esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The possibility of metal removal from the real acid mine drainage (AMD) in the area of copper ore mines in the southeast Serbia, was investigated through a combination of neutralization and adsorption methods. This approach of metal removal from AMD includes a two-step neutralization method in the first phase, aiming to separate metals as sludge. The results of laboratory test revealed that more than 99 mass % of Fe is removed up to pH 4 and more than 99 mass % of Cu up to pH 7. Based on the results obtained in laboratory conditions, a test on a semi industrial plant was carried out. The two-step neutralization separately removed Fe and Cu at pH 4 and 7, respectively. Especially, the obtained sludge at pH 7 included 1.24 mass % of Cu, much higher than usual Cu ore. Chitosan was applied for dissolved Mn removal from treated AMD. After 24 h incubation, 70 mass % of Mn is removed from the treated AMD at pH 7.4. Mn concentration was reduced from approx. 35 mg L\({}^{-1}\) to 5 mg L\({}^{-1}\). These results have indicated that a combination of neutralization and adsorption methods could be used effectively for metal removal from real AMD.  
acid mine drainage; metals removal; neutralization; adsorption; chitosan; semi industrial plant
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 5 Real-Time UWB Indoor Positioning Systems (RTIPSs)
### Real-Time Implementation Ranging and Topologies
#### 5.2.3 Phase Difference of Arrival (PDoA)-Based Indoor Positioning Systems  
This method is significant for peer-tracking applications, especially for mobile tracking applications that have to be implemented without relying on a fixed infrastructure. Positioning is determined by calculating the distance and direction of the UWB nodes. Measuring the angle of arrival (AoA) and the time of flight between tags and anchors can be achieved using both angle-of-arrival and two-way ranging algorithms [8]. One or both of the used UWB units should have two antennas to allow for the AoA calculations. Many researchers have used PDoA for IPS implementation. [PERSON] et al. [110; 111] proposed a PDoA-based IPS for tracking the elderly in smart homes. Their work aimed to overcome the NLoS problem in elderly living environments. The coarse location was calculated initially to find the nearest LoS anchors in the NLoS environment, and then the NLoS anchors' measurement was canceled and only the LoS anchors were used for the elderly location tracking. The system hardware used two different units from Qrovo (DecaWave Limited, Dublin, Ireland): a UWB transceiver, and a PDoA chip combined with a host processing board. The tag had a different design, and it was included in a wearable wristwatch with GPS, Zigbee, and some inertial and biosensors. The system was practically tested with nine fixed anchors in different positions, with LoS and NLoS positions for the tag, and the results showed that the system performed the tracking tasks successfully. [PERSON] et al. [112] proposed four different machine learning models for PDoA IPSs. The models were trained with real-time data from commercial UWB units. The system hardware consisted of the DecaWave Beta-PDoA UWB kit, and the practical testing of the developed models showed that the random forest regressor model had the best performance, with a mean parameter of 8\({}^{\circ}\). [PERSON] et al. [113] proposed a single-anchor RTLS using wrapped PDoA based on an antenna array with arbitrary geometry. The object localization was implemented by measuring the ToF and the wrapped phase of the UWB signals. The localization process included obtaining parameters by running a soft positional information algorithm, which was approximated by the Gaussian mixture model (GMM) to accelerate the filtering process. The system was practically implemented in a single-antenna tag and an eight-antenna anchor connected to the DecaWave DW1000 UWB transceiver, which could calculate the time of arrival and the carrier phase--the main parameters required for implementing PDoA. The system testing results revealed that localization accuracy of \(\approx\)8 cm could be achieved. Other PDoA approaches are presented in [114; 115; 116; 117; 118; 119; 120].
###### Abstract  
Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Therefore, more research studies are needed in this domain. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN's protection against LDDoS attacks.  
network security; SDN; OpenFlow; DDoS attacks; low-rate DDoS attacks; machine learning; detection mechanisms +
Footnote †: journal: Computer and Information Science  
0000-0001-9563-3330][PERSON]  
1 Department of Computer and Information Science, Universitat Teknologt Petronas, Seri Iskandar 32610, Malaysia; [EMAIL_ADDRESS] (A.A.A.); [EMAIL_ADDRESS] (M.Y.D.)  
M.Y.D.
## 6 Analysis of Orbit Determination Performance
### Noise-Free Analysis  
First, in order to analyze the noise floor of the OD scheme devised, we estimated the orbital parameters from a set of error-free observations. To this aim, we set \(\varepsilon_{ij}(t)=0\) in (1) when simulating the ISR and GSR observables, and \(\mathbf{Q_{y}}=\mathbf{I}\) in (6).  
Figure 2: Maximum post fit RMSE for the 24 MEO satellites when fitting 15 perturbed Keplerian parameters to dynamic orbits, as function of the fitting interval length. (**a**) Fitting interval: 10 to 120 min. (**b**) Fitting interval: 2 to 6 h.  
Table 2 (a) gathers the average OD RMSE and SiSRE\({}_{\text{orb}}\) of the 24 MEO satellites in the scenario with three European ground stations GS1-GS3. It can be observed that the longer the arcs, the larger the orbit estimation errors. The SiSRE\({}_{\text{orb}}\) measures less than 1.5 cm for a 1 h fit interval and grows to 9.42 cm for a 3 h fit interval.  
The same analysis was performed for a scenario with four ground stations, and the corresponding results are given in Table 2 (b). The addition of a fourth ground station improves the OD accuracy, mainly due to a better observation geometry, and the larger the orbital arc considered, the bigger the improvement, from 9% for 1 h intervals to 25% for 3 h intervals. It should be noted that OD accuracy is significantly lower than the accuracy of the F/NAV fit to the simulated orbits (Section 5.1) due to geometry and a much lower number of observations (ranges only).
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 3 Results
### Reusability of Simulator Components (GQ6)  
Two key challenges to the reduction of the training costs are the reusability of the medical simulator components and the minimization of spare parts cost. With regard to question GQ6 (\"Are the simulator components reusable?\"), in the largest part of the studies analyzed [22; 23; 24; 25; 27; 28; 29; 30; 31; 41; 43] the entire simulator is reusable. This is due to the fact that the authors perform only non-destructive tasks such as palpation, basic laparoscopictasks (i.e., instrument navigation, peg transfer, clipping of virtual objects, etc.), insertion of tools (i.e., wire navigation, intravenous injection, etc.), and surgical planning. In particular, the authors use materials that are extremely durable over time such as silicone rubbers, polyurethane, and plastic materials.  
In studies [26,44], the authors have designed solutions to make all anatomical components reusable such as the liver, gallbladder, pancreas, abdominal aorta, esophagus-stomach-duodenum, arterial tree, and biliary tree, except the connective tissue which must be dissected during each training session. Finally, only in four studies [22,29,36,37] was the entire simulator substituted after each training session.
## 5 Experiment Conclusions
### Metrics
#### 5.2.3 Monotonicity  
To further assess the accuracy of the ranking algorithm, we employ the monotonicity index, denoted as \(M\), to measure the monotonicity of the DNC method and compare it with the monotonicity of the comparative methods [47]. The definition of ranking monotonicity is as follows:  
\[M(X)=[1-\frac{\sum_{r\in V}N_{r}(N_{r}-1)}{N(N-1)}]^{2} \tag{15}\]  
where \(X\) represents the node sequence, \(r\) is the ranking index, and \(N_{r}\) denotes the total count of nodes in the sequence with a rank of \(r\). When \(M(X)\) approaches 1, this indicates a higher degree of monotonicity in the sequence, with all nodes having distinct rank values. When \(M(X)\) approaches 0, the network has only one ranking value, implying that all nodes have the same rank value. Additionally, we employed rank distribution plots to assess DNC and the baseline methods.
# Pharmacokinetics of Marine-Derived Drugs
## 3 Pharmacokinetics Studies in Animals
### Absorption
#### 3.1.5 Sea Urchin-Derived Compounds  
Sea urchins are marine invertebrates belonging to _Echinoidea_. All parts of these organisms have been used in traditional medicine [121] and are a highly valuable source of medicines. The sodium salt of naphthoquinone pigment echinochrome A (**17**) (under the name of histochrome), isolated from the shell of sea urchin _Scaphechinus mirabilis_, is approved in Russia for the treatment of acute myocardial infarction and, in ophthalmology, for dystrophic diseases of the retina and cornea [9; 122; 123]. The absorption of histochrome was studied in rabbits after subconjunctival and parabulbar injection. It was not absorbed into the blood, but rapidly distributed over the tissues of the eye [124].  
A glycopeptide (GPP) from the internal organs of the green sea urchin _Strongylocentrotus droebachiensis_ is effective as an anti-inflammatory agent in the treatment of bronchitis [125]. GPP was administered to rats i/v (100 \(\mu\)g/kg) and intranasally (i/n) at the dose of 50-200 \(\mu\)g/kg. Its plasma concentration-time function was two-exponential after i/v administration with a mean T\({}_{1/2}\) of 0.8 h and an AUC of 8.00 \(\mu\)g-h/mL. After i/n administration, GPP appears in plasma at 15 min and reaches a C\({}_{\text{max}}\) of 2.90-6.22 \(\mu\)g/mL at a T\({}_{\text{max}}\) of 0.67-0.75 h with AUC of 7.14 \(\mu\)g-h/mL. The C\({}_{\text{max}}\) increases with increasing dose; however, it does not change after repeated dose administration. Repeated doses of i/n administration (3 days of 100 \(\mu\)g/mL) led to a significant increase in AUC (up to 23 \(\mu\)g-h/mL. Notably, C\({}_{\text{max}}\) and AUC were equal after both administration routes at identical doses (Table 2). This finding supports the rationality of the i/n administration of GPP [30].  
Lipid extract of the gonads from _S. droebachiensis_ (LES) lowered glucose level and inhibited enzyme dypetydylpeptidease IV (DPPIV) [126]. The inhibition of DPPIV was used as a biomarker for the pharmacokinetics of LES in rabbits after peroral administration (Table 1). The pharmacokinetic was linear at doses of 5-25 mg/kg. The C\({}_{\text{max}}\) of LES in plasma was 37.1-114.7 \(\mu\)g/mL at T\({}_{\text{max}}\) of 3-3.5 h, but the difference in T\({}_{\text{max}}\) with dose increase was not statistically significant. A second increase in LES concentration in plasma was observed at 10 h. The AUC ranged from 192.3 to 594.2 \(\mu\)g-h/mL with increasing dose (Table 2) [29].
## 3 Time-Frequency Coded Cooperative Transmission Based on MC-CDMA and Power Allocation  
Assume that there are \(K\) source sensors which send messages to the sink node simultaneously in the considered WSN shown in Figure 1. The spreading code of source sensor \(S_{k}\) and its cooperative sensor \(P_{k}\) is expressed as \(\textbf{{c}}_{k}=\left[c_{k}(1),\cdots,c_{k}(g),\cdots,c_{k}(G)\right]^{T}\) with spreading gain \(G\)[26]. Thus, the \(G\times K\) spreading code matrix \(\textbf{{c}}\) of \(K\) source sensors or \(K\) cooperative sensors is given by \(\textbf{{c}}_{o,K}=\left[\textbf{{c}}_{1},\cdots,\textbf{{c}}_{k},\cdots, \textbf{{c}}_{K}\right]\). Moreover, the spreading codes between wireless sensors are orthogonal, that is \(\frac{1}{G}[\textbf{{c}}^{T}\textbf{{c}}]=\textbf{{I}}\), where \(\textbf{{c}}^{T}\) is the transposed matrix of \(\textbf{{c}}\), and \(\textbf{{I}}\) is the unit matrix.  
Time-Frequency Coded Cooperative Transmission of Source Sensors and Cooperative Sensors in Two Phases  
Before the signals are transmitted by \(K\) source sensors in the considered WSN, Frame Check Sequence (FCS) should be added to the original data stream firstly, then convolution coding and BPSK modulating are applied to obtain the \(M\) bits channel coding frame \(\textbf{{b}}_{k}^{t}\). Then, the channel coding frame \(\textbf{{b}}_{k}^{t}\) will be coded according to Figure 4 to get the time-frequency coded frame \(\textbf{{d}}_{k}^{1}\) and \(\textbf{{d}}_{k}^{2}\) which are corresponding to phase 1 and phase 2, respectively.
# Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation
## 2 Materials and Methods
### Pore Solution Analysis
#### 2.2.3 Chemistry of Pore Solution  
It is obvious that the addition of extra water in ESL dilutes the solution obtained, resulting in an underestimation of the ion concentration. To obtain the composition of the pore solution from the results of ESL, a calculation process was conduct by considering the free water content in a paste sample and the water added during leaching [22; 25]. The calculation method comprised the following steps.  
1. Measurement of free water content  
When the prismatic paste was broken up for the leaching test, one part of the prism was taken for the measurement of free water content. The weight of the sample was recorded as \(W_{1}\), and then it was dried in an oven at 60 \({}^{\circ}\)C. The weight after drying was recorded as \(W_{2}\), so the free water content \(W_{b}\) is equal to \((W_{1}-W_{2})/W_{1}\times 100\%\).  
2. Conversion from leaching solution to pore solution The conversion is based on the assumption that in one part of cement, the sum of the soluble ions in leaching solution and pore solution is identical, namely  
\[C_{p}\times V_{p}=C_{L}\times V_{L} \tag{1}\]  
where \(C_{p}\) and \(V_{p}\) stand for the ion concentration and volume of pore solution respectively; \(C_{L}\) and \(V_{L}\) stand for the ion concentration and volume of leaching solution respectively. Considering the free water content, the calculation formula for the ion concentration of pore solution finally can be written as  
\[C_{p}=C_{L}\times\frac{(1-W_{b})}{W_{b}} \tag{2}\]  
(3) Data correction by PHREEQC  
Basically, the data conversion from leaching solution to pore solution is a process of concentration, in which precipitation may occur due to ion saturation or compatibility. A geochemical software PHREQC (Version 3.4.0.12927, database waterq4f) was introduced to simulate the equilibrium reaction after conversion. The model was built by using three modules: SOLUTION, REACTION and EQUILIBRIUM PHASES. Firstly, the initial condition, including the pH and the concentration of every component measured in leaching solution, was set in the module of SOLUTION. Secondly, conversion was accomplished in the module of REACTION by removing water from the chemical system. Water can be specified as an irreversible reactant with a relative stoichiometry of \"\(-1\)\". The reaction amounts can be determined based on the difference of Na\({}^{+}\) between \(C_{p}\) and \(C_{L}\). Finally, EQUILIBRIUM PHASES was applied to reach the equilibrium state of the chemical system after removing water. For OPC system, Ca(OH)\({}_{2}\) was considered as the equilibrium phase, while in CSA system, Al(OH)\({}_{3}\), CaSO\({}_{4}\) and Ca(OH)\({}_{2}\) were considered. The saturation index (SI) was set as \"0\", which means the solution was saturated (in equilibrium).
## 4 Experiments and Their Outcomes
### Answer to Third RQ
Explanation for better outcomes: our work proposes combining a BiLSTM with a CNN model. The capacity of BiLSTM models to store two-directional context data efficiently--forward (next) and backward (previous)--is the chief factor for the suggested model's enhanced performance. Its improved representation of data (input text), gained via the CNN model, allows it to gather information not only from the current input but also from previous ones, preventing information decay. This results in effective court decision prediction using past legal data, since the BiLSTM model maintains both present and previous context data, whereas the CNN model extracts just localized features. Due to the improved representation of the input text, this results in high classification results. This study makes a unique contribution by demonstrating the capability of hybrid DL with effective feature selection for anomaly detection methods. To classify incoming traffic into legitimate or malicious categories, we presented a hybrid DL method based on CNN-BiLSTM. In comparison to shallow learners, our method outperformed them in terms of accuracy, recall, F1-measure, and precision. Because of their capacity to cope with a large degree of complicated nonlinear relationships, DL methods hold promise for accurately detecting intrusions. It may be used to overcome the limitations of conventional classification approaches, which rely on classical feature encoding to detect anomalous traffic [5].
# Joint Invariants of Linear Symplectic Actions
## 5 From Joint to Differential Invariants
### Functions and Other Examples  
Let us discuss invariants of jets of functions on the symplectic plane. The action of \(G=\operatorname{Sp}(2,\mathbb{R})\) on \(J^{0}V=V\times\mathbb{R}(u)\simeq\mathbb{R}^{3}(x,y,u)\), with \(l_{0}=u\) invariant, prolongs to \(J^{\infty}(V)=\mathbb{R}^{\infty}(x,y,u,u_{x},u_{y},u_{xx},u_{xy},u_{yy},\dots)\). Please note that functions can be identified as surfaces in \(J^{0}V\) through their graphs.  
For any finite set of points \(\hat{A}_{k}=(x_{k},y_{k},u_{k})\) the values \(u_{k}\) are invariant, and the other invariants \(a_{ij}\) are obtained from the projections \(A_{k}=(x_{k},y_{k})\). In this way we get the basic first order invariant (as before we omit indices \(x_{1}=x\), \(y_{1}=y\), \(u_{1}=y\) for the reference point \(A_{1}\) in the right-hand side)  
\[I_{1}=\lim_{A_{0},A_{2}\to A_{1}}\frac{a_{01}(u_{1}-u_{2})+a_{12}(u_{1}-u_{0}) }{a_{01}-a_{02}+a_{12}}=xu_{x}+yu_{y}\]  
as well as two invariant derivations  
\[\
abla_{1}=\overrightarrow{OA_{1}}=x\mathcal{D}_{x}+y\mathcal{D}_{y},\quad \
abla_{2}=\lim_{A_{0}\to A_{1}}\frac{I_{1}}{a_{01}}\overrightarrow{A_{1}}- \frac{u_{1}-u_{0}}{a_{01}}\overrightarrow{OA_{1}}=u_{x}\mathcal{D}_{y}-u_{y} \mathcal{D}_{x}.\]  
To obtain the second order invariant \(I_{2c}=u_{x}^{2}u_{yy}-2u_{x}u_{y}u_{xy}+u_{y}^{2}u_{xx}\) let \(A_{0}\) belong to the line through \(A_{1}\) in the direction \(\
abla_{2}\) (this constraint reduces the second order formula to depend on only two points), i.e., \(A_{0}=(x+\epsilon u_{y},y-\epsilon u_{x})\), \(A_{1}=(x,y)\). Then \(u_{0}-u_{1}=\frac{\epsilon^{2}}{2}I_{2c}+o(\epsilon^{2})\), \(a_{01}=\epsilon I_{1}\) and letting \(\epsilon\to 0\) we obtain  
\[\lim_{\begin{subarray}{c}A_{0}\to A_{1}\\ A_{0}A_{1}\|\
abla_{2}\end{subarray}}\frac{u_{0}-u_{1}}{a_{01}^{2}}=\frac{I_{ 2c}}{2l_{1}^{2}}.\]  
In the same way we get \(I_{2a}=x^{2}u_{xx}+2 xyu_{xy}+y^{2}u_{yy}\) and \(I_{2b}=xu_{y}u_{xx}-yu_{x}u_{yy}+(yu_{y}-xu_{x})u_{xy}\). These however are not required as the algebra of differential invariants is generated as follows ([7], SS3.1) for some differential syzygies \(\mathcal{R}_{i}\):  
\[\mathcal{A}_{G}=\langle l_{0},l_{2c}\,;\,\
abla_{1},\
abla_{2}\,|\,\mathcal{R }_{1},\mathcal{R}_{2},\mathcal{R}_{3}\rangle.\]  
Similarly, one can consider surfaces in the contact 3-space (with the same coordinates \(x,y,u\) but different lift of \(\operatorname{Sp}(2,\mathbb{R})\) extended to \(\operatorname{GL}(2,\mathbb{R})\)) and higher-dimensional cases. The idea of discretization of differential invariants applies to other problems treated in [7].
## 3 Results
### Level of Patient Satisfaction
#### 3.2.4 Additional Non-Paid Services  
Patients appeared to be comparatively more satisfied (3.14 \(\pm\) 0.1) with on-call services, as displayed in Table 5. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Characteristics** & **Range/Groups** & **Frequency** & **Percentage** \\ \hline \multirow{8}{*}{Age (years)} & 25 or below & 93 & 8.5 \\  & 26–30 & 181 & 16.6 \\  & 31–35 & 289 & 26.5 \\  & 36–40 & 207 & 19.0 \\  & 41–45 & 168 & 15.4 \\  & 46–50 & 103 & 9.4 \\  & 51 or above & 47 & 4.3 \\  & Mean age \(\pm\) SD & 35.2 \(\pm\) 3.6 & \\ \hline \multirow{8}{*}{Educationalstatus} & Below matriculation & 44 & 4.0 \\  & Matriculation & 368 & 33.8 \\ \cline{1-1}  & Intermediate & 219 & 20.1 \\ \cline{1-1}  & Bachelor & 259 & 23.8 \\ \cline{1-1}  & Master & 183 & 16.8 \\ \cline{1-1}  & Higher Education & 15 & 1.4 \\ \hline \multirow{2}{*}{Gender} & Male & 917 & 84.3 \\  & Female & 171 & 15.7 \\ \hline \multirow{8}{*}{Monthly income inPakistani rupees (PkR)} & Less then or 15,000 & 251 & 23.1 \\  & 15,001–30,000 & 286 & 26.3 \\ \cline{1-1}  & 30,001–45,000 & 216 & 19.8 \\ \cline{1-1}  & 45,001–60,000 & 249 & 22.8 \\ \cline{1-1}  & More then 60,000 & 86 & 7.9 \\ \hline \multirow{8}{*}{ Disease (for which participant visited pharmacy)} & Gastrointestinal diseases & 237 & 21.7 \\ \cline{1-1}  & Respiratory infections & 212 & 19.4 \\ \cline{1-1}  & Cardiac diseases & 198 & 18.2 \\ \cline{1-1}  & Orthopedic problems & 146 & 13.4 \\ \cline{1-1}  & Primary tract infections & 123 & 11.3 \\ \cline{1-1}  & Eye diseases & 94 & 8.6 \\ \cline{1-1}  & Dermatological problems & 71 & 6.5 \\ \cline{1-1}  & Oncological diseases & 5 & 0.4 \\ \cline{1-1}  & Other diseases & 2 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of study participants (\(n\) = 1088).