esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Opportunistic networks (OppNets) provide a scalable solution for collecting delay-tolerant data from sensors to their respective gateways. Portable handheld user devices contribute significantly to the scalability of OppNets since their number increases according to user population and they closely follow human movement patterns. Hence, OppNets for sensed data collection are characterised by high node population and degrees of spatial locality inherent to user movement. We study the impact of these characteristics on the performance of existing OppNet message replication techniques. Our findings reveal that the existing replication techniques are not specifically designed to cope with these characteristics. This raises concerns regarding excessive message transmission overhead and throughput degradations due to resource constraints and technological limitations associated with portable handheld user devices. Based on concepts derived from the study, we suggest design guidelines to augment existing message replication techniques. We also follow our design guidelines to propose a message replication technique, namely Locality Aware Replication (LARep). Simulation results show that LARep achieves better network performance under high node population and degrees of spatial locality as compared with existing techniques.  
Keywords:opportunistic networks; delay tolerant data; sensed data collection; routing; wireless sensors; smart city; Internet of Things; smartphones +
Footnote †: journal: Information
## 5 Discussion
Furthermore, this approach allows for the inclusion of new objects in future studies without altering the scores of other objects, thereby preventing rank reversal and improving the  
Figure 7: Overall satisfaction in the cities based on QSL question. Source: See maps in [47]. overall stability of the analysis. The comparison of results obtained through BTOPSIS and the analysis of the QSL question emphasizes the consistency and robustness of the findings, while also highlighting the strengths and limitations of both approaches. This illustrates how different methodologies, when applied together, can shape a more comprehensive understanding of resident satisfaction in European cities, offering complementary insights into regional trends and intra-country variations. It is worth noting that a series of studies [10; 44; 73; 76] introduced an intuitionistic fuzzy approach for assessing socio-economic phenomena using survey data measured on an ordinal scale. This approach recognizes that evaluations can encompass both positive and negative opinions, alongside an element of uncertainty, often expressed through responses such as \"no\", \"difficult to say\", or \"no opinion\". Subsequent research systematically explores the application of Intuitionistic Synthetic Measures, focusing on diverse techniques for defining ideal solutions, creating weighting systems, and applying various distance measurement methods. One notable application is the use of Intuitionistic Fuzzy TOPSIS [44] to rank EU cities by quality of life based on survey data from earlier editions. The study [44] also highlights the strengths and limitations of both IF TOPSIS and classical TOPSIS in handling survey data. While IF TOPSIS integrates an additional dimension of uncertainty--such as missing answers, refusals, or lack of knowledge--often ignored by the classical TOPSIS method, it does not differentiate between categories within positive and negative responses, potentially leading to information loss. Classical TOPSIS, by contrast, allows for more detailed distinctions between opinion categories but overlooks uncertainty and missing responses, which are not typically accounted for in the measurement scale used in such analyses. This comparison of BTOPSIS, Classical TOPSIS, Intuitionistic Fuzzy TOPSIS, and analysis QSL survey question highlights the strengths and limitations of different approaches, showing how different methodologies can shape the overall picture of resident satisfaction in European cities.
## 2 Materials and Methods
### Microstructural Analysis  
To examine the microstructures, the remaining samples (n = 1 for each condition) were cut in directions transverse to their longitudinal directions with a low-speed diamond cutter to form cylindrical samples (diameters: \(\sim\)3 mm; heights: \(\sim\)1 mm). The cylindrical samples were polished using waterproof emery paper (up to 1000 grit), a 9 \(\upmu\)m diamond, and a 0.04 \(\upmu\)m colloidal silica suspension, then etched with an H\({}_{2}\)SO\({}_{4}\)/CH\({}_{3}\)OH (5:95) solution at 16-20 V for 1 s. The microstructures were then evaluated using confocal laser scanning microscopy (CLSM; OLS4000, Olympus, Tokyo, Japan) at the macroscale and scanning electron microscopy (SEM) at the microscale with energy-dispersive X-ray spectroscopy (EDS; S-3400 NX, Hitachi, Tokyo, Japan) at an accelerating voltage of 15 kV. The crystallographic orientation was examined via electron backscatter diffraction (EBSD; Gatan, Pleasanton, CA, USA) using a field-emission scanning electron microscope (XL-30 FEG, Philips, Amsterdam, Netherlands) with a step size of 1.1 \(\upmu\)m, a working distance of 15 mm, and an accelerating voltage of 15 kV. The TSL OIM 7 software (TSL Solutions KK, Anan, Japan) was used to obtain the inverse pole figure (IPF) maps, phases, grain boundaries (GBs), kernel average misorientation (KAM), and phase maps, as well as the grain size distribution. The grain sizes were measured using the data for all complete grains in a single map (500 \(\upmu\)m \(\times\) 500 \(\upmu\)m) [9]. Phase identification was performed using X-ray diffraction (XRD; MiniFlex, Rigaku, Tokyo, Japan) in the 20 scanning range of 40-55\({}^{\circ}\) with the diffractometer operating at 40 kV and 40 mA with Cu K\(\alpha\) radiation, a step size of 0.02\({}^{\circ}\), and a dwell time of 1.0 s per increment.
# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
## 4 Experiments and Analysis  
Full-scale experiments with a GHH LF11H model LHD (see picture in Figure 1) were carried out in a production tunnel of a sublevel stoping mine located in the Coquimbo Region (Chile), facilitated by the Chilean mining company, CMSG. Pictures of the mine site and the LHD inside its tunnel are shown in Figure 20. Figure 20a shows the mechanical workshop just outside the mine's main entrance, and Figure 20b shows the LHD during surface tests. Figure 20c shows the LHD outside the test tunnel with an almost fully loaded bucket.  
Figure 21 shows the layout of the tunnel where experiments were carried out. The LHD traveled from the \"start-end point\" shown in the figure to the end of the red line, where the stope was originally located. A stope is a large open underground space that is produced by the sublevel stoping mining method. The LHDs load ore inside the stope from a large pile of material, also sometimes referred to as the loading point or draw point. Once the LHD has a fully loaded bucket, it has to travel back to the \"start-end point\" to dump the ore.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\beta_{weigh}\) & 0.2 \\ \hline \(m\) & 0.0207 \\ \hline \(n\) & 1.0774 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weight model parameters used for the full-scale validation.  
Figure 20: LHD machine and mining facility. (**a**) Mechanical workshop just outside the mine’s main entrance; (**b**) LHD during surface tests; (**c**) LHD outside the test tunnel with an almost fully loaded bucket.  
The experiments reported here were carried out in a fully functional tunnel of the mine, i.e., a tunnel used for extracting mineral. In fact, the experiments took place during day hours, while the mine's night shift used the LHD in manual mode for production. As a result of this, the shape and location of the stope and the draw point changed every day (as the ore runs out, the inner wall of the stope is blasted as part of the mining process).  
Because of this, and since in situ experimentation was time-limited, experiments were separated in two parts. First, the field trial experiments regarding charging and collision detection of the LHD and the pile, and the LHD's excavation and payload weighing, are reported in Section 4.1. Then, pile identification experiments from an offline processing of the collected field data are presented in Section 4.2.  
The mining tunnel was narrower than recommended by the LHD manufacturer, so the LHD's positioning was more complex than initially expected. This also contributed to dividing the experiments into the two groups described above. Figure 22 shows a picture of the draw point as seen from the teleoperation system cameras.  
Figure 21: Layout of the mine’s tunnel where experiments were carried out.  
Figure 22: Operator’s point of view of the loading point used for onsite experiments.
## 4 Lagrangian Relaxation-Based Solution Procedures
### Procedure of Step 4 and 5: Dual Problem and the Subgradient Method  
Based on the weak Lagrangian duality theorem [29], the multiples are all non-negative values; that is, \(\mu^{1}_{\text{rs}},\mu^{2}_{\text{rs}},\mu^{3}_{\text{rs}},\mu^{4}_{\text{rs}}, \mu^{5}_{i},\mu^{6}_{i},\mu^{7}_{\text{rs}i},\mu^{8}_{\text{rs}i},\mu^{9}_{i} \geq 0,\forall\tau\in T,\forall\tau\in\Pi,\forall i\in I\), and \(\forall s\in S\). The objective value of the LR problem, \(Z_{\text{LR}}\), is the lower bound of the primal problem, \(Z_{\text{IP}}\). Construct the following dual problem \(Z_{\text{D}}\) based on the LR problem to calculate the tightest boundary of steps 4 and 5 in Figure 5, where the dual problem (9) is shown as [31]  
\[\text{Objective function :}\] \[\text{max }Z_{\text{D}} \tag{9}\] \[\text{subject to :}\mu^{1},\mu^{2},\mu^{3},\mu^{4},\mu^{5},\mu^{6}, \mu^{7},\mu^{8},\mu^{9}\geq 0.\]  
There are several methods to solve the dual problem (9). The most popular one of them is the subgradient method proposed in Reference [28; 29]. First, define the multiplier vector of \(k\)-th iteration, \(\pi^{k}=(\mu^{1,k},\mu^{2,k},\mu^{3,k},\mu^{4,k},\mu^{5,k},\mu^{6,k},\mu^{7,k}, \mu^{8,k},\mu^{9,k})\), and let it to be iteratively updated by \(\pi^{k+1}=\pi^{k}+\mu^{k}\,s^{k}\), where \(k\) is the number of iterations. The vector  
Figure 5: Obtaining primal feasible solutions and the lower bound (LB) [20].  
is the subgradient of \(Z_{\text{D}}\) of the \(k\)-th iteration which represents the directions to the optimal solutions. The step size \(t^{k}\) is determined by \(t^{k}=\lambda\frac{(Z_{\text{IP}}^{*}-Z_{\text{D}}(\pi^{k}))}{||g||^{k}||^{2}}\), where \(Z_{\text{IP}}^{*}\) is an objective value of the primal problem, and \(\lambda\) is a constant, \(0\leq\lambda\leq 2\). The optimal solutions of the LR and dual problems for each iteration are determined by solving the sub-problems and iteratively updating the multipliers by using the subgradient method, respectively.
## 3 Results
### Synthetic Fertilizer Replacement  
The percentage of synthetic fertilizer used which is replaceable with livestock manure compost as N (urea), P2O5 (diammonium phosphate), and K2O (muriate of potash) were estimated for each district of Bangladesh, and the data is classified into five categories, i.e., less than 20% (pink colored), 21-40% (blue colored), 41-60% (green colored), 61-80% (yellow colored), and greater than 81% (red colored), as shown in Figure 13. About 20% of N content fertilizer uses could be replaced by manure compost for at least 26 of 64 districts, where 3 districts (Narail, Lakshimpur, Jhalokati) displayed the opportunity to replace more than 80% of synthetic N content fertilizer with livestock manure compost in Bangladesh in 2024. This study revealed that over 80% of P2O5 and K2O fertilizers can be substituted with manure compost across 8 and 24 districts, respectively. This suggests that compost holds greater potential compared to synthetic fertilizers in these regions. An even higher replacement rate, exceeding 100%, was observed in various districts characterized by lower agricultural crop production but a significant number of reared livestock.  
However, the average synthetic fertilizer replacement by manure compost in Bangladesh in 2024 was 31.367%, 37.89%, and 86.26% for N content fertilizer, P2O5, and K2O, respectively.
# Analysis of Non-Minimum Phase System for AC/DC Battery
## 3 Converter Analysis
### State-Space Averaging and Small-Signal Model Analysis in Buck Mode  
Figure 2b shows a simplified model of the converter in buck mode. The same procedure of the boost mode modeling will be followed to model the transfer function for the buck mode. The average state-space model in the buck mode can be expressed as follows:  
\[\begin{array}{rcl}\frac{{{di_{L1}}(t)}}{{dt}}&=&\frac{{{v_{g}}(t)-{v_{o}}(t )-{v_{c}}(t)(1-{d_{2}}(t))}}{{L_{1}}},\\ \frac{{{di_{L2}}(t)}}{{dt}}&=&\frac{{{v_{o}}(t)-{v_{c}}(t){d_{2}}(t)}}{{L_{2}} },\\ \frac{{{dv_{c}}(t)}}{{dt}}&=&\frac{{{i_{L2}}(t){d_{2}}(t)+{i_{L1}}(t)\,(1-{d_{ 2}}(t))}}{{C}},\\ \frac{{{dv_{o}}(t)}}{{dt}}&=&\frac{{{i_{L1}}(t)\,-\,{i_{L2}}(t)}}{{{\rm{C}}_{o }}}-\frac{{{v_{o}}(t)}}{{{R_{o}}{{\rm{C}}_{o}}}}.\end{array} \tag{6}\]  
The steady-state values of the converter variables are obtained from (6) after equating to zero the different differential equations, can be represented as follows:  
\[\begin{array}{rcl}{V_{\rm{C}}}&=&{{V_{g}}},\\ {D_{2}}&=&\frac{{{V_{o}}}}{{{V_{g}}}},\\ {I_{L1}}&=&\frac{{{V_{o}^{2}}}}{{{R_{o}}{V_{g}}}},\\ {I_{L2}}&=&-\frac{{{V_{o}}({V_{g}}\,-\,{V_{o}})}}{{{R_{o}}{V_{g}}}}.\end{array} \tag{7}\]The converter variables can be decomposed as the same equations in (3) in the buck mode however, using \(d_{2}\) as a duty cycle for this mode as follows:  
\[d_{2}(t) = D_{2}\ +\ \Delta d_{2}(t), \tag{7}\]  
where is \(D_{2}\) express the dc equilibrium point and the other term \(\Delta d_{2}(t)\) is the small variation around it. Also in this mode, the voltage output \(V_{o}\) should be controlled for regulating its average, and the inductor current \(i_{L1}(t)\) is controlled to reshape the line current. Thus, the same two-transfer functions: the duty cycle to input inductor current \(Gi_{L1}d\_d(s)\) and the duty cycle to output voltage \(Gvd\_d(s)\) are expressed as:  
\[Gi_{L1}d\_d(s) = \frac{E_{1}s^{2}+E_{2}s+2R_{o}V_{g}A_{v}}{Den\_d(s)\ R_{o}},\] \[Gvd\_d(s) = \frac{R_{o}\ V_{g}\left(L_{s}\ C\,s^{2}+1\right)+V_{o}\,s\left(L_ {2}-A_{v}\ L_{s}\right)}{Den\_d(s)},\]  
where,  
\[Den\_d(s) = G_{4}s^{4}+G_{3}s^{3}+G_{2}s^{2}+G_{1}s+R_{o},\] \[E_{1} = R_{o}V_{g}\,L_{2}\left(C-A_{v}\,C_{o}\left(A_{v}-1\right)+C\,C_{ o}\,R_{o}\,s\right),\] \[E_{2} = -V_{o}\ (L_{2}\left(A_{v}-1\right)-C_{o}\,{R_{o}}^{2}),\] \[G_{1} = A_{v}{}^{2}\,L_{s}-L_{2}\left(2\,A_{v}-1\right), \tag{8}\] \[G_{2} = R_{o}\ (C_{op}\,L_{2}+C\,L_{1}-A_{v}\,C_{o}\left(2\,L_{2}-A_{v}\,L_{s }\right)),\] \[G_{3} = C\,L_{1}\,L_{2},\] \[G_{4} = C\,L_{1}\,L_{2}\ C_{o}\,R_{o}.\]
## 3 Results
### Footprint of Field GPP  
#### 3.2.1 Real-Time Footprint of Field GPP  
A map of the real-time footprint of field GPP on 24 July, 2014, in which the atmosphere is relatively stable on a clear day, is shown in Figure 4. In general, the real-time footprint was consistent with the dominant wind directions, i.e., west and northwest wind. Footprint distances in the east to west direction were more than 500 m in Huyanglin (DBF) and Hunhelin (MF), demonstrating that footprint distances were the furthest in forest. In Sidaoqiao (shrub) and Shidi (wetland), the footprint distances were about 150-250 m. Footprint distances were less than 50 m in Huazhaizi (desert) and Bajitan (Gobi), which were the nearest among these land cover types. The wind direction and height of the instruments were the main factors influencing the real-time footprint, which will be discussed in Section 4.2.  
Figure 4: Real-time footprint of field GPP. The footprint displayed in this figure is from 24 July 2014. The gray level describes the contribution rate of the source area.  
Figure 3: Seasonal variation in GPP, respiration of ecosystem (RE) and net ecosystem carbon dioxide exchange (NEE) in the growing season.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 3 O-RAN in Smart IoT Systems
### Problem Space
Additionally, for energy-based IoT systems such as smart grids [68], it is critical to understand the performance requirements of smart grid applications in both the transmission and distribution side based on the standardization and carry out performance evaluation of different networking architectures and protocols. As the O-RAN is a new network infrastructure, the standardization and research community shall have joint efforts on the performance gap of O-RAN to support IoT applications via modeling, simulation, and testbed development.
## 5 Security Analysis
### Tampering Attack  
We assume that the user forwards the tampered message \(\left\{ID_{i},M_{0},M_{1}\right\}\) during the pre-checkups phase. RCRS receives \(\left\{ID_{i},M_{0},M_{1}\right\}\) and then obtains \(D_{i}\) by retrieving \(ID_{i}\) in the database. If RCRS cannot map the corresponding \(D_{i}\), it will find that these messages have been tampered with. Additionally, RCRS will also check if \(M_{0}\) is equal to \(\mathsf{h}(R_{U}\oplus\mathsf{h}(r_{i}\oplus X_{s}))\), and then use \(M_{1}\) to calculate \(M_{0}\) to solve \(R_{U}\). In other words, the attacker cannot reverse the real \(R_{U}\) by tampering with the message. In another case, the message \(\left\{ID_{Cj},M_{2},M_{3}\right\}\) transmitted by the checkups station is tampered with and forwarded to the RCRS. RCRS will also check if \(M_{3}\) is equal to \(\mathsf{h}(ID_{Cj}\oplus R_{C})\) and then use \(M_{2}\) to calculate \(M_{3}\) to solve \(R_{C}\).  
Therefore, if users and checkups stations forward these tampered messages to RCRS, RCRS can check that these messages may have been tampered with by an attacker. We assume that RCRS then sends these tampered messages \(\left\{M_{4},M_{5},M_{6}\right\}\) to the checkups stations during the verification phase, which will use \(ID_{Cj}\) and \(X_{RCCj}\oplus X_{S}\) to look in the database to confirm whether or not \(M_{5}^{*}\) equals \(M_{5}\). According to the above description, our proposed scheme can resist tampering attacks.
## 5 System Implementation
### Sentiment Analysis  
Sentiment analysis aims to identify and extract the sentiment and emotional information contained in the text. It is not only limited to simply identifying the emotional state of the text (such as positive, negative, or neutral), but can also deeply analyze the complexity and context of the sentiment, including the emotional intensity, the emotional object, and the emotional tendency behind the text [19]. As shown in Figure 5, the six basic emotions proposed by American psychologist [PERSON], [PERSON] listed six basic emotions, in turn, anger, happiness, surprise, disgust, sadness and fear. [PERSON] proposed PAD (Pleasure-Arousal-Dominance) three-dimensional emotional space. Among them, P stands for pleasantness, which indicates the positive and negative characteristics of individual emotional states. A stands for activation, which indicates the neurophysiological activation level of an individual. D stands for dominance, which indicates an individual's state of control over the situation and others. In this paper, we will use CEmotion, a third-party emotion analysis library, which has the advantages of high accuracy and is easy to call. The implementation consists of the following steps:  
1. Data acquisition: Through the sentiment classification or sentiment polarity classification of the user's text, multi-modal data are collected from the perspective of the learner's speech and text, and preprocessing and feature extraction are carried out.
2. CEmotion emotion analysis: In the teaching process, the virtual teacher's facial expressions are constantly adjusted with the changes of the situation, as shown in Figure 6. The CEmotion emotion analysis algorithm is used to process the multimodal data of the learner and obtain the emotional state of the learner.
3. Emotional interaction: According to learners' emotional state, virtual teachers automatically adjust their language style, expression, and tone to better communicate with learners. According to learners' emotional states and behavior data, virtual teachers can adjust teaching strategies, such as adjusting the difficulty, providing more support, etc., to achieve better teaching effects.  
Figure 4: Voice interaction process.
# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 3 Proposal
### Big Data and Machine Learning
#### 3.3.2 Data Frame  
To ensure a robust data frame for effective detection, we need data acquired from input and output devices susceptible to information related to bottleneck detection, key manufacturing values, and specific information about the source element:  
Figure 7: Information flow between Big Data system blocks.  
* From sensors and actuators, we can have discrete data (all/nothing), accumulated information, and numerical values from analogue sensors, such as temperatures, humidity, flow rates, levels, etc.
* The key manufacturing data [54] are those that will give us information about the moment of manufacture, such as yields, number of machine stoppages in each time, number of format changes in each time, product manufactured, etc.
* Specific information of the origin element, such as identification of the process, line, machine, specific station, etc.  
To build the prediction model using classification algorithms, it is essential to define the input and output data for the data frame. The target output should be determined by indicators and situations providing information about the bottleneck. Previous studies, such as [1; 12; 55], highlight various bottleneck indicators, such as the smallest isolation production rate (PR), work-in-process inventory (WIP), machine-specific performance, or line performance variability over time. For instance, if the bottleneck is associated with fluctuations in line performance, buffering-induced stops, and a critical temperature for the product, these factors would serve as output indicators in the model.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 2 Problem Description
### Reformulation of AEOSSP  
The solution construction can be seen as a sequential decision process. Each task decision can be considered to be a stage. As Figure 2 shows, in each stage, we can determine the next task node based on the current graph state according to the policy, which is represented using the decision neural network in the proposed method. Once the node of one stage is determined, the graph of the current state is updated. The next task node is selected according to the current state. Then, the process is repeated until a scheduling solution is constructed.  
We model this construction process as a Markov decision process (MDP) defined by the 5-tuple \(\langle S,A,T,R,C\rangle\), where:  
* \(S\) is the state set of the time-attitude adjacency graph model;
* \(A\) is the set of actions that a satellite can perform (i.e., the candidate task set);
* \(T:S\times A\to S\) is the state transition function;* \(R:S\times A\rightarrow\mathbf{R}^{+}\) is the reward function, which represents the profit of the selected task;
* \(C:S\times A\rightarrow\{0,1\}\) is the set of constraints, including constraints (2), (4) and (5). When \(C(s,a)=0\), \(T(s,a)=\perp\), which means that the constraints are not satisfied and the state transition is infeasible.  
According to the Bellman equation, under the optimal policy \(\pi^{*}\), the optimal value function satisfies:  
\[V^{*}(s)=\max_{a\in A}(R(s,a)+V(T(s,a)))\ \forall s\in S\ s.t.\ T(s,a)\
eq\perp \tag{10}\]  
The corresponding optimal strategy \(\pi^{*}\) is:  
\[\pi^{*}(s)=\operatorname*{arg\,max}_{a\in A}(R(s,a)+V(T(s,a)))\ \forall s\in S\ s.t.\ T(s,a)\
eq\perp \tag{11}\]