esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of DMLF in Pulse Ranging LiDAR System
## 2 Methods
### DMLF Algorithm Flow Chart  
Through analysis of Sections 2.3 and 2.4, we have known that the DMLF algorithm can effectively improve the accuracy of the pulse-ranging LiDAR. In order to obtain a clearer understanding of the DMLF algorithm, we will introduce the specific process of the algorithm below. Figure 3 shows the main steps of the DMLF algorithm, which are summarized as follows.  
First: Select the sampling points. The slope of each sampling point in the differential echo signal is measured, and the sequence of sampling points between the sampling point with the lowest slope and the sampling point with the highest slope is selected.  
Second: Determine the condition of the selected sampling points. If the peak of the sampling points is included in the sequence selected in the first stage, go to the fourth stage. If not, please go to the third stage.  
Third: Reselect the sampling point. Within the entire sampling points, move a time window, with the width of the time interval between the lowest-slope sampling points and the highest-slope sampling points. Select the sampling points sequence with the largest sum in the window, and then return to the fourth stage.  
Fourth: Apply the linear fitting method. Finally, the stop timing moment of the echo pulse is designated as the zero-crossing point of the fitted curve.  
Linear fitting is the core content of DMLF algorithm, which is related to the accuracy of pulse ranging. Therefore, the specific steps of linear fitting in this article are as follows.  
Step 1: Find the sampling points sequence from the lowest-slope sampling points to the highest slope sampling points, which contain the peak.  
Step 2: Search the sampling point in the sequence of sampling points whose amplitude is closest to zero.  
Step 3: Seven points near the zero-sampling point are selected and a linear curve is fitted.  
Figure 3: Flow chart for DMLF algorithm.  
Step 4: It is assumed that the waveform of selected sampling points can be expressed by the following equation  
\[y_{i}=a+bx_{i} \tag{8}\]  
The sum of the distances between the fitting datapoints and the sampled datapoints can be expressed as  
\[RSS=\sum_{i=1}^{n}\left(y_{i}-y_{j}\right)^{2}=\sum_{i=1}^{n}(y_{i}-(a+bx_{i}))^ {2} \tag{9}\]  
where \(y_{i}\) is the amplitude of the sampling points; a and b are the parameters of the fitting curve; \(x_{i}\) is the time of the sampling points.  
Step 5: Take the derivative of Equation (9), and we will obtain the expression for a and b as follows  
\[\begin{array}{l}b=\frac{\sum_{i=1}^{n}(x_{i}-X)(y_{i}-Y)}{\sum_{i=1}^{n}(x_{ i}-X)^{2}}\\ a=Y-bX\end{array} \tag{10}\]  
where \(X\) and \(Y\) represent the mean value of \(x_{i}\) and \(y_{i}\). Substituting the sampling points data into Equation (10), \(a\) and \(b\) can be obtained; thus, we will obtain the fitting curve.  
Step 6: The stop-time moment is set as the time value when the fitting curve amplitude is zero  
\[t_{r}=-\frac{a}{b} \tag{11}\]
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 4 Dataset and Classification Results  
### Dataset Description  
A public dataset for ship classification is used to evaluate the ship classification performance. The dataset is named the Game of Deep Learning Ship dataset. The dataset mentioned can be found on Kaggle [36]. We utilized this dataset to investigate the proper classification of our proposed method. The collection contained information on five different categories of vessels: 2120 images for cargo ships, 1217 images for tankers ships, 1167 images for military ships, 916 images for carrier ships, and 832 images for cruise ships. This other ship category made it much easier to understand how various neural networks could classify inland ships and how ship classification detection was affected by the network when applying different classification network models as the system's backbone.  
The ship's dataset images were taken from various vantage points, including multiple distances and angles, directions and atmospheric conditions, and international and offshore harbors. The collection contains a total number of 6252 photographs, some of which are RGB shots, some of which are grayscale images, and some of which have varied pixel sizes for each sort of image.  
The total number of examples selected for the classification evaluation is the number of samples contained in this dataset which is greater than 832; this number is appropriate for completing the requirements for both the training and testing of models. The original dataset was partitioned into three groups with a ratio of 70% for training, 15% for testing and 15% for validation. Figure 2 shows a small selection of the images in the dataset. The classes with a sample number of more than 900 are trimmed, the purpose of trimming is to let different classes fairly contribute to the training procedure. Then, augmented images are created by replacing the original data samples.
## 4 Discussion
### Limitations  
This study has several limitations. First, the small sample size may limit the generalizability of the findings. Second, the lack of blinding of participants due to the nature of the interventions could introduce bias. Additionally, the focus on elite athletes may not make the results applicable to recreational athletes or those at different training levels. In our study, we focused mainly on elbow flexors and extensors because these muscle groups are heavily involved during straight punches performed by the players in the experiment, and they are characterized by a significant change in muscle length. In addition to the muscles of the shoulder joints, they also play a key role in performance in these sports and are easier to assess with the available equipment and time constraints. Future studies should aim to include a comprehensive assessment of shoulder muscles to provide a more complete understanding of the recovery processes in these athletes. Incorporating shoulder muscle assessments would allow for a more holistic evaluation of muscle recovery and performance, addressing this significant aspect of the athletic performance in boxing and kickboxing. Although we initially recruited 18 participants, only 14 completed all phases of the study. Four participants were unable to complete the study due to personal reasons or scheduling conflicts that arose during the course of the experiment. As a result, the final analysis was conducted with the data from these 14 participants. While this reduced sample size may impact the statistical power of our findings, the results still provide valuable insights into the effects of vibration therapy and passive rest on muscle recovery
# Glueless Multiple Input Multiple Output Dielectric Resonator Antenna with Improved Isolation
## 4 Conclusions  
In this dissemination, a glueless MIMO DRA is proposed for X-band applications. Measured IBW of antenna is 10.5% (8.05-8.95 GHz) GHz with maximum gain of 6.2 dBi. A novel technique of insertion of copper rods is proposed, which avoids use of glue between the DRA and the substrate. This technique not only provides greater mechanical strength w.r.t. the glued one, but it also avoids radiation losses due to the insertion of a glue coat between the substrate and the DRA, which makes it competent for higher frequency applications. The proposed antenna shows acceptable MIMO performance, justified by parameters such as \(\rho\) and TARC so as to qualify for diverse applications.  
Conceptualization, S.S.S.; methodology, L.M.; investigation, S.S.S., B.K.K. and L.M.; resources, L.M. and B.K.K.; data curation, S.S.S.; writing--original draft preparation, S.S.S.; writing--review and editing, L.M. and B.K.K.; visualization, S.S.S.; supervision, L.M.; project administration, B.K.K.; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No data are available.  
The authors declare no conflict of interest.
# Multiple-Collision Free-Electron Laser Compton Backscattering for a High-Yield Gamma-Ray Source
## 4 Discussion  
### Spectra of Multiple-Collision FEL-Compton Backscattering  
Compared with the experimental data from the two-bunch operation with bunch-fill pattern (#0, #5), the yield of the gamma-ray beam in the three-bunch operation was approximately 3.9 times greater, though the electron-beam current per bunch was approximately 1.1 times greater. This is because the number of collisions per revolution period increased to six in the three-bunch operations. In these calculations using the data in Figure 4, the powers of the FEL pulses with which the electron bunches collided were evaluated in consideration of the electron-beam current of the bunches. The energy spreads were 15% and 13% at the full width at half-maximum for electron bunches #0 and #5, respectively. In order to evaluate the difference between the measurement and the calculated curve, the coefficient of determination \(R^{2}\), which is a statistical measure in regression models that determines the proportion of variance in the dependent variable that can be explained by the independent variable, was calculated. The coefficients of determination calculated for the energy region of 400-1200 keV in Figure 5 were 0.85, 0.91, and 0.89 for electron bunches #0, #5, and #10, respectively. Therefore, the spectra calculated using the FEL-Compton backscattering gamma rays from each collision point were in agreement with the spectra of the measured gamma-ray beams. These experimental results demonstrate that the gamma-ray spectrum generated when one electron bunch collides with multiple FEL pulses can be described as the superposition of the gamma-ray spectra generated at each collision point.
## 6 Space-Segment Optimization Aspects
### Constellation Maintenance
#### 6.6.3 Space Radiation  
One of prominent problem for electronics in space is the ionizing radiation that disturbs or destroys semiconductors. The primary sources of radiation in space are: the Galactic cosmic rays, the solar proton events, and the trapped radiation in Earth's magnetic field. The impact of this radiation on the satellite platform and subsystems can be grouped as follows:  
* _Satellite Charging and Internal Charging (SCIC)_: It is the accumulation of charge on the outer surface of the satellite or on the interior surfaces. This causes potential variations between the spacecraft surfaces and the ambient plasma, resulting in Electro-Static Discharges (ESD)-related anomalies.
* _Single Event Effects (SEE)_: Single event effects are caused by the impact of high-energy charged-particle-sensitive electronics of Sat subsystems.
* _Total Ionizing Dose (TID) and Displacement Damage (DD)_: The total ionizing dose refers to the energy produced through the passage of electrons and protons through materials, resulting in degradation.  
To withstand the radiation environment, the electronic components should be specially designed and qualified for high-radiation environments (radiation hardening), which makes the components very expensive. Lately, the usage of non-hardened components has been growing to meet the price pressure. The radiation in near-Earth space has been concentrated into so-called radiation belts, starting from an altitude around 1500 km to 2000 km. Therefore, based on the operational altitude and region of the SCIC, the satellite designer can estimate the TID to select components and device radiation shielding and protection [135]. [PERSON] et al. surveyed space anomalies from various databases in [136] and concluded that the largest anomalies recorded were due to ESD and charging. The second largest group was SEE, whereas the surface degradation due to radiation damage, especially for solar arrays, formed the third-most group of recorded anomalies. As the radiation level and its type are dependent on orbital parameters, the optimization of Sat electronics depends on orbit design.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 2 Background and Preliminaries
### Intelligent Energy Management Systems  
Intelligent Energy Management Systems (IEMSs) represent a sophisticated integration of IoT sensing technologies, high-frequency metering, and advanced machine learning analytics as depicted in Figure 2. Their primary function is to minimize energy consumption while simultaneously ensuring optimal occupant comfort and maintaining grid stability. IEMSs operate by continuously integrating data streams from a variety of sources, including environmental and occupancy sensors, smart meters, and external weather services. This integrated data is then fed into a supervisory analytics engine. This engine is responsible for forecasting energy demand, detecting anomalous patterns in energy usage, and scheduling equipment operations in real time. Adaptive control signals are subsequently dispatched to local actuators, such as HVAC dampers, variable-speed drives, dimmable luminaires, and storage inverters, thereby closing the loop between prediction and action within mere seconds.  
A transparent framework for automated energy management was presented in [12], which combined ML, expert knowledge, and semantic reasoning to enhance learning and foster trust. IEMSs can incorporate various renewable energy sources to optimize sustainable energy utilization while maintaining grid stability and ensur  
Figure 1: Uses of ML in building energy efficiency.  
To function as a single high-power energy source, the authors in [13] suggested combining energy storage options with solar, wind, and hydroelectric energy sources. IEMSs are becoming increasingly interconnected with building automation systems as the idea of smart buildings develops. This enables buildings to operate more efficiently and effectively control energy usage while maintaining occupant comfort. To reduce environmental effects and achieve sustainability goals, building management must be approached holistically. GreenBuilding, an intelligent system that monitors and automatically adjusts the energy usage of appliances in a building, was deployed by [14] and showed significant energy savings. By permitting two-way communication between energy providers and users, smart grids have further expanded the capabilities of EMS. This has enabled the implementation of demand response, dynamic pricing, and enhanced grid resilience. In [15], the authors described a sophisticated IoT-enabled intelligent energy management system for buildings that improves the interactivity of building energy management systems.
## 3 Proposed Method
### Adaptive Thresholds-Based Decision Making  
To measure the similarity between hashes of original and attacked/tampered images, the metric distance between two hashing code is calculated by:  
\[d(\mathbf{h_{1}},\mathbf{h_{2}})=\|\frac{\mathbf{h_{1}}-\mathbf{h_{2}}}{2 \sqrt{\|\mathbf{h_{1}}\|\|\mathbf{h_{2}}\|}}\|, \tag{26}\]  
where \(\mathbf{h_{1}}\) and \(\mathbf{h_{2}}\) are two image hashes. In general, the more similar the images, the smaller the distance. The greater the difference, the greater the distance.  
Then, the threshold \(T\) is defined to judge whether the image is a similar image or a tampered image.  
\[\left\{\begin{aligned} & Similar\:\:images\:\:pair,&& \quad\text{if }(d\leq T)\\ & Tampered\:\:images\:\:pair,&&\quad\text{if }(d>T) \end{aligned}\right.. \tag{27}\]  
If the distance is less than a given threshold, the two images are judged as visually identical images. Otherwise, they are judged as distinct images.  
Traditional image tamper detection algorithms take a fixed value as the threshold to judge similar images/tampered images. However, due to the different characteristics among images, some images cannot be correctly judged by the fixed threshold value. In our adaptive thresholds algorithm, we firstly find the maximum value for the distance value of the similar images and the minimum value for the distance value of the tampered images. In order to prevent the two values from being too extreme, we set the following limits:  
\[dist_{min}=\max(dist1) s.t.\ (dist_{min}-median(dist1))>\psi, \tag{28}\] \[dist_{max}=\min(dist2) s.t.\ dist_{max}>\xi\]  
where \(dist1\) is the distance between the similar image and the original image; \(dist2\) is the distance between the tampered image and the original image. \(\psi\) and \(\xi\) are two constants set experimentally. Then, the resulting maximum and minimum values are compared with fixed thresholds:  
\[\widetilde{\tau}=\begin{cases}\tau,&\text{if }(dist_{min}<\tau<dist_{max})\\ dist_{max}-(dist_{max}-dist_{min})/3,&\text{if }(dist_{min}<dist_{max}\leq\tau)\\ dist_{min}+(dist_{max}-dist_{min})/3,&\text{if }(\tau\leq dist_{min}<dist_{max})\\ (dist_{min}+dist_{max})/2,&\text{otherwise}\end{cases}, \tag{29}\]  
where \(\tau\) is a fixed threshold obtained experimentally, \(\widetilde{\tau}\) is the adaptive threshold suitable for this image. Then, all images have their own thresholds, which are represented as:  
\[\widetilde{T}=[\widetilde{\tau}_{1},\widetilde{\tau}_{2},...,\widetilde{\tau }_{n}]. \tag{30}\]  
Finally, we put the adaptive threshold at the top of the hash code and transfer it along with the hash code. Thus, the final hash code is represented as:  
\[\widetilde{\mathbf{h_{i}}}=[\widetilde{\tau}_{i},\mathbf{h_{i}}]. \tag{31}\]
###### Abstract  
As optical networks operate using light-based transmission, assigning wavelengths to the paths taken by traffic demands is a key aspect of their design. This paper revisits the wavelength assignment problem in optical backbone networks, focusing on survivability via \(1+1\) Optical Chanel (OCh) protection, which ensures fault tolerance by duplicating data over two disjoint optical paths. The analysis gives great emphasis to studying the influence of topological parameters on wavelength requirements, with algebraic connectivity being identified as the most significant parameter. The results show that, across a set of 27 real-world networks, the wavelength increment factor, defined as the ratio between the number of wavelengths required with protection and without protection, ranges from 1.49 to 3.07, with a mean value of 2.26. Using synthetic data, formulas were derived to estimate this factor from network parameters, resulting in a mean relative error of 12.7% and errors below 15% in 70% of the real-world cases studied.  
optical network; wavelength assignment; optical protection; survivability; topological parameters 2025  
[PERSON], [PERSON] The Role of Topological Parameters in Wavelength Requirements for Survivable Optical Backbone Networks 2025 5 18
## 6 Case Studies in AI for Communication Networks
### Case Study 1: AI in 5G/6G Networks for Managing Connectivity in Dense Urban Environments  
The deployment of 5G and 6G networks in dense urban environments presents significant challenges due to the high density of users and devices, varying traffic demands, and the need for optimal coverage. AI plays a crucial role in managing network traffic, improving bandwidth allocation, and ensuring reliable connectivity for users in these environments [132].  
AI-based systems can predict network traffic patterns, analyze the conditions of different base stations, and dynamically adjust network parameters to ensure that resources are efficiently allocated. Additionally, AI can optimize handovers between cells, manage interference, and predict potential points of congestion before they affect the user experience.  
Figure 10 represents an AI-driven traffic management system in a 5G/6G network for dense urban areas. The key components include base stations, users, and an AI Optimization Center:  
* Base stations (BTSs): Serve as network nodes facilitating communication with user devices (e.g., User 1, User 2).
* AI Optimization Center: Operates as a central entity collecting real-time network data from base stations, performing analysis, and sending back optimization commands.  
* Blue arrows: Represent real-time data feedback loops from base stations to the AI Optimization Center, enabling system analysis and congestion prediction.
* Orange arrows: Show congestion alerts sent from base stations to the AI Optimization Center.
* Red arrows: Depict optimized traffic allocation and bandwidth distribution from base stations to users.  
This setup highlights how AI systems dynamically adjust bandwidth, predict congestion, and balance load to ensure that even in high-density environments, the network maintains seamless connectivity. By prioritizing high-demand services and optimizing resource usage, AI-driven systems significantly enhance network reliability and user experience [132].
## 4 Light Intensity- and Spectrum-Associated Adjustment of Metabolism
### Light Control of Secondary Metabolism
#### 4.6.1 Effect of Light Intensity on Secondary Metabolism
Because of their molecular diversity, in this paragraph, the light- and redox-associated biosynthetic responses of the most investigated alkaloid types, tropane and indole alkaloids, will be summarized, including their accumulation and the regulation of some early biosynthetic genes. However, it should be noted that the alteration of these early genes is not necessarily sufficient to enhance the final alkaloid yield [238]. Light acting through phytochromes has been suggested to modulate key enzymes involved in the metabolic pathways of alkaloids [215]. Indole alkaloid accumulation was reported to be induced by light and ROS, whereas tropane alkaloid production was diminished by light. The indole alkaloid metabolism in _Psychotria leiocarpa_ ([PERSON] & Schltdl.) is linked to the redox system via N,\(\beta\)-D glucopyranosyl uncosamide (GPV), which is an N-glycosated monoterepene indole alkaloid with ROS scavenging activity [239]. The GPV level is controlled by light since its accumulation was enhanced by darkness-to-light transition but diminished after light-to-darkness transfer. In addition, the nicotinic alkaloid synthesis catalysed by putrescine N-methyltransferasease was also light- and ROS-sensitive in tobacco calli [240]. Both light and ROS negatively affected this reaction by downregulating PMT which catalyses the S-adenosyl methionine-dependent N-methylation of putrescine. The inhibition was mitigated by reducing H\({}_{2}\)O\({}_{2}\) levels by CAT or 1,3-dimethyl-2-thiourea (DMTU, also a scavenger of O\({}_{2}\)\({}^{\bullet-}\) and HO\({}^{\bullet}\)) application. Interestingly, DTMU treatment reduced alkaloid content in contrast to CAT or darkness, which might suggest a role of other ROS (O\({}_{2}\)\({}^{\bullet-}\) and/or HO\({}^{\bullet}\)) in alkaloid biosynthesis of tobacco callus [240].