esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 1 Introduction  
Recent advancements in nanotechnology allowed the use of nanomachines in a plethora of applications, which ranges from flexible electronics to medical technologies [1]. Particularly, in medicine, nanotechnology offers a novel way to perform non-invasive diagnosis, with the in-vivo use of nanodevices [2]. Nanosensing can also be used to detect chemical compounds in extremely low concentrations in the human body, while other nanodevices can be used to perform drug delivery services [3, 4]. However, the aforementioned scenarios require a large number of nanomachines in order to cover sufficiently the areas under monitoring, thus creating a nanonetwork inside the human body. Moreover, in most applications, the utilized nanonetworks should be capable of communicating with nodes outside the human body.  
Advances in the field of nanomaterials now allow the exploitation of graphene and its derivatives for developing transceivers, which can be used in nanonetworks. The operational frequency of nanoantennas, constructed from these materials are in a region of 0.1 THz-10 THz, called the _terahertz gap_. Note that THz radiation is non-ionizing and, thus, it is generally considered as safe for the human body [1].
## 3 Results
### Work Stability and Movement Pattern
#### 3.2.2 Movement Pattern Analysis
In the masonry task (MT), movement patterns showed distinctive improvements during the repetitive bending motions required for bricklaying. The cycle duration CV decreased from 10.5% to 7.6% in low-intensity conditions and from 14.2% to 9.3% in high-intensity conditions, improvements of 27.6% and 34.5%, respectively. The middle graph in Figure 7 illustrates how the exoskeleton helped maintain a more consistent amplitude during the cyclical bending and reaching movements characteristic of masonry work. Figure 7: Movement pattern analysis showing acceleration profiles. For the scaffolding installation task (ST), the exoskeleton provided significant stability benefits, particularly evident in the reduction of cycle duration CV from 11.3% to 8.1% in low-intensity conditions (28.3% improvement) and from 13.9% to 8.9% in high-intensity conditions (36.0% improvement). The bottom graph in Figure 7 shows more subtle but consistent improvements in movement patterns compared to the other tasks. The movement smoothness index, calculated as the normalized jerk metric (where higher values indicate smoother movements), showed significant improvements with exoskeleton assistance across all tasks. As detailed in Table 4, the average improvement in movement smoothness ranged from 26.3% to 41.9% across different tasks and intensities, with high-intensity tasks consistently showing greater improvements. These results suggest that the exoskeleton helped participants maintain more controlled and fluid movements, particularly when performing physically demanding tasks. Notably, across all measured movement metrics, the improvements were consistently more pronounced in high-intensity conditions compared to low-intensity conditions. This pattern was observed in both cycle duration consistency and movement smoothness, suggesting that the exoskeleton provides the greatest benefits when workers are subjected to more physically demanding tasks. The combined analysis of movement patterns demonstrates that the wearable robotic exoskeleton significantly enhances work stability and movement quality across various construction tasks, with the most substantial benefits observed during high intensity work that place greater physical demands on workers.
## 6 Discussion  
To validate the performance of the proposed hybrid architecture, ablation experiments were conducted on the SE-CNN-LSTM-Attention model. The proposed SE-CNN-LSTM-Attention hybrid architecture was compared with the following variants: SE-CNN-LSTM, CNN-LSTM-Attention, CNN-Attention, and baseline CNN. The architectural configurations of all evaluated models are systematically compared in Table 11. This comparative analysis isolates the contributions of SENet channel recalibration, LSTM temporal modeling, and attention-based feature prioritization from the overall predictive performance. Please note that in the table, \"SA\" refers to spatial attention, \"CNN3\" represents three convolutional blocks, and \"Attn\" refers to the multi-head attention module.  
The hyperparameters and training data used in the table models were identical to those of the SE-CNN-LSTM-Attention hybrid neural network. The evaluation metrics of each model on the test set are presented in Table 12, and the classification confusion matrices are shown in Figure 11.  
As demonstrated in Table 12, the SE-CNN-LSTM-Attention hybrid architecture achieves accuracy improvements of 3.24%, 3.08%, 4.16%, and 6.48% on the test set compared to the SE-CNN-LSTM, CNN-LSTM-Attention, CNN-Attention, and baseline CNN models, respectively. The results indicate that the SE-CNN-LSTM-Attention architecture delivers the best performance on the test set. In conclusion, the proposed SE-CNN-LSTM-Attention hybrid neural network demonstrates robust capabilities for stratigraphic prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multirow{2}{*}{**Composition of the Core Module**} & \begin{tabular}{c} **Description of Differences** \\ **(in Comparison with the** **Model in this Paper)** \\ \end{tabular} & \multirow{2}{*}{**Key Parameter Configuration**} \\  & & & Conv1d (k = 7, 5, 3), SE (reduction = 16), \\ \hline \
\begin{tabular}{c} SE-CNN-LSTM- \\ Attention (Baseline) \\ \end{tabular} & SE + SA + CNN3 + LSTM + Attn & none & SA (k = 5, 5, 3), LSTM (h = 256), \\ \multicolumn{3}{c}{} & MultiheadAtt (4 head) \\ \multicolumn{3}{c}{} & \multirow{2}{*}{
\begin{tabular}{c} SE-CNN-LSTM \\ LSTM (h = 7, 5, 3), SE (reduction = 16), \\ LSTM (h = 256) \\ Conv1d (k = 7, 5, 3), LSTM (h = 256), \\ MultiheadAttn (4 head) \\ Conv1d (k = 7, 5, 3), MultiheadAttn (4 head) \\ Conv1d (k = 7, 5, 3) \\ \end{tabular} & SE + CNN3 + LSTM & -SA, -Attn & Conv1d (k = 7, 5, 3), LSTM (h = 256) \\ CNN-LSTM-Attention & CNN3 + LSTM + Attn & -SE, -SA, -LSTM & Conv1d (k = 7, 5, 3), MultiheadAttn (4 head) \\ CNN & CNN3 & -SE, -SA, -LSTM, -Attn & Conv1d (k = 7, 5, 3) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Statistical table of the structures of various models.  
Figure 10: Confusion matrices of the model’s training set and test set.
## 4 Training Method of COMA Policy Network Based on Differential Evolution
### Optimization of COMA Policy Network with DE
#### 4.2.2 Design of Fitness Function  
In EAs, the fitness value is the primary indicator describing an individual's performance. Individuals are selected for survival and reproduction based on their fitness values. Fitness serves as the driving force for evolutionary algorithms. The choice of the fitness function directly affects the convergence speed of the evolutionary algorithm and whether the optimal solution can be found. Establishing a reasonable fitness function for reinforcement learning problems is a key point in algorithm optimization. The fitness function is defined as the average return of DE-COMA over n training steps.  
\[fitness=\frac{1}{n}{\sum_{i=0}^{n}{reward_{i}}} \tag{3}\]  
The specific components of the \(reward\) are given as follows.  
\[reward=delta_{enemy}+delta_{deaths}-delta_{ally} \tag{4}\]  
where \(delta_{enny}\) represents the cumulative damage reward inflicted by our agents on enemy agents, \(delta_{deaths}\) represents the reward for eliminating enemies, and \(delta_{ally}\) represents the reward for our agents being eliminated.
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 5 Proposed Network Deployment
### Second Phase: Service
#### 5.2.2 h-hop Network Deployment  
To account for the limited communication range of UAVs, Equation (8) is modified for one-hop and two-hop neighbors, resulting in Equations (9) and (10), respectively. These equations represent potential fields in the form of \(c\times d^{*}\), where the values of the coefficients and exponents are optimized to generate the desired UAV movements.  
\[\vec{P}_{k}=\sum\limits_{i=0}^{m_{k}}c_{1}\vec{b}_{i}^{\tau_{1}}+\sum\limits _{j=0}^{n_{k}}(c_{2}\vec{u}_{kj}^{\tau_{2}}+\delta_{r}c_{3}\vec{r}_{kj}^{\tau_ {3}})+\delta_{c}c_{4}\vec{s}_{k}^{\epsilon_{4}} \tag{9}\]  
\[\vec{P}_{k} = \sum\limits_{i=0}^{m_  
Here, \(c_{1},c_{2},c_{3},c_{4},c_{5},c_{6}\) are the coefficients, \(e_{1},e_{2},e_{3},e_{4},e_{5},e_{6}\) are the exponents of the potential fields, \(m_{k}\) is the number of users within the \(k\)th UAV sensing range, \(n_{k}\) is the number of one-hop neighbors, and \(n_{k}^{\prime}\) is the number of two-hop neighbors. \(\vec{b}\) is the bandwidth required by the \(i\)th user and points towards the user. \(\vec{u}\) is the bandwidth being served by the \(j\)th one-hop neighbor of \(k\)th UAV and points toward this neighbor. \(\vec{\tau}\) is the vector difference between the positions of UAV \(k\) and UAV \(j\) and points from \(j\) to \(k\). In the last term, \(\vec{s}\) is the vector difference between the command center position and UAV \(k\) pointing from \(k\) to the command center.  
Note that, for \(h=1\) and \(h=2\), the \(k\)th UAV experiences four and six different potential fields, respectively. In general, for \(h\)-hop communication, the \(k\)th UAV will experience \(2(h+1)\) different potential fields. Since each potential field has two tunable parameters \((c,e)\), a total of \(2\times 2(h+1)\) potential field parameters will need to be optimized. Due to the non-linearity of potential fields, optimizing their parameters is difficult and thus a genetic algorithm is used. Coefficients and exponents are encoded in chromosomes, where \(c^{\prime}s\in(-8192,8192)\), \(e^{\prime}s\in(-5.12,5.12)\), and \(d^{*}\) ranges between \(0\leq d^{*}\leq 2^{8}\). Once GA evolved solutions, we compared the deployed network performance using GANet against the performance of the current state-of-the-art ATRI algorithm.
###### Abstract  
This study aims to develop an oxygen regeneration layer sandwiched between multiple porous polyurethane (PU) to improve the performance of implantable glucose sensors. Sensors were prepared by coating electrodes with platinum nanoparticles, Nafion, glucose oxidase and sandwich hierarchically porous membrane with an oxygen supplement function (SHPM-OS). The SHPM-OS consisted of a hierarchically porous structure synthesized by polyethylene glycol and PU and a catalase (Cat) layer that was coated between hierarchical membranes and used to balance the sensitivity and linearity of glucose sensors, as well as reduce the influence of oxygen deficiency during monitoring. Compared with the sensitivity and linearity of traditional non-porous (NO-P) sensors (35.95 nA/mM, 0.9987, respectively) and single porous (SGL-P) sensors (45.3 nA/mM, 0.9610, respectively), the sensitivity and linearity of the SHPM-OS sensor was 98.45 nA/mM and 0.9989, respectively, which was more sensitive with higher linearity. The sensor showed a response speed of five seconds and a relative sensitivity of 90% in the first 10 days and remained 78% on day 20. This sensor coated with SHPM-OS achieved rapid responses to changes of glucose concentration while maintaining high linearity for long monitoring times. Thus, it may reduce the difficulty of back-end hardware module development and assist with effective glucose self-management for people with diabetes.  
glucose sensor; hierarchically porous structure; oxygen regeneration; polyurethane; polyethylene glycol +
Footnote †: journal: Applied Soft. 2020, 10, 2848; doi:10.3390/app10082848
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### Low-Earth-Orbit Satellites  
With the fast-pace deployment of satellites in LEO orbits, the research interest in the ISAC with LEO has also increased in the past few years. Some of the unsolved research questions in this area include how to manage the interference between multiple beams of LEO satellites serving different users, especially in densely populated areas, how to avoid congestion in the frequency bands shared by LEO satellites (e.g., particularly in the S-band, C-band, and millimeter-wave bands) when radar and communication waveforms co-exist in the same frequency bands, and how to increase the power and energy efficiency (as LEO satellites have limited resources) while, at the same time, ensuring fair resource allocation among users. A few of the recent research papers focusing on LEO ISAC are the works in [52] (addressing the problem of optimizing the energy efficiency) and in [53] (proposing a beam-squint-aware ISAC LEO technique).
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 2 Regional Geology and Tectonics
### Stratigraphic Setup of Khyber Area  
The Khyber range exposes Paleozoic sedimentary successions such as the Cambrian Shagai formation, the Ordovician-Silurian Landi Kotal formation (LK), the Silurian-Devonian Ghundai Sar formation (GS), and the Devonian Khyber limestone (K). The Landikotal formation consists of an assemblage of slate, phyllite, and quartzite intruded by basic rocks. This Silurian-Devonian-age formation is structurally highly deformed with frequent repetition of strata. The dominant lithology in the Devonian Shagai formation is massively bedded micritic limestone. The Devonian Ali Masjid formation (AM) consists of sandstone at the lower portion and the presence of siltstone, volcanic ash, quartzite, and conglomeritic beds at the top. According to [45], Khyber limestone is dominantly composed of limestone with very little argillaceous and arenaceous partings. It has a medium- to fine-grained texture, a thick-bedded structure, and some recrystallization. The lower portion of this Carboniferous-Permian-age formation is more likely to include clays and layers of arenaceous limestone. Ghundai Sar formation is a belt of Silurian-Devonian coralline limestone exposed in the hills of Ghundai Sar, near Jamrud, Khyber district. The lithological composition of the Lowera Mena formation (LMC) is dominated by phyllite, phyllitic slates, and interbedded limestone with some quartzite. Quartz veins and granitic intrusion are also common. The Murree formation (M) is dominated by silty shale and sandstone occurring in association with Paleocene limestone. Bazar Limestone (B) is mostly limestone, but not much literature is available on its composition. All these formations are shown in the geological map of the study area in Figure 2.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 1 Introduction
### Research Contribution  
It is no longer a question of whether an organization will be attacked or not. It is rather a question of when and how severe the damage will be. Organizations are therefore forever seeking solutions that can enhance their business continuity in the face of adversarial actions. Timely detection and response are key in today's hostile cyberenvironment. The high volume of alerts is counterproductive to the efforts of security analysts in trying to speedily triage alerts, prioritize them, and execute response actions. Complexity is a security antidote that ought to be avoided by implementing streamlined security operations' processes and systems. This research fills the gap in the current intrusion alert correlation approaches as it aims to address the following issues.  
* Reduce the actual volume of alerts generated by an IDS, by correlating the raw events themselves, rather than correlate alerts that have already been raised. This is significant because it places the task of reducing the volume of alerts within the IDS itself. Further, the IDS will raise higher-quality alerts since it will be analyzing correlated information. Further, the IDS can detect more sophisticated attacks as it does not process and analyze data packet by packet as is the case with the state of the art [32].
* Reduce the time taken to respond to threats, because of the reduced number of alerts that need to be analyzed.
* Enhance the capability of intrusion detection systems by improving their correlation unit using a hierarchy of correlation techniques that takes advantage of their cumulative strengths. The contributions arising out of this research are the following.  
1. An event correlation process: All correlation processes reviewed relate to intrusion alerts. Some of the process steps are not relevant to the correlation of raw events. The process model proposed in this research assembles only those process steps that relate to event correlation.
2. A hierarchical event correlation model: The model concentrates on the correlation component of an intrusion detection system and combines similarity and graph-based techniques in an ensemble. The first level of the hierarchy performs aggregation, data reduction, and clustering so that the events ingested by the graph correlation algorithm are significantly reduced. The graph correlation then performs cluster interconnections and visualization to reveal communication patterns and visual analytics in real time.
3. A clustering algorithm, after considering options for clustering the network event data and their unique characteristics, with a time complexity of \(\mathcal{O}n\).  
The outcome of the research is a streamlined, end-to-end event correlation model that enables real-time attack detection.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Device Discovery
#### 7.1.2 Initial Device Discovery Signal  
This refers to the devices communicating with an initial discovery signal to discover the neighboring devices. However, any proximal device can effectively obtain this signal. The unsuitable scheduling allows the initial discovery of signals information to influence other devices. In certain situations, such as designing parameters, constructing the initial signal for the radio resources is considered a critical job.
## 3 Building the Vitual Geosites
### Drone-Based Image Collection  
Step one focused on image collection, using unmanned aerial vehicles (UAVs), also called drones. We chose two different types of multirotor vehicles: (i) the DJI Spark (UAV1), a 300 gr drone supplied with a small CMOS sensor (1/2.3 inch), suitable for taking pictures with a resolution of 12 megapixels and (ii) the DJI Phantom 4 PRO (UAV2), a 1388 gr drone supplied with a larger CMOS sensor (1 inch), suitable for collecting pictures with a resolution of 20 megapixels. The latter's flight time is approximately 30 min and can be remotely controlled. Both can capture images, comprising the exchangeable image file format (EXIF) information and the location of the pictures reported in geographic coordinates (DATUM WGS84). Both have very stable hovering and a stabilized camera. They can be easily carried in the field and collect pictures flying at a very low height, resulting in a greater ground resolution and proving very useful for VGs reconstruction [71]. In addition, UAV2 is also designed for automatized flight missions, a peculiarity that is especially suitable for modeling large areas (further details in [72]). Generally speaking, the pictures collected using drones or cameras need to have a high overlap ratio, usually greater than 75-80% with the following pictures and must be orthogonally oriented to the target [73].  
Regarding the specific area where we collected the pictures, it is recommended to fly the drones with the straight sun overhead (zenith), so the middle part of the day; it is also helpful to reduce shadows around elevated features. Moreover, it is also suggested to perform this kind of activity during the low-wind season; in Santorini, this corresponds to mid-spring (April-May) and late summer, or the beginning of autumn (September-October). Taking into account logistics, we have adopted different approaches for the different VGs:  
1. For VG1 and VG3, we used UAV1; images were taken by flying the drone at a height lower than 30 m, with the camera in nadir and oblique orientation, respectively. The drone was manually flown, keeping the velocity at 2 m/s, and pictures were taken in equal interval mode (every two seconds). Further details are provided in [73].
2. For VG2, VG4, VG5, VG8, and VG9, we used UAV2; pictures were taken in a range of elevations between 25 and 120 m, with the camera orthogonal to the caldera wall (oblique orientation); the drone was manually flown at a constant velocity of 2 m/s and pictures were captured every two seconds. Further details are provided in [2].
3. For VG6 and VG7, we ran two different surveys. The first one aimed to cover the entire area with an automatized flight mission and the camera in nadir settings. The second mission was devoted to collecting pictures all along the mine's vertical wall, with the camera oriented orthogonally to it (oblique orientation); in the latter case, the drone was manually flown at a constant velocity of 2 m/s and pictures were collected every two seconds. Further details are provided in [71].
# Improvement in Signal Phase Detection Using Deep Learning with Parallel Fully Connected Layers
###### Abstract  
We report a single-shot phase-detection method using deep learning in a holographic data-storage system. The error rate was experimentally confirmed to be reduced by up to three orders of magnitude compared with that in the conventional phase-determination algorithm by learning the light-intensity distribution around a target signal pixel. In addition, the output speed of a signal phase could be shortened by devising a network and arranging the fully connected layers in parallel. In our environment, the phase-output time of a single-pixel classification was approximately 18 times longer than that in our previous method, with the minimum-finding algorithm. However, it could be reduced to 1.7 times or less when 32 pixels were simultaneously classified. Therefore, the proposed method can significantly reduce the error rates and suppress the phase-output time to almost the same level as that in the previous method. Thus, our proposed method can be a promising phase-detection method for realizing a large-density data-storage system.  
holographic data storage; single-shot detection; interpixel crosstalk; deep learning +
Footnote †: _Photonics_  
Footnote †: _  
boundaries between the known phase pixel and signal pixel. So far, we have demonstrated the effectiveness of this method in both simulation and experiment. A sufficiently low phase-detection error rate can be obtained in a four-level phase-modulated signal. However, our recent investigation has revealed that the leakage of light waves from nonadjacent distant pixels affects the phase detection and causes errors. To reduce detection errors, we need to introduce a broader range of pixel information into the phase-determination algorithm. However, building such an algorithm is difficult because of its complexity.  
In the present study, deep learning [20] is introduced to develop a phase-determination algorithm that considers information on distant and adjacent pixels. Deep learning is a method that has achieved excellent results in the field of image classification [21], language translation [22], drug discovery [23], and holographic data storage [24; 25; 26; 27; 28]. Our proposed algorithm utilizes deep learning to determine the phase signal from the intensity pattern. A significant reduction in the phase-detection errors is experimentally demonstrated in an algorithm with deep learning compared with the conventional phase-determination algorithm based on boundary intensity. Furthermore, we significantly improve the computational speed of the phase determination. By devising a network structure, we obtain the signal phase at the same computational speed as the conventional method even with deep learning, which requires a substantial computational cost.