other constructions of simplicial complexes--e.g., independent sets, neighborhoods, and dominating sets--have also been discussed and utilized in applications, though, to the best of our knowledge, not in connection with persistence.  
The construction of simplicial complexes from digraphs poses similar but not identical problems to their undirected counterparts. In [32], directed cliques were used to investigate the structural and functional topology of the brain. A differently flavored homology was built in [33; 34], where the role of simplices was played by directed paths. Directed paths were also at the basis of an application of Hochschild homology [35] (Ch.1)--through the use of \"connectivity digraphs\" and within the framework of [6]--for a new persistence homology for digraphs in [36].
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 6 Bromelain Studies
### Blood Coagulation and Cardiovascular Disease  
Bromelain is effective, as an adjuvant, in the treatment of cardiovascular diseases, especially as it is known to possess antithrombotic and anticoagulant activities [67], although clinical trials are, in part, conflicting and require further confirmation. Bromelain has been demonstrated to reduce platelet aggregation, as induced by adenosine phosphate, blood viscosity and the risk of thrombus formation, while also improving the phosphorylation of Akt, and thus inhibiting cell apoptosis [119]. In vivo studies have shown that bromelain decreases the risk of angina pectoris and transient ischemic attack (TIA), although long-term RCTs are required to confirm this preliminary evidence [2]. Furthermore, bromelain possesses antihypertensive properties [7]. It has been reported that the oral administration of bromelain significantly reduces the risk of acute thromboplebitis [2; 120]. On the other hand, bromelain supplementation did not significantly affect blood clotting in another clinical trial that included healthy volunteers [121]. Similar conclusions have been obtained in another trial in which supplementation with 160 mg/day of bromelain (for one week in patients with oedema and inflammation) did not affect bleeding, coagulation and prothrombin [122]. These contradictory results might, in part, be explained by both the limited sample size and dosage of supplemented bromelain. In fact, [PERSON] and colleagues have highlighted the dual action of bromelain on blood coagulation; at low dosage, this supplement seems to have a pro-coagulant effect, while having an anticoagulant effect at a high dosage [123].
# Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments
## 2 System Architecture and Design  
An essential step of the designing process of the sensors, that preceded the development was the assessment of the system requirements. For that purpose, a close interaction with the end-users and the partners was pursued via multiple bi-weekly and bilateral user and technical teleconferences. These teleconferences served as meeting points for the exchange of information regarding not only the nature and the form of the measurements needed but also the infrastructures that each pilot site provides. As a result, the design and development of the smart tags was proceeded according to the needs of each Tier 1 building. The lack of infrastructure, such as power and/or connectivity, has been taken into consideration in order to provide a reliable, power-efficient and weather-resilient solution. In detail, the identified and concluded system requirements are listed below:  
1. Temperature measurements;
2. Relative humidity measurements;
3. Dew point temperature calculation;
4. Assessment of Dry/Wet state on the building's walls;
5. Network autonomy--multiple network capabilities;
6. Power autonomy--multiple power sources, solar energy;
7. Long life operation--low power consumption, recharge capability;
8. Over the Air programming--firmware update;
9. Weather resistant--outdoor and indoor use;
10. Easy deployment;
11. Not intrusive installation;
12. Data Visualization platform.;
13. Scalability--custom-made hardware board and casing.  
After analysing all the system requirements, the system architecture of the smart tag device was structured. The architecture is presented in Figure 1 and is composed of the following components:  
* Main Processing Unit-ESP32: A low-power micro-controller that is the main processing unit of the device and supports multiple communication interfaces with the peripherals;
* Environmental sensors: (i) Sensirion SHTC3 sensor measuring temperature and relative humidity and communicates with the main processing unit via I2C interface, (ii) Samyoung SY-DS-1L that is used for the definition of dry/wet state and communicates with the main processing unit via ADC bus;
* Power management unit: Composed of a rechargeable battery, a solar panel, a charging module and a voltage regulator that supply the main processing unit with stable 3.3 V for its operation;
* Network module: Simcom SIM7020 module offering NB-IoT connectivity over a 4G network and using the UART interface to communicate with the main processing unit;
* Middle-ware: Gateway gathering the data transmitted from the SIM7030 module via the NB-IoT network and MQTT protocol. It is also hosting the visualization platform.  
More details about the components and the hardware being used on the architecture are presented in Section 2.2.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory  
### Empirical-Model-Based Predictive Modeling  
#### 2.1.1 Log-Distance Path Loss Model  
Path loss is defined as signal attenuation in dB, representing the difference between the transmitted power and the received power [42]. The log-distance path loss model commonly used in V2V communication states that the signal strength is based on the exponential change in the distance between the transmitter (Tx) and the receiver (Rx). The formula for this model is given by  
\[\overline{PL}(d)=PL(d_{0})+10n\log\biggl{(}\frac{d}{d_{0}}\biggr{)},\quad d>d_ {0} \tag{1}\]where \(\overline{PL}(d)\) represents the path loss at distance \(d\) between the Tx and Rx (dB), \(d_{0}\) shows the reference distance (m), \(PL(d_{0})\) is the path loss at the reference distance (dB), and \(n\) refers to the path loss exponent, indicating the rate at which the received signal strength decreases with distance.
# Implementing YOLO Convolutional Neural Network for Seed Size Detection
## 2 State of the Art
In the study [16], research on seed quality classification in mixed cropping using the You Only Look Once v5 (YOLO) algorithm was presented. The system was trained on a dataset containing labeled images of healthy and diseased seeds of pearl millet and maize. The system achieved the highest precision and recall at 99%. The methods presented do not encompass all solutions for image segmentation and classification. Recently, numerous new proposals for architectures and training methods have emerged. Techniques related to deep residual learning [17], densely connected convolutional networks [18], and gated convolutional networks with hybrid connectivity for image classification [19] deserve attention. Image recognition and segmentation techniques have substantial practical applications and contribute to the creation of many training datasets, libraries, and frameworks that can be utilized in practice [20]. It can be observed that the use of deep learning and convolutional neural networks yields excellent results in seed classification and segmentation. However, it is important to note that numerous publications address seed classification and segregation. Factors such as seed size, seed type, the number of recognized seed classes, and the number of seeds in an image can influence the results obtained.
# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 2 Background
### Scream Detection Features  
Research work in [20] presents an analysis of discriminant features of a scream from neutral speech. They found that fundamental frequency and energy distribution are two primary features of scream, stating that scream has almost double the fundamental frequency and higher energy distribution at frame level than neutral speech. However, these prosodic features are susceptible to noise and unsuitable for scream detection under high-noise conditions [27].  
MFCCs are widely used in the field of scream detection [23; 25; 26; 29; 30; 31; 32; 34; 35; 40]. Dimension of MFCCs varies with the application. For example, 60 MFCC coefficients have been used as features in [33] for deep neural network architecture, whereas only the first 12 coefficients have been used in [21] for improved one-class SVM classifier. Research work in [39] reports that low-frequency components of MFCC play significant roles in the classification of non-speech human sounds. The first and second derivatives of MFCCs known as delta and delta-delta have been utilized in the research work in [30; 40]. In [33], a robust scream detection method has been proposed using concatenated MFCCs and gammatone frequency cepstral coefficients (GFCCs) as features.  
Spectral features such as spectral entropy [35], spectral centroid [22; 32; 35], and spectral roll-off [22; 29; 32] have been found to be effective for scream detection. Research work in [27] describes a low-cost noise-robust scream detection system by using band-limitedspectral entropy as a feature. They presented a comparative analysis of a conventional scream detection technique with MFCCs and their proposed technique and reported that their proposed method outperforms the conventional technique in detecting screams with equal error rates of 0.3% and 0.8% under noisy conditions at 0 and \(-\)5 dB, respectively.  
The temporal feature zero crossing rate (ZCR) is also widely used in scream detection for discriminant performance [21; 22; 29; 32]. In [30], the authors used the continuity of log-energy combined with MFCCs to detect screams from non-scream audio. Short-term energy has been used in [22; 34]. The papers in [21; 34] used LPCC as features along with MFCCs and other features.  
Research work in [41] investigated a spectro-temporal feature representing roughness as another discriminant feature of a scream. Roughness refers to fluctuations in amplitude modulation. The authors presented experimental results on a spectro-temporal region covering 30-150 Hz, which indicates roughness for screams. They showed that this range is irrelevant for human speech.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 6 Conclusions
### Limitations  
Our focus was studying and evaluating the routing protocols under different traffic scenarios. We considered 500 vehicles as a low traffic density and 5000 vehicles as a high traffic density for our study. It might not be the case in a real-world scenario for a highly congested area. Furthermore, we did not assess dynamic vehicle velocity changes based on conditions in our simulation. Although we considered a reasonable size of map area for the city of Madinah, we might need to take into account a larger area to study the overall performance. Other environmental factors that might affect the performance are out of scope for this particular study.
## 4 LSTM Neural Network Model  
### Principle of LSTM Model  
A typical LSTM cell is shown in Figure 6, where \(\otimes\) and \(\oplus\) denote multiplication and addition, respectively, by bits, \(x_{\rm t}\) is the input parameter at moment t, \(c_{\rm t}\) is the output parameter from the memory cell at moment t, \(h_{\rm t}\) is the output parameter of the control cell at moment t, \(c_{\rm t-1}\) is the memorized information of the previous moment, \(f_{\rm t}\) is the probability that \(c_{\rm t-}\) is forgotten, \(i_{\rm t}\) is the probability that the candidate valuegenerated from the activation function tanh xenters the memory cell, \(\hat{c}_{t}\) is the memory candidate value after the current data were transformed, and \(o_{\rm t}\) is the probability that the current data become the output data. The output parameter \(h_{\rm t}\) is not only related to the input \(x_{\rm t}\) but also to the previously remembered information \(c_{\rm t-1}\), and the new input \(x_{\rm t}\) will also prompt the memory cells to generate updates that are carried forward [20]. The output of the implicit layer is mapped using the function of the output layer to obtain the predicted value of the temperature.  
Figure 6: Schematic diagram of LSTM.  
The calculation formulas for the LSTM model are shown as follows:  
\[\left\{\begin{array}{l}f_{t}=\sigma\Big{[}W_{f}\cdot(h_{t-1},x_{t})+b_{f}\Big{]} \\ i_{t}=\sigma[W_{i}\cdot(h_{t-1},x_{t})+b_{i}]\\ o_{t}=\sigma[W_{o}\cdot(h_{t-1},x_{t})+b_{o}]\\ h_{t}=o_{t}\cdot\tanh(c_{t})\\ c_{t}=F_{t}\cdot c_{t-1}+i_{t}\cdot c_{t}^{\prime}\\ c_{t}^{\prime}=\tanh[W_{c}\cdot(h_{t-1},x_{t})+b_{c}]\end{array}\right. \tag{15}\]  
where \(W_{f}\), \(W_{i}\), \(W_{o}\), and \(W_{c}\) are the parameter matrices to be trained and \(b_{t}\), \(b_{i}\), \(b_{o}\) and \(b_{c}\) are the bias terms to be trained.
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 4 Experimental Results
### Experimental Metrics  
In the experiment, the two basic parameters used to measure target tracking accuracy were the precision and success rate, which were calculated from the center location error (CLE) and overlap score. The CLE is the average Euclidean distance between the center locations of the tracking bounding box and the ground truth. The unit of CLE is pixels, and 20 pixels is usually used in experiments as a threshold. The overlap rate is defined as the ratio of the intersection to the union between the estimated bounding box and given ground truth box. The threshold for the overlap rate varies between 0 and 1; 0.5 is often used for performance evaluation. For the OTB2015 dataset, we used one-pass evaluation (OPE) to test the precision and success rate of all trackers.
## 4 Simulation
### Battery Specifications and Loading Conditions  
The battery dataset for the analysis was downloaded from the NASA Ames Prognostics Data Repository. The dataset consists of 34 commercially available second-generation 18650-size lithium ion rechargeable cells of 2 Ahr nominal capacity. These batteries were cycled using different environmental conditions and loading profiles. The voltage, current, and temperature measurements were recorded during charging and discharging of the batteries. An environmental chamber was used to expose the batteries to different temperatures while cycling (4, 24 and 44 \({}^{\circ}\)C). Fixed and variable loading profiles of 1, 2 and 4 A were used and several End of Discharge (EOD) voltages were used (2, 2.2, 2.5 and 2.7 V) in order to get sufficient data on battery behavior under different operating conditions. One important factor to note is that the batteries were mostly tested in sets of four and not all of them were cycled until the capacity has faded 20% of its nominal value. Therefore, certain datasets were unsuitable for the EOL prediction model. The charging protocol used for the cells was Constant Current of 1.5 A until the cell voltage reached 4.2 V and then Constant Voltage until the current dropped under 20 Ma. The recorded data were stored in.MAT files consisting of all cycling measurements for a given cell, which are stored in an array of 'Cycle' structures. This data structure is shown in Figure 7. Each cycle structure consists of measurements from one of the three types: charge, discharge, and impedance. The Time array consists of the sampling times, while the other field contains current and voltage  
Figure 6: Diagnostic and prognostic framework.  
measurements (Figure 8). The Capacity field was evaluated by integrating the current over time until the battery voltage reaches 2.7 V.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### Image Data Collection
#### 5.1.2 Multispectral Sensor  
The use of the multispectral sensors has become a trend nowadays because it has more than three (RGB) bands installed. Compared to RGB sensors, several vegetation indices that can be investigated are significantly expended. Nevertheless, to obtain accurate indices, radiometric and atmospheric calibration are compulsory. Moreover, unlike RGB sensors, the multispectral sensor is unable to deliver a high-quality spectral resolution image. This drawback can be overcome by using a lower flying height and acceptable percentage of horizontal and vertical image overlap [48].  
In general, the typical steps involved in preparing multispectral images captured by UAV remote sensing are: (1) radiometric and atmospheric calibration, (2) locating and avoiding input and output (I/O) errors, missing data, and mission failure, and (3) image rectification, georeferencing, and stacking [41]. In addition, these sensors are increasingly being employed in machine learning algorithms for site-specific weed management (SSWM) [49; 50; 40].