esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bio-adsorption Percentage
[PERSON] [109] reported a 98.03% removal efficiency of Cd using avocado seed-pretreated carbon at concentrations of 200 mg/L, with an adsorbent dosage of 0.10 g, a pH of 3.37, and a contact time of 100 min. The affinity of the avocado bio-adsorbent for the retention of Pb compared to the other metals tested (Cd, Hg, and Ni) was evident as higher concentrations were adsorbed. This could be related to the solubility of each metal ion, considering that Pb has a substantially lower solubility in aqueous media when it forms different salts. It has been demonstrated that the adsorption process for metals in different sorbents is affected by the initial pH of the solution since the pH determines the surface charge of the adsorbent and the degree of speciation and ionization of the adsorbate [87; 110]. Moreover, other factors such as temperature, initial metal concentration, coexisting ions, and adsorbent dosage also affect the adsorption equilibrium [83]. Studies have shown the efficient removal of Cd and Pb using modified avocado seed at a maximum pH of 6, whereas, at lower pH values, the presence of carboxyl groups (-COOH) in the avocado seed retains protons, reducing the probability of binding to any  
Figure 3: Removal percentages for initial concentrations of Cd, Hg, Pb, and Ni pretreated with ethanol (blue) and ethylene glycol (red). positively charged metal ions [76]. Mqehe-Nedziwhe [101] achieved a higher removal percentage by increasing the initial concentration from 5 to 30 mg/L when solutions were adjusted to a pH of 6 at 25 \({}^{\circ}\)C.  
For Hg, less than 80% removal percentage was obtained at lower concentrations of 1 mg/L for both pretreatments. Finally, for Ni, the ethanol-pretreated bio-adsorbent showed a slightly higher retention than the ethylene glycol-pretreated one. However, the limited adsorption did not show that avocado biomass was a good adsorber for this metal. In all cases, the relative standard deviations of the replicates were less than 10%, demonstrating acceptable reproducibility of metal sequestration.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
###### Abstract  
Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires extensive planning to ensure vehicle safety and mission success, particularly when dealing with strong ocean currents. As gliders are often involved on missions that pursue multiple objectives (track events, reach a target point, avoid obstacles, sample specified areas, save energy), path planning requires a way to deal with several constraints at the same time; this makes glider path planning a multi-objective (MO) optimization problem. In this work, we analyse the usage of the non-dominated sorting genetic algorithm II (NSGA-II) to tackle a MO glider path planning application on a complex environment integrating 3D and time varying ocean currents. Multiple experiments using a glider kinematic simulator coupled with NSGA-II, combining different control parameters were carried out, to find the best parameter configuration that provided suitable paths for the desired mission. Ultimately, the system described in this work was able to optimize multi-objective trajectories, providing non dominated solutions. Such a planning tool could be of great interest in real mission planning, to assist glider pilots in selecting the most convenient paths for the vehicle, taking into account ocean forecasts and particular characteristics of the deployment location.  
multi-objective optimization; underwater glider; path planning; genetic algorithm; NSGA-II +
Footnote †: journal: Sensors
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 2 NB-IoT
### NB-IoT with Other 3 GPP Technologies  
The 3 GPP completed the first NB-IoT specification in 2016 and standardization is ongoing for schemes to strengthen mobility and reduce transmission delay. Although the NB-IoT appeared after LoRa and SIGFOX, it solves the congestion problem by using licensed bands, which enable more reliable services for mission-critical applications. In addition, given that the NB-IoT can coexist with GSM and LTE networks, which have already been successfully deployed, it is possible to use the existing network hardware and reduce the deployment cost [11]. Considering other 3 GPP technologies, the maximum NB-IoT data rate has decreased, but its cell coverage is enhanced and the hardware complexity is reduced by 90% compared to that of LTE Cat-1, as shown in Figure 2. Hence, the NB-IoT can reduce the cost and energy consumption, which are the main shortcomings of cellular network technology for IoT devices.  
Figure 2: Comparison between narrowband (NB)-IoT and other technologies.
# Integrating a Blockchain-Based Governance Framework for Responsible AI
## 2 Programming Smart Contracts for Responsible AI
### Technological Issues of Smart Contracts  
Smart contract implementation entails a variety of technological issues that are currently being solved and improved. Scalability is the most significant bottleneck. The network can become crowded and sluggish as more transactions are added to a blockchain, resulting in high fees and long processing times. Smart contracts can be incorporated throughout chains to mitigate this issue (multiple platforms). There are now several blockchain systems, each with its own set of unique capabilities and restrictions [22; 88]. As a result, interoperability solutions that enable smart contracts to communicate and function across multiple blockchains are required [89].  
Another significant integration difficulty is that smart contracts are self-executing and irreversible, which means that any faults or vulnerabilities in the code might have catastrophic effects [90]. As a result, comprehensive security testing and audits are required to verify that smart contracts are secure and free of vulnerabilities. Furthermore, while smart contracts have several advantages, they might be difficult to use and comprehend for non-technical people. As a result, improved user interfaces and tools that make it easier for individuals to build, implement, and interact with smart contracts are required [91]. Recently, there has been a lot of discussion about decentralisation and privacy [92]. Smart contracts are often maintained on a public blockchain, which means that the contract's terms are available to everyone on the network. There is a demand for privacy solutions that enable smart contracts to be executed more privately and securely.  
In addition to the major technical challenges mentioned earlier, there are a number of minor challenges that can arise when implementing smart contracts. Code complexity is one of these. Smart contracts can be complex, with many lines of code and complex logic. This can make it difficult to debug and test the code, and can lead to errors that are difficult to spot. Integrating smart contracts with existing systems can be another challenge, particularly if those systems are based on legacy technologies [93]. This can require additional development work and may result in interoperability issues. Moreover, smart contracts may need to comply with legal and regulatory requirements, such as data protection laws or financial regulations. Ensuring compliance can be a challenge, particularly as the regulatory landscape is constantly evolving [94]. After a smart contract has been deployed on a blockchain, it can be difficult to make changes to the code. Upgrading a smart contract can require coordination with other parties, and can result in downtime or other disruptions.  
While these challenges may be considered minor compared to the major technical challenges, they can have a significant impact on the implementation of smart contracts. Addressing these challenges requires careful planning, collaboration, and ongoing development efforts.
## 1 Introduction  
The Malayalam language is spoken by the people of Kerala State in India. After the pandemic situation created by the novel coronavirus, it has become a necessity to encode the local language, conventionally written in pen and paper, in an electronic format. Optical character recognition (OCR) systems convert handwritten documents to a computer-editable digital form. This is highly beneficial for individuals to share documents among different offices, banks, and teacher notes when conducting online classes in the local language. Malayalam is a language with a rich character set and it is written from left to right. Some of the vowel diacritics in the language extend above or below the full length of a normal alphabet. The language contains compound letters formed of two different characters. The characters have a looped and curved nature. Some characters, like 'Chandrakkala', in the language are written with a space above another character. Because of the large variations in handwriting styles, large gaps are created above or below the characters where such letters are written. Some of these possible cases are illustrated in Figure 1. All these factors make the recognition of handwritten documents in this language a complex problem [1,2]. Therefore, the development of OCR for the recognition of unconstrained handwritten document images for the Malayalam language has not progressed yet. The work presented in this paper extracts the text lines from handwrittenMalayalam by considering different handwriting styles in the language. Most of the research is conducted on the recognition of isolated handwritten Malayalam characters. As far as unconstrained handwritten documents are considered, the recognition of individual characters can be performed in a hierarchical way. First, text lines are extracted; then, words are segmented from each line; after this, characters are segmented from each word. These characters are recognized and converted into digital form. Using this digital representation, the handwritten characters are encoded into a printable text format. The success of this entire process is mainly dependent on the correctness of the text line extraction step. In this paper, a novel technique for the extraction of text lines from Malayalam handwritten documents is developed.  
The paper is organized as follows. Section 2 describes the related work in this area. Section 3 presents the proposed methods for text line extraction. The experimental setup, results and discussion are detailed in Section 4, while Section 5 concludes the work.
## 1 Introduction
### Objectives and Scope  
The main goal of this study was to develop decision-making factors that could be used to determine EMP protection level. A group of EMP protection experts from the military and private sector was set up, and the Delphi method was used to derive a differential EMP protection method. The derived decision-making factors were applied to model brigade-level EMP protection facilities for the military. Based on this, the protection-level differentiation method of EMP protection facilities was analyzed.
## 2 Methods
### Implementation Process
#### 2.2.3 Watershed Delineation  
The watershed is the principal tool of morphological segmentation [44]. Watershed regions are topographically defined by the conjunction of watershed boundaries and confluence boundaries, typically comprising ridge-line and valley-line elements. Building upon the skeletonized line network obtained through previous processing, this method employs a two-step spatial connectivity analysis: first by detecting critical connection points through spatial indexing and then constructing hydrological catchment boundaries using a KD-tree-based linking algorithm.  
A KD-tree is a space-partitioning data structure designed for efficient nearest neighbor searches in multi-dimensional spaces. Each node in the tree represents a data point, and recursive partitioning along dimension axes subdivides the space into orthogonal hyperrectangles. During construction, points are hierarchically divided into subsets by selecting splitting criteria along alternating dimensions, creating a balanced binary search structure that enables logarithmic time complexity for proximity queries.  
Figure 3: Comparison of whether to use morphological preprocessing: (**a**) non-use of morphological preprocessing; (**b**) usage of morphological preprocessing.  
The connection process employs a bidirectional spatial linking strategy that utilizes skeleton line endpoints and intersections as key connection points. This two-phase procedure begins with ridge-based initiation: Starting from ridge-line endpoints or intersections, the KD-tree facilitates efficient nearest neighbor searches to identify corresponding valley-line connection points through Euclidean distance minimization. Edges are incrementally established between compatible points, expanding connectivity until either all reachable nodes are connected or dead-ends are encountered. Subsequently, valley-based completion reverses the linking direction by initiating connections from valley-line junctions, ensuring bidirectional topological integration between ridge and valley segments. Any residual unconnected points resulting from incomplete linkages undergo morphological pruning, where orphaned skeleton segments are systematically removed through iterative erosion or dilation operations. This integrated framework not only reconstructs watershed networks with geometric precision but also maintains hydrological coherence through adaptive spatial partitioning and intelligent connection strategies, effectively balancing computational efficiency with topographic accuracy.
# A Gene Expression Programming Model for Predicting Tunnel Convergence
## 2 Case Study
### Construction Method  
NATM was implemented for the tunnel excavation in the KUR project. Aiming to ensure ground self-stability until the completion of the lining installation, the excavation of the tunnel was distributed into two sections: Top heading and bench. The top heading was excavated in one step and the bench in two steps. Figure 2 shows the tunnel dimensions and excavation sequence. According to this figure, the tunnel has a horseshoe-shape with 9 m height and 9.6 m width, after excavation, and 7.8 m height and 8.4 m width with the lining. A layer of shotcrete lining with 0.30 m thickness including wire mesh (10 mm diameter at 150 mm by 150 mm spacing) together with lattice girders formed the primary support system. A 30 cm thick reinforced concrete layer shaped the final tunnel lining.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**In-Situ Test** & **Unit** & **Min** & **Max** \\ \hline SPT & - & 22 & 76 \\ Permeability & m/s & \(10^{-9}\) & \(6.9\times 10^{-2}\) \\ Unit Weight & kN/m\({}^{3}\) & 17 & 22.4 \\ Moisture content & \% & 3.5 & 18.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geotechnical field testing results for Phase I of KUR.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**L1**} & \multicolumn{2}{c}{**L2**} \\ \cline{3-5}  & & **Min** & **Max** & **Min** & **Max** \\ \hline SPT & - & 39 & 46 & 49 & 57 \\ Friction angle (\(\Phi\)) & (\({}^{\circ}\)) & 24 & 31.5 & 36.5 & 33 \\ Cohesion (c) & (kPa) & 23 & 38 & 11 & 23 \\ Elasticity modulus (E) & (MPa) & 48 & 81 & 64 & 89 \\ Poisson’s ratio (v) & - & 0.3 & 0.32 & 0.3 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical Properties of soil layers in Part I of KUR.  
As schematically represented in Figure 2, the excavation of the tunnel was separated into three steps during which the top heading (Step I) was excavated first. The excavated area was supported by using a primary support system after excavating the top heading for a distance of 1.2 m. Excavation for Steps II and later III began upon reaching the 120 m distance for the excavation of the top heading. Tunneling process was completed by applying concrete reinforced liner as a final tunnel support, after installation of the waterproof membrane.
## 2 Phenomenological Description
### Transducer Arrays for the Generation of Directional Waves in a Plate  
For some measurement applications, especially when localization of obstacles is of key interest, it is advantageous to directionally send or receive sound waves. The first step to obtain directional ultrasound radiation is the generation of directional flexural waves in the plate. This can be achieved  
Figure 3: Determination of the radiation angle. (**A**) Wavelength of the A\({}_{0}\)-mode in a 2 mm thick GF/PA6 plate; (**B**) Radiation angle of sound waves in air.  
Figure 2: Dispersion diagrams for a GF/PA6 plate. (**A**) Phase velocity; (**B**) Group velocity.  
by using transducer arrays instead of a single transducer. Through adaptation of the actuator array configuration, the mainly excited or received wave mode can be precisely controlled [22]. Furthermore, the directivity of the waves can also be adjusted [23; 24]. The simplest setup is a one-dimensional array on the surface of the plate. Depending on the size and the distance between the individual transducers and the time-delays in the electrical driving signal, the directional characteristics can be adjusted, for example, to amplify the wave generation in one direction. It is also possible to use electronic beam-steering (see e.g., [25]) in order to vary the direction of the dominant wave generation.  
For a one-dimensional array the optimal delay time for an additive superposition in the array longitudinal direction can be analytically calculated using the transducer offset \(\Delta l\) and the phase velocity \(c_{\mathrm{p}}\). In the case of a GF/PA6 plate and a 15 mm offset between the actuators the optimal time delay yields [26]:  
\[\Delta t=\frac{\Delta l}{c_{\mathrm{p}}}=\frac{15\;\mathrm{mm}}{640\;\mathrm{ ms}^{-1}}=23.4\;\mathrm{\upmu s} \tag{2}\]
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### GNSS Error Analysis and Modeling
#### 3.2.4 Multipath Effect Error
\tag{43}\]  
In the GNSS/INS integrated navigation system, if the number of satellites is \(n\), then the pseudorange observation equation obtained from Equation (41) is as follows:  
\[Z_{\rho}=H_{\rho}X+V_{\rho}, \tag{44}\]  
where  
\[H_{\rho}=[O_{n\times 6}H_{\rho 1}O_{n\times 6}H_{\rho 2}], \tag{45}\]  
\[H_{\rho 1}=\left[ \begin{array}{ccc}e_{1x_{u}}&e_{1y_{u}}&e_{1z_{u}}\\ e_{2x_{u}}&e_{2y_{u}}&e_{2z_{u}}\\ \vdots&\vdots&\vdots\\ e_{nx_{u}}&e_{ny_{u}}&e_{nz_{u}}\end{array} \right]\delta C_{n}^{e},H_{\rho 2}= \left[ \begin{array}{ccc}-1&0\\ -1&0\\ \vdots&\vdots\\ -1&0\end{array} \right]. \tag{46}\]  
In the same way, the pseudorange rate from the carrier to the GNSS satellite calculated by INS is as follows:  
\[\dot{\dot{\rho}}_{I}^{i}=\dot{\rho}_{i}+e_{ix_{u}}\dot{\delta x}_{u}+e_{iy_{u }}\dot{\delta y}_{u}+e_{iz_{u}}\dot{\delta z}_{u} \tag{47}\] \[=e_{ix_{u}}(\dot{x}_{u}-\dot{x}_{s}^{i})+e_{iy_{u}}(\dot{y}_{u}- \dot{y}_{s}^{i})+e_{iz_{u}}(\dot{z}_{u}-\dot{z}_{u}^{i})+e_{ix_{u}}\dot{\delta x }_{u}+e_{iy_{u}}\dot{\delta y}_{u}+e_{iz_{u}}\dot{\delta z}_{u}\]  
The pseudorange rate that is output by the GNSS receiver can be obtained by deriving the pseudorange \(\dot{\rho}_{G}^{i}\) in Equation (40):  
\[\dot{\rho}_{G}^{i}=\frac{\rho_{G}^{i}}{dt}=\frac{d(\rho_{i}+b_{cl}+\
u_{pi})} {dt}=\dot{\rho}_{i}+\dot{b}_{cl}+\dot{v}_{pi}. \tag{48}\]  
By combining Equation (47) with Equation (48), the pseudorange rate difference observation equation of INS and GNSS can be obtained:  
\[\dot{\delta y}_{i}=\dot{\rho}_{I}^{i}-\dot{\rho}_{G}^{i}=e_{ix_{u}}\delta x_{ u}+e_{iy_{u}}\dot{\delta y}_{u}+e_{iz_{u}}\dot{\delta z}_{u}-\dot{b}_{cl}- \dot{v}_{pi}. \tag{49}\]  
Similar to the pseudorange error, the velocity error in the geographical coordinate system must be converted into the velocity error in the ECEF coordinate system, as shown in the following formula:  
\[\left[ \begin{array}{c}\dot{\delta x}_{u}\\ \dot{\delta y}_{u}\\ \dot{\delta z}_{u}\end{array} \right]=C_{n}^{e}\left[ \begin{array}{c}\delta V _{N}\\ \delta V_{D}\\ \delta V_{D}\end{array} \right]. \tag{50}\]  
By comparing this with Equation (44), the measurement equation of the pseudorange rate can be obtained as follows:  
\[Z_{\dot{\rho}}=H_{\dot{p}}X+V_{\dot{\rho}}, \tag{51}\]  
where  
\[H_{\dot{\rho}}=[O_{n\times 3}H_{\dot{\rho}1}O_{n\times 9}H_{\dot{\rho}2}], \tag{52}\]  
\[H_{\dot{\rho}1}=\left[ \begin{array}{ccc}e_{1x_{u}}&e_{1y_{u}}&e_{1z_{u}}\\ e_{2x_{u}}&e_{2y_{u}}&e_{2z_{u}}\\ \vdots&\vdots&\vdots\\ e_{nx_{u}}&e_{ny_{u}}&e_{nz_{u}}\end{array} \right]C_{n}^{e},H_{\dot{\rho}2}= \left[ \begin{array}{ccc}-1&0\\ -1&0\\ \vdots&\vdots\\ -1&0\end{array} \right]. \tag{53}\]  
By combining the observation equations of the pseudorange and pseudorange rate information, we can obtain the closely combined observation equation:\[Z(t)=H(t)X(t)+V(t)=\left[ \begin{array}{c}Z_{\rho}(t)\\ Z_{\dot{\rho}}(t)\end{array} \right]=\left[ \begin{array}{c}H_{\rho}(t)X(t)+V_{ \rho}(t)\\ H_{\rho}(t)X(t)+V_{\rho}(t)\end{array} \right]. \tag{54}\]
## 3 Mudstone Electrochemical Modification Tests Program
### Determination of the Electrolyte Solution Amount  
During the test of the electrochemical modification of mudstone, the volume of electrolytes injected into the sample via the separatory funnel needs to be determined through repeated modification procedures. The specific determination method is as follows:  
1. Begin with a given initial volume of electrolytes for testing. When the current stabilizes at zero and no longer changes, remove the sample, weigh it to determine the decrease in mass, and then conduct a direct shear test on the sample.
2. Replenish the electrolyte solution with the same mass as the initial mass reduction in the specimen. Inject the electrolyte into the specimen that has completed the initial  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Electrode**} & \multicolumn{4}{c}{**Electrolyte**} & \multicolumn{4}{c}{**Mass Fraction of CaCl2 Solution**} & \multicolumn{4}{c}{**Mass Fraction of AlCl3 Solution**} \\ \cline{2-13}  & **CaCl2** & **AlCl3** & & & & & & & & & & \\ \hline Iron & Test 1 & Test 2 & - & - & - & - & - & - & - & - & - \\ Aluminum & Test 3 & Test 4 & - & - & - & - & - & - & - & - & - \\ Graphite & Test 5 & Test 6 & 15\% & 20\% & 25\% & 30\% & 35\% & 5\% & 10\% & 15\% & 20\% & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrochemical modification test protocol.  
Figure 2: Experimental device for electrochemical modification of mudstone specimens.  
modification 4-5 times, allowing it to stand so that the electrolyte is uniformly distributed. Conduct the electrochemical modification again until the current stabilizes at zero. To determine the mass reduction value, remove the secondary modified specimen and weigh it. Maintain the same vertical direction as the initial test specimen and conduct a direct shear test on the secondary specimen.
3. Repeat the above process until the modification experiment no longer changes the shear strength of the mudstone. The total amount of electrolyte consumed in this repeated process is the volume required for a mudstone sample to achieve complete modification. Prepare mudstone samples, conduct electrochemical modification tests according to the above experimental conditions, and test the specimen's liquid and plastic limits and shear strength parameters. Compare these properties to evaluate the electrochemical modification effects under different experimental conditions.