esa-sceva/satcom-chunk-collection huggingface dataset:
# Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees
## 2 Materials and Methods
### Windowing and Look-Back Filtering
#### 2.3.1 Classifiers  
Different machine learning classification algorithms were used in this systematic comparison. They can be classified in 3 big groups:  
* Linear Models (4): Logistic Regression (LR), Linear Discriminant Analysis (LDA), Stochastic Gradient Descent(SGD) classifier and Passive Aggressive (PA) classifier.  
Nonlinear Algorithms (5): Decision Trees (DT), Support Vector Machine(with linear kernel) (SVMLinear), Support Vector Machine(with polynomial kernel)(SVMPoly), Support Vector Machine(with rbf kernel)(SVMRbf), Naive Bayes (NB) classifier. Ensemble Algorithms (4): Ada Boost Classifier (AB), Bagged Decision Trees (BDT), Random Forest (RF) and Extra Trees (ET) classifier.  
All theses algorithms were used with their default hyper-parameter values, except the Ensemble Algorithms for which we set the number of estimators to 100. In this state of the research no hyper-parameter tuning was performed.  
In addition to the filtering of the feature vectors as described above, we also implemented a _majority voting_ stabilizer that takes the last \(N=20\) outputs of the classifier in the queue and provides the most repeated class as the output.  
As stated earlier, each of the six gestures (shown in Figure 1) was performed at three different force levels by the users: low, medium and high. The original authors' main objective was to improve performance of gesture recognition despite force/amplitude variations EMG signals recorded from amputees. Our objective, however, is to allow the user to voluntary modulate gripping forces applied by the fingers of a prosthetic device. We therefore treated the six gestures \(\times\) three force levels as if they were independent gestures, i.e., as 18 classes. To ease comparison, we mimicked the train/test split used in [10], where 3 trials are used for training each gesture+force state, and the rest are used for testing. As five to eight trials were recorded for each gesture+force condition, this means 54 trials were used for training and from 42 to 99 trials were used for testing, depending on the user. The database contains 1027 total trials (486 for training 541 for testing) and the minimum number of windows a trial contains is 80.
## 3 Acoustic Individual Identification in Birds  
A vocalization database of a few individuals is necessary to evaluate the proposed methodology. For this purpose, several audio files were collected from two free online databases:  
* The bird sound library of Mexico (from INECOL) [28]: A small database used for ornithology research and the diffusion of audio records of bird species in Mexico.
* Xeno-canto [29]: A citizen-science database with about 500k audio records of vocalization of bird species from around the world.  
Although databases have different audio records, some of them do not offer specific features (e.g., the type of vocalization or information about if the records are from more than one individual). The acoustic information shown in Table 1 was obtained from the databases for different species in order to use it in the proposed algorithm (all birds in the database are Passeriformes). There is not a special consideration about the selection of bird species. Given that there is a wide variety of songs available in databases, a quick search was made (audios that were easy to identify acoustically) to test the system.  
Table 1 gives information about the number of vocalizations extracted from original audio files. The variability of vocalizations correspond to the variability of audio files available from a specific individual and the number of specific vocalizations in each audio file.  
The proposed method uses a pre-stored audio record of a bird vocalizations (call or song) to identify whether an input audio record belongs to the same individual (genuine matching) or not  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Family** & **Species** & **Number of** & **Number of** **Vocalizations** \\  & & **Individuals** & **from Each Individual** \\ \hline Furnariidae & **(a)**_Clihamornis rubiginos_ (P.L.Sclater, 1857) & 4 & 4,4,27,6 \\  & **(b)**_Spinalaxis erythothorax_ (P.L.Sclater, 1855) & 6 & 7,15,7,5,27,9 \\ Cardinalidae & **(c)**_Cardiuals cardinalis_ (Linnaeus, 1758) & 3 & 17,2,22 \\ Thamophilidae & **(d)**_Cercomacra tyranmina_ (P.L.Sclater, 1855) & 4 & 7,4,2,3 \\ Troglodytidae & **(e)**_Hylorchilus sumichrasti_ (Lawrence, 1871) & 5 & 4,6,4,4,2 \\ Tyranidae & **(f)**_Myiozetetes similis_ (Spix, 1825) & 6 & 10,28,8,13,7,8 \\  & **(g)**_Pitangus sulphurntatus_ (Linnaeus, 1766) & 3 & 5,5,6 \\ Icteridae & **(h)**_Psarocolius montezuma_ (Lesson, 1830) & 5 & 5,3,2,2,2 \\ Pstitacidae & **(i)**_Anazona viridigenalis_ (Cassin, 1853) & 2 & 6,3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of bird species and records in the database.  
(impostor matching). As mentioned in the Introduction, the proposed technique has two variants (depending on the method used to analyze and extract the bird vocalization from records): automatic individual verification algorithm and semi-automatic individual verification algorithm. The process is depicted in Figure 1.
# Analysis and Application of Lining Resistance to Water Pressure in Tunnel through Karst Cave
## 2 Failure Mode of Grouting in Tunnels Crossing High-Pressure Karst Caves
### Failure Characteristics of Grouting in Tunnel Crossing the High-Pressure Karst Cave  
#### 2.1.1 Establishment of the Numerical Calculation Model  
Statistics on karst tunneling hazards show that most of the water-bearing cavities encountered in karst tunneling are in the near-horizontal direction, with only a few cavities in the near-vertical direction [20]. The near-vertical cavities are governed by the flow characteristics of dissolved water. The cavities are generally larger in size, and most of them have signs of surface exposure; thus, such geological structures are often avoided in tunnel surveys, and tunneling disasters caused by vertical cavities are not common. The RFPA software simplified the karst into a nearly horizontal water-bearing cavity to study the damage characteristics of the tunnel grouting circle under a high-pressure cavity. Taking a large lateral cavity as an example, it was assumed that the tunnel parallel heading expansion profile crossed the cavity, and the cavity fill needed to be reinforced by curtain grouting before expansion. The inner diameter of the tunnel was 10 m, the inner diameter of the parallel heading was 5 m, and the grouting reinforcement circle range was 5 m outside the tunnel expansion contour. Taking into account the influence of the parallel heading expansion range, the calculation model size had to be greater than three times the diameter of the hole, which was taken as 100 m \(\times\) 100 m. The tunnel burial depth was 500 m, the average self-weight of the surrounding rock was 20 kN/m\({}^{3}\), the applied vertical mean load was about 10 MPa, and the lateral pressure coefficient of rock was taken as 1.5. The calculation model is shown in Figure 1. The model was considered by the plane strain problem, and the modified Coulomb criterion including the tensile cutoff was used as the strength criterion for unit damage. The structural parameters of the surrounding rock level are shown in Table 1.  
The calculation steps of all models in this paper were the same. The first step was a steady pressure after grouting for the existing parallel heading; the water pressure load was applied to the inner wall of the water-bearing cavity to simulate the water pressure effect in the cavity, and the initial water pressure in the water-bearing cavity was 0.1 MPa, with a single-step increment of 0.01 MPa. The second step was the construction of the existing parallel heading expansion, and the water pressure in the cavity was calculated to increase until the rupture of the grouted reinforcement circle bursts water.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 4 Experiments and Results
### Data Sets and Evaluation Metrics  
In this paper, we collected remote sensing images containing ships in the open source dataset to construct the dataset. We screened specific types of training samples from the HRSC2016 (High-Resolution Ship Collection 2016) dataset [26] and preferred images containing small targets in different complex backgrounds to evaluate the optimization degree of the improved network. The original dataset did not annotate the small ship targets in the images; we re-annotated them and annotated the ship targets of all scales in the images, which expanded the number of small ship samples and helped to improve their detection effects. We labeled the ships into four categories, such as merchant ship, aircraft carrier, warship and submarine, with a total of 1333 labeled images, and we divided them into three sets: training, validation and test, in a ratio of 8:1:1. The constructed dataset can provide training data for training the convolutional neural network for coarse recognition of ship targets. Detailed information regarding the dataset can be found in Table 1.  
The common evaluation indexes of ship detection are: Precision P (Precision), R (Recall), Average Precision (AP) and Mean Average Precision (mAP). P indicates the ratio of the number of correctly identified positive samples to the total number of predicted positive samples; R refers to the ratio of the number of correctly identified positive samples to the number of all positive samples. As shown in Figure 7, in the PR (precision-recall) curve, the vertical axis is precision, and the horizontal axis is recall. The higher the precision and recall of the model indicate its better performance and, correspondingly, the larger the area under the PR curve. The mAP is a composite measure of the average accuracy of the detected objects. When detecting multiple classes of objects, the mAP is obtained by averaging the AP for each class.  
Figure 6: Our improved model structure diagram.  
\[\mathrm{P}= \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}^{\prime}} \tag{12}\] \[\mathrm{R}= \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}^{\prime}},\] (13) \[\mathrm{AP}= \int_{0}^{1}\mathrm{P(R)}d\mathrm{R},\] (14) \[\mathrm{mAP}=\frac{\sum\mathrm{AP}}{\mathrm{m}}, \tag{15}\]  
where TP means a real positive sample, TN means a real negative sample, FP is a false positive sample and FN is a false negative sample. m represents the number of categories of objects in the dataset.
## 2 Materials and Methods
### Determination of Tested Elements by Flame Atomic Absorption Spectroscopy (F-AAS)  
The samples underwent mineralization before F-AAS analysis. For food supplements P-1 and P-2, the powdered extract was poured from the capsules, weighed, and homogenized. In other cases, after homogenization, the desired portions of the product were weighed. Three approximately 0.4 g samples of each product were weighed with 0.1 mg accuracy. The samples were then transferred to Teflon vessels, and 1 mL of 30% H\({}_{2}\)O\({}_{2}\) solution and 5 mL of 65% HNO\({}_{3}\) were added. The wet mineralization process was carried out in a Magnum II microwave apparatus (ERTEC, Wroclaw, Poland) in two stages of 10 and 20 min, at a power of 80% and 100%, with a pressure range of 42-45 Ba, maintaining the temperature at 295-300 \({}^{\circ}\)C. After mineralization, the solutions were transferred to quartz evaporators and heated for approximately 60 min at 140 \({}^{\circ}\)C on a hot plate to remove excess reagents to the so-called \"almost dry\" stage. The residues after evaporation were quantitatively transferred to 25-mL Erlenmeyer flasks and diluted up to the mark with fourfold distilled water. Samples were refrigerated until F-AAS analysis using an iCE 3000 Series Atomic Absorption Spectrometer (Cambridge, UK) equipped with a flame atomizer and SOLAAR Software, Version 2.01 software (Thermo Fisher Scientific, Waltham, MA,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Form** & **Extract/Dosage** &
\begin{tabular}{c} **Country of** \\ **Origin/Production** \\ \end{tabular} & **Cultivation** & **Organs of Plant** \\ \hline P-5 & & & & \\ Yerba mate & Instant & - & tablespoon & Brazil & Conventional & - \\ soluble & & & & & \\ \hline P-6 & & & & & \\ Yerba mate & Powder & - & 1 g & Brazil/Poland & Conventional & - \\ powder & & & & & \\ \hline P-7 & & & & & & 95\% large \\ Yerba mate & Tea bag & - & & & & leaves and max \\  & & & & & 5\% small sticks \\ \hline P-8 & & & & & & \\ Yerba mate & Dry herbs & - & (10–15 g for & Brazil/Poland & Conventional & 95\% leaves and \\ despalada & & & & beginner) & & \\ \hline P-9 & & & & & \\ Yerba mate & Dry herbs & - & (10–15 g for & Brazil/Poland & Conventional & Leaves \\ Food Supplement & & & & beginner) & & \\ \hline P-10 & & & & & \\ Traditional & Dry herbs & - & (10–15 g for & Argentina & Conventional & Leaves and \\ yerba mate & & & beginner) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
USA). Calibration curves were determined for each analyzed element, and then its content in the analyzed solutions was determined. The parameters of the applied method were previously described using the same methodology and apparatus [41; 42]. The obtained concentrations were calculated based on the average weight of the samples, taking into account mean masses for capsules. The results are expressed in mg/kg.  
Determination of Organic Compounds in Yerba Mate Products by High-Performance Liquid Chromatography (HPLC)
## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Fuzzy-Based Methods  
Some of the earliest works in expert systems for ECG interpretation employed fuzzy logic or fuzzy inference systems. [PERSON], [PERSON] [37] designed a fuzzy expert system to detect different types of arrhythmias from ECG signals. Wavelet signal analysis method using Daubechies wavelet family is used for signal analysis and ECG characteristic features extraction including P, Q, R, S, T waves and the zero level of signal. Fuzzy rule-based classifier was used for 14 known arrhythmia type detections. Experimentation on MIT-BIH arrhythmia dataset gave good results. However, the performance was low in some types of arrhythmias such as PVC and PAC. [PERSON], [PERSON] [40] also designed a fuzzy expert system to predict the risk of hypertension in heart disease patients utilizing the fuzzy inference system (FIS) tool. The diagnosis was based on heart rate measured by ECG and other parameters such as blood pressure, BMI and age. The approach performed better than other statistical approaches. In another study, [PERSON] and [PERSON] [61] developed an expert system for diagnosing sleep apnea from patient ECG recordings using fuzzy logic. The approach extracted HRV features from ECG signals which were fed into a fuzzy inference engine based on triangular membership function for diagnosis. The approach proved effective in detecting sleep apnea from patient ECG.  
A recent study conducted by [PERSON] and [PERSON] [82] presented a fuzzy expert system for the diagnosis of coronary artery disease using a rule base and membership functions based on different risk factors such as hypertension smoking, gender, age and family history. The expert system was developed in MATLAB and the proposed framework had 84.7% accuracy in the prediction of heart disease. The same approach was used in [PERSON], [PERSON] [83] to develop an expert system for heart disease prediction using up to 19 risk features along with ECG recording. The system was able to predict low, middle and high risk of heart disease in patients.  
**Findings:** Fuzzy-based methods were found to be mostly applied in ECG interpretation for multiple heart disease detection. This is due to its effectiveness in multi-class classification. Fuzzy inference systems were used to detect different types of arrhythmia and different risk levels of heart disease in patients. Fuzzy inference systems were also found very useful in developing expert system applications.
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
## 3 Markov Process of Staged Deployment  
### Definitions of Staged Strategy State Variables  
Assuming that a mega-constellation consists of \(n_{t}\) identical satellites, \(n_{t}\in\{0,1,\cdots,n_{\max}\}\). By reason of the congestion problem on LEO, collisions would frequently occur during operation stages and the performance or utility would degrade; therefore, additional satellites need to be launched. The optimal deployment strategy is that the decision to launch rational number of satellites assures that the constellations follow normal operation sequentially and that the corresponding cost is minimized when the systems encounter malfunctions.  
The number of on-orbit satellites \(n_{t}\), along with the space collision probability \(p_{t}\), forms the state space. The state variables obey the Markov property and the collision probability has three realization modes: decreased, maintained, and increased. Within time step \(t\), the state variables could be expressed by \(x_{t}=(s_{t},p_{t}),x\in\{0,1,\cdots(n_{\max}+1)\cdot(p_{\max}+1)-1\}\).  
Decision-makers should determine the satellite numbers to be launched during the time steps, assuming that the launch capability limit is \(u_{\max}\). Let \(p_{t}=0\), \(p_{t}=1\), \(p_{t}=2\)  
Figure 5: The influence of debris size for collision probability as years.  
Figure 6: The influence of orbital height for collision probability as years.  
express the modes of decreased, maintained, and increased, respectively. The transition matrix can be seen in Table 2.  
During the various stages of constellations' deployment, satellites being launched successfully is the first consideration. The normal operation of satellites needs to be guaranteed after satellites are inserted into orbit.  
No launch failures and no collisions are important prerequisites for constellation-staged deployments. Let \(e_{vf}\) be the event for launch failure and \(e_{c}\) be the event for collision, then the failure probability of a single satellite can be computed with the following equation:  
\[P\Big{(}e_{sf}\Big{)}=P\Big{(}e_{vf}\Big{)}+P\Big{(}e_{vf}^{-1}\Big{)}\cdot \Big{[}P(e_{c})\cdot P\Big{(}e_{sf}|e_{c}\Big{)}+P\Big{(}e_{c}^{-1}\Big{)} \cdot P\Big{(}e_{cf}\Big{|}e_{c}^{-1}\Big{)}\Big{]} \tag{11}\]  
In this equation, \(e_{*}^{-1}\) is the opposite event of the responding event \(*\). Compared with other events, such as subsystems malfunction and maneuver faults, the failure probability is relative large when collisions occur. Assuming \(\varepsilon\) satellites fail after the first deployment, then the constellation system could not provide service for customers anymore; in other words, when \(k\geq\varepsilon\), the whole system fails, so the failure probability can be calulated by:  
\[P=\sum_{k=\varepsilon}^{m_{\max}}\left\{C_{n_{\max}}^{k}\cdot P\Big{(}e_{sf} \Big{)}^{k}\cdot\Big{[}1-P\Big{(}e_{sf}\Big{)}\Big{]}^{n_{\max}-k}\right\} \tag{12}\]
## 3 Results
### Transaction Structure and Maritime Data Ownership Issues for MASSs
#### 3.3.1 Transaction Subjects  
The transaction of data needed for the operation of autonomous vessels will be completed through the transmission, use, and transfer of proven data online or offline using the \"digital platform for trading maritime data (tentative name)\" [28]. Data--a major source of such transaction markets--might be legally protected under the Personal Information Protection Act and the Copyright Act [53]. These are the presuppositions of the source supplier, buyer, broker, and so on, to obtain consent within a certain scope.  
First, the data providers required for commercial MASS operations are direct producers, with exclusive rights to the underlying production of data as rights holders or people with rights granted by such producers; they provide the data to a \"digital platform for trading maritime data (tentative name)\" [29]. Typical data providers include ship owners operating autonomous vessels. However, they could also be ship managers or agents entrusted with the vessels' operation, who have accepted the vessel owners' rights, responsibilities, and obligations.  
Second, data buyers are people with access to a \"digital platform for offshore data transactions\", who receive services under legally agreed terms and conditions intended for value addition [36]. Examples include marine insurers, shipyards, vessel materials companies, ship owners, governments, research institutes, universities, and shipping companies.  
Third, data brokers support or manage data transactions related to MASS operations using a one-stop authorized brokerage platform; such platforms enable data consumers and suppliers to conduct the entire data transaction process (e.g., data retrieval, contracts, payments, and analyses) with a legal safety net [29]. In addition, data brokers manage the \"digital platform for trading maritime data (tentative name)\", purchasing data from suppliers and selling it to buyers. Data brokers are obliged to manage the platform's system security to prevent data leakage and damage.
## 7 ECC-based Key Agreement Protocol
### Implementation of Key Agreement Protocol  
Our development environment is TinyOS 2 [29] running on the Moteiv Tmote Sky platform, which features a 16-bit, 8 MHz Texas Instruments MSP430 processor with 48 KB of program ROM and 10 KB of RAM. Program code in this environment is written in neSC (networked embedded systems C), a dialect of C designed for use with TinyOS. (As of late 2007, Moteiv has changed its name to Sentilla and has discontinued production and support of its Tmote product line in favor of a new hardware platform designed for Java applications. However, the new platform is backwards-compatible with the Tmote Sky. Furthermore, Crossbow Technology still offers its TelosB mote for sale, which is functionally identical to the Tmote Sky. Both the TelosB and Tmote Sky remain popular in the research community.)  
Our basic ECC operations are based on North Carolina State University's TinyECC 1.0 distribution [16]. We implemented ECIES for secure key agreement, using the elliptic curve secp160r1 defined over a 160-bit prime field as recommended by [30]. We use 160-bit private keys, 320-bit public keys, and a 160-bit random number \(r\) in ECIES. We then implemented our key agreement protocol, using a 32-bit session number (\(SN\)), 48-bit patient fingerprint (\(FP\)), 128-bit master key (\(K_{m}\)), 128-bit transient and session keys (\(K_{s}^{{}^{\prime}}\), \(K_{mac}^{{}^{\prime}}\), \(K_{s}\), \(K_{mac}\)), and 64-bit nonces (\(n_{1}\), \(n_{2}\)). The resulting _KeyGenStart_, _KeyGenAck_, and \(KeyGenVerify\) messages are 56, 52 and 32 bytes long.  
Due to its customizability and ease of implementation, we chose the RC5 block cipher in ciphertext stealing (CTS) mode for the symmetric encryption in ECIES, using the recommended parameters of 64-bit blocks, 128-bit keys, and 12 rounds. For the hash function we chose SHA-1 [31], which allowed us to save code space by using SHA-1 based algorithms for both the message authentication code function (for integrity checking) and key derivation function. We implemented HMAC-SHA-1 [32] and PBKDF1 [33], respectively.
## 2 Methods
### Data Extraction and Analysis  
First, two reviewers (SH-H and NL-V) undertook the reading of all the titles and abstracts, and those that did not refer to the research question were removed. Subsequently, the aforementioned reviewers conducted an independent evaluation, following the inclusion/exclusion criteria, until reaching a consensus on the studies to be included in the study. Finally, a total of 19 studies were included and the full texts of the selected studies were obtained for review. The extraction of data from each study was performed using Excel spreadsheets, completing the following categories: year of publication, sample size, country, percentage of a second canal, Vertucci's configuration, other relevant results, CBCT model, voxel size, FOV, CBCT settings and software visualization.