esa-sceva/satcom-chunk-collection huggingface dataset:
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Research and Optimization  
Innovations, research, development and service optimization are the major entities to develop cost effective and provide sustainable services based on the end users or community requirements, demographic and geographic situation, costumes, cultures, and many other aspects of the rural communities. The actual needs of the society have to be analyzed, and suitable technology has to be implemented. Current RTDF utilization to develop a broadband network as initiated by the NTA is expected to uplift the people's economic status, including improvements in several other dimensions of society. For this, we need suitable policies, plans, and strategies so that government services can be effectively provided to the citizens, including promotions of local industries and the creation of an environment for effective use of technologies to develop sustainable rural societies. Hence, broadband data collections from rural communities to effectively analyze the existing services, customer needs, and requirements will help to get back into action after research to optimize the service use and cost of operation.
## 4 Future Upgrades  
The experimental hall of FLASH2 can accommodate up to six beamlines and experimental stations. Since spring 2016, the beamlines FL24 and FL26 have been open for users. FL24 provides an open port for user-supplied experiments and has been equipped with KB (Kirkpatrick-Baez) focusing optics with bendable mirrors in order to adapt focus size and focal length to user demands. At FL26, the permanent end station REMI, a reaction microscope from the Max-Planck Institute for Nuclear Physics in Heidelberg, has been installed for advanced AMO (atomic, molecular, and optical) physics and molecular femtochemistry experiments [15]. As one of the next beamlines, a new time-compensating monochromator will be installed in the FLASH2 experimental hall. The design for this beamline has recently been finalized after intense discussions with the user community. In addition, it is planned to install a THz undulator at FLASH2 and to integrate a THz streaking station based on a single cycle source in the FLASH2 photon diagnostics section for online pulse duration monitoring.  
The FLASH2 FEL control system is not yet completely finished. Regarding the control system, the undulator server, which controls the undulator gaps, phase shifters, and aircoils to correct gap-dependent kicks, now includes the automatic focusing. This means that with one click, one can change the wavelength, keeping the phases, undulator gaps, and focusing at an optimum. However, the undulator length and the starting point for and degree of tapering will also be determined in the near future automatically [16].  
The new opportunities with the variable gap undulators in the FLASH2 line outlined above will significantly enhance the FLASH performance for users in the coming years. In the period from 2018 to 2020 we plan to refurbish two accelerator modules in the linac and to install a variable polarization afterburner in FLASH2. Furthermore, a new flexible injector laser for FLASH will be developed which can provide flexible electron bunch patterns at the full repetition rate for simultaneous operation of FLASH1 and FLASH2. In FLASH2, an X-band-deflecting cavity will be installed behind the undulators for advanced diagnostics of SASE pulses. The electron beam diagnostics will be upgraded with particular emphasis on low-charge operation required for the shortest SASE pulses. DESY is also currently in a preparation phase for a long-term upgrade plan of FLASH known as \"FLASH2020\". A major part of FLASH2020 will be the exchange of the fixed gap undulators in FLASH1 and the implementation of a new flexible undulator scheme aimed at providing coherent radiation for multi-color experiments over a broad wavelength range.  
All authors contributed equally to the work presented in the manuscript
This is the so-called Micromagnetic Multiparameter Microstructure and Stress Analysis (3 MA), which combines several methods [9]. These nondestructive methods for characterizing materials properties are based on physical principle that magnetic behaviour is correlated to macroscopic physical properties of materials [10]. However, the first step of any practical application of nondestructive techniques should be to find a correlation between them and destructively measured, standardized parameters. It means that a rigorous calibration is necessary before any nondestructive technology can be validated. In several of our recent papers [11; 12; 13], the neutron irradiation-generated embrittlement of RPV steels was studied by a novel magnetic nondestructive testing method, called magnetic adaptive testing, MAT [14; 15]. The MAT method is based on minor magnetic hysteresis loops measurement and it has a lot of advantages compared with the traditional magnetic hysteresis measurements. In these works, the destructively determined ductile-to-brittle transition temperature (DBTT) values were compared with the magnetic parameters, measured nondestructively by measuring minor magnetic hysteresis loops on reactor pressure vessel materials irradiated by different neutron doses. At any time, a definite, significant correlation between DBTT and MAT parameters was found. In a very recent paper by us [13], four types of RPV steels (three base metals and one weld material) were studied by MAT and also by other magnetic methods. Beyond the confirmation of the above-mentioned correlation between magnetic parameters and material hardening, a large scatter of points was also observed, but the correlation between MAT descriptors and transition temperature was clearly seen and the obtained correlation could be used for the estimation of the real DBTT values. This correlation is shown in Figure 1. It is evident that the points scatter significantly. This scatter was \"partly\" attributed to the different surface conditions of the investigated specimens [13]. However, it is suspected that the surface condition is not the only reason of this big scatter: different material properties of measured samples can also play an important role. The purpose of the present work is to analyze this other possible reason for this scatter. A series of measurements were carried out on another type of RPV steel (15 Kh2 NMFA), before and after neutron irradiation, and the result of the MAT measurements were compared with the destructively determined transition temperature. Standard Charpy specimens were used for investigation. For checking the conclusion of the above-mentioned previous work, a special care was made to produce specimens with exactly the same quality of surface to reduce the possible influence of the surface roughness on the measured magnetic quantities. A detailed analysis of the error of MAT descriptors is also presented in the Appendix A.
## 2 System Requirement
### Design of Printed Circuit Board  
The Eagle software version 7.7.0 design tool was used to create the schematic design and a printed circuit board. After validating the simulation and breadboard prototyping, the schematic design was transferred to a PCB prototype, as shown in Figure 4. To keep the schematic design as small as possible, surface mount components are used. The diameter of this PCB is 45 mm. The prototype was optimized and designed on FR4 material, which has a dielectric constant of 4.2, a loss tangent of 0.02 S/m, and a thickness of 1.6 mm. The developed wireless sensor module was then properly programmed for RSSI at the ISM band at 2.45 GHz using the IEEE 802 b/g/n standard and incorporated with a BSA via a T-shaped matching circuit, as shown in Figure 5. ESP8266 is a WiFi-enabled microcontroller integrated circuit. The wireless modules are widely utilized to control devices via the internet. The ESP8266 can be programmed using the Arduino IDE3 in C++, the firmware Espruino4 in JavaScript, and the Espressif SDK in C. For Internet of Things (IoT) applications, ESP8266 has become a popular and affordable solution. The 26 MHz Crystal Oscillator, 4 MB Flash memory, USB to FTDI connector, stepdown regulator (3.3 V), power supply, and 2.4 GHz RF transceiver are all included in the ESP8266 EX wireless module (Espressif Systems, Shanghai, China).  
Figure 3: Structure and dimensions of the BSA (dimensions in the given mm): (**a**) the side view; (**b**) the top view; and (**c**) the fabricated BSA.
# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 2 Background
### Smart Home Orchestration  
One of the primary challenges in realising the full potential of smart home systems lies in orchestrating multiple Internet of Things (IoT) devices, protocols, and services so that they behave collectively as a cohesive and adaptive environment. While simple rule-based methods--such as scheduling lights based on the time of day or triggering alarms under predefined conditions--were sufficient for early smart home deployments, contemporary environments often demand more nuanced coordination [4]. Orchestration, in this sense, involves the continuous and dynamic management of device interactions, event triggers, and resource allocation, aiming to achieve goals such as energy savings, personalised user experiences, and seamless interoperability with minimal direct supervision [5].  
Research in this area has progressively moved beyond static or semi-static policies towards more sophisticated paradigms that offer higher degrees of flexibility and autonomy. Agent-based systems, for instance, distribute decision-making across multiple agents capable of learning user preferences and negotiating shared resources [6; 7]. Semantic web technologies leverage ontologies to formally represent device capabilities and relationships, enabling reasoning engines to compose services on the fly [8]. Other approaches explore context-aware frameworks in which situational data (e.g., location, weather, or user activity) informs real-time adjustments to device behaviour. Despite these advances, many existing solutions still rely on extensive manual configuration or are unable to accommodate rapid changes without significant downtime or reprogramming.  
The next generation of orchestration mechanisms aims to address these gaps through adaptive, AI-driven methods capable of dynamically generating and updating logic in response to changing conditions. Such an evolution holds the promise of not only simplifying system management for end-users but also improving the overall responsiveness and resilience of smart home ecosystems. In the subsequent subsections, we explore how generative AI techniques, combined with retrieval strategies and robust modular frameworks, can pave the way for truly dynamic orchestration solutions that surpass the limitations of traditional approaches.
# Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions +
## 2 Results
### Output Time-Frequency Power Distribution  
We will assume the matched-filter output is the correlation of \(N\) pulses against an infinite pulse train input, represented by the periodic ambiguity function (PAF) \(|\chi_{NT}|\), given by [55]:  
\[|\chi_{NT}(\tau,\rho)|=\left|\frac{1}{NT_{r}}\int_{0}^{NT_{r}}x(t)x^{\ast}(t+ \tau)e^{\abs{2\pi\rho t}}dt\right|, \tag{31}\]  
where \(x(t)\) is the normalized unit-energy input pulse train and \(T_{r}\) is the pulse repetition interval (PRI). The pulse repetition frequency (PRF) \(F_{r}=1/T_{r}\). The PAF has the following properties that are relevant to our application:  
\[|\chi_{NT}(\tau,\rho)|=|\chi_{NT}(\tau+T_{r},\rho)|, \tag{32}\]  
and:  
\[|\chi_{NT}(\tau,\rho)|=|\chi_{1T}(\tau,\rho)|\left|\frac{\sin N\pi\rho T_{r}}{ N\sin\pi\rho T_{r}}\right|. \tag{33}\]  
One important result from [PERSON]'s work [56] was that the time-frequency power distribution is proportional to the convolution of the ambiguity function with the WSSUS system scattering function:  
\[\begin{split} P(\tau,\rho)&=E_{x}S_{h}(\tau,\rho )\ast\ast|\chi_{NT}(\tau,\rho)|^{2}\\ &=E_{x}\int\int S_{h}(\tau^{\prime},\rho^{\prime})|\chi_{NT}(\tau -\tau^{\prime},\rho-\rho^{\prime})|^{2}d\tau^{\prime}d\rho^{\prime},\end{split} \tag{34}\]  
where \(E_{x}\) is the energy of the pulse train. Note that since \(|\chi_{NT}|^{2}\) is periodic along the \(\tau\) axis, the result of the convolution integral of Equation (34) is periodic along this axis as well. If we assume the  
* The mean wave speed (and hence mean Doppler shift) is proportional to the wind speed. For example, it is commonly assumed that the wave speed is \(1/8\)th the wind speed [57], and
* The wind velocity vector has no \(Z\) component, and
* The waves move in the same direction as the wind,  
then the following mathematical statements can then be constructed to express this testable information:  
* \(\mu_{\rho}\propto v_{w}\cos\theta\cos(\phi-\phi_{w})\),
* \(\rho\operatorname{sgn}(\mu_{\rho})\in[0,\infty)\).  
There are cases when these statements are not true, such as when there is a sudden change of wind direction, but for a fully developed wind-wave these are not particularly controversial statements [49]. The MaxEnt prior that satisfies these criteria can be shown to be:  
\[\underline{b}(\rho|\tau)=\frac{1}{|\mu_{\rho}(\tau)|}\exp\left(-\frac{\rho}{ \mu_{\rho}(\tau)}\right)u\left(\rho\operatorname{sgn}\left(\mu_{\rho}(\tau) \right)\right), \tag{39}\]  
where \(u(\cdot)\) is the Heaviside step function. This is an exponential distribution with respect to the wind direction. The intuition behind this is that, while the mean Doppler shift will be proportional to the wind speed, there will be some wave components that move much faster, but none that move in the opposite direction of the wind.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### Low Frequency (LF) Section  
The LF section uses a linearly Positive Temperature Coefficient (PTC) deviated oscillator working as temperature sensor and a chain of digital inverters that operate as a voltage buffer (red-dashed line box in Figure 1). The PTC oscillator uses a Proportional To Absolute Temperature (PTAT) \(I_{P}\) current to ensure and enhance its positive dependence on temperature and delivers a \(v_{osc}\) voltage square wave that is fed into a voltage buffer that drives the ASK backscatter modulator embedded in the HF section.
# Towards Resolving Challenges Associated with Climate Change Modelling in Africa
## 3 Materials and Methods  
### Data Collection: Review of Climate-Related Publications from 2011-2020  
According to [PERSON] and [PERSON] [37], Google Scholar provides greater coverage for cross-disciplinary research outputs, although they note that the user interface may not be suitable for bibliometric analyses. However, the inclusion of exclusionary notations and limiting search results by year can produce refined outputs. Thus, a systematic, year-by-year comprehensive literature survey was conducted for the period 2011-2020. Search queries were applied to publications, by year, relating to climate change modeling in Africa that mentioned encountering a \"limitation\" or \"challenge\", irrespective of discipline. The article selection criteria were as follows: \"research articles on climate change modeling in Africa\", \"research articles published between year 2011 and 2020\", the keyword \"climate change\", the keyword \"modeling\", the keyword \"Africa\", the keyword \"challenges\", and the keyword \"limitations\". Only peer-reviewed, original research publications based on a region within the African continent were considered, excluding conference proceedings, review articles, global perspective articles, working papers, and university theses. In all, a total of about 10,000 articles were reviewed, based on the selection criteria detailed above. (See Figure 1 for articles reviewed from the Google Scholar Database). It must be noted that this literature survey was undertaken to understand the challenges and limitations of climate research over the African continent and not as a simple systematic review of works published during the year under investigation. Although additional portals, such as Web of Science, could also have been utilized, the survey undertaken here is acceptably representative of the issues faced by researchers working in Africa.
## 3 HGV Tracking with Multiple Model Algorithms
### IMM Tracking Algorithm
#### 3.2.2 Grid Distances Readjustment  
In this paper, we divide the grid distances readjustment situation into three cases no-jump, up-jump and down-jump.  
Case 1.No-jump: \(\omega_{k}^{2}\) is the biggest in \(\omega_{k}^{1}\), \(\omega_{k}^{2}\) and \(\omega_{k}^{3}\).  
\[\alpha_{1,k+1}=\left\{\begin{array}{l}\alpha_{2,k+1}-0.5\lambda_{d,k}, \omega_{k}^{1}<t_{1}\\ \alpha_{2,k+1}-\lambda_{d,k},\text{otherwise}\end{array}\right. \tag{29}\]  
\[\alpha_{3,k+1}=\left\{\begin{array}{l}\alpha_{2,k+1}+0.5\lambda_{u,k}, \omega_{k}^{3}<t_{1}\\ \alpha_{2,k+1}+\lambda_{u,k},\text{otherwise}\end{array}\right. \tag{30}\]where \(\lambda_{d,k}=\max\left\{\alpha_{2,k}-\alpha_{1,k},\xi\right\}\); \(\lambda_{u,k}=\max\left\{\alpha_{3,k}-\alpha_{2,k},\xi\right\}\); \(t_{1}=0.35\) is a threshold for detecting an unlikely model; and \(\xi=1\) is a model separation distance (design parameters).
2. Up-jump: \(\omega_{k}^{3}\) is the biggest in \(\omega_{k}^{1}\), \(\omega_{k}^{2}\) and \(\omega_{k}^{3}\). \[\alpha_{1,k+1}=\alpha_{2,k+1}-\lambda_{d,k}\] (31) \[\alpha_{3,k+1}=\left\{\begin{array}{l}\alpha_{2,k+1}+2\lambda_{u,k},\omega_ {k}^{3}>t_{2}\\ \alpha_{2,k+1}+\lambda_{u,k},\text{otherwise}\end{array}\right.\] (32) where \(t_{2}=0.65\) is a threshold for detecting the significant mode.
3. Down-jump: \(\omega_{k}^{1}\) is the biggest in \(\omega_{k}^{1}\), \(\omega_{k}^{2}\) and \(\omega_{k}^{3}\) \[\alpha_{3,k+1}=\alpha_{2,k+1}+\lambda_{u,k}\] (33) \[\alpha_{1,k+1}=\left\{\begin{array}{l}\alpha_{2,k+1}-2\lambda_{d,k},\omega_ {k}^{1}>t_{2}\\ \alpha_{2,k+1}-\lambda_{d,k},\text{otherwise}\end{array}\right.\] (34)  
Thus, the AGIMM algorithm for HGV tracking is defined. Through the adaptive grid method the attack angle \(\alpha_{1,k}\), \(\alpha_{2,k}\) and \(\alpha_{3,k}\) are closer to the real unknown attack angle which is good to improve the tracking accuracy.
###### Abstract
These combined effects contribute to robust and efficient communication systems. Considering sustainable development in areas like the economy, the environment, and health, achieving green communication has become a key focus in designing and operating current and future wireless communication networks [10]. Non-Orthogonal Multiple Access (NOMA) is widely adopted in 5G applications like massive machine-type communication (mMTC). However, Enhanced Mobile Broadband (eMBB)--which relies on orthogonal frequency division multiple access (OFDMA)--will remain a dominant use case in the near future. Therefore, research on overlay spectrum sharing is still of substantial significance. This paper investigates spectrum allocation and power control in overlay spectrum sharing to align with green communication.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.1 Austria  
The Austrian C-Roads-Pilot builds on the core elements of the EU C-ITS Corridor project in Austria (ECo-AT-European Corridor Austrian Testbed) [80] and extends them to a motorway-based network. The services investigated are basically Day-1 applications and particular attention is paid to the ITS-G5 communication standard. The leading competent body is the Austrian motorway operator ASFINAG, which will cover a total of 300 km of motorways and will start cross-site tests during 2019. Finally, it is worth noting that Austria is one of the Member States involved in the European project C-ITS Corridor [81], together with Germany and The Netherlands.
## 1 Introduction
Most dual image-based methods convert secret data from the binary system to decimal or other numeric bases to hide the secret data and add or subtract them to obtain stego pixel values. If the secret data value is too large, the difference between the values of stego image and cover image pixels is also high. Therefore, it is obtained low image quality, and it can be understood by third parties that there is secret data in the stego image. We propose a new dual image based reversible data hiding method using most significant bits and center shifting techniques to avoid this problem in this paper. The temporary values are obtained by calculating the difference between the most significant bit values of the relevant pixels and the numerical values of the secret data to reduce the numerical values of the secret data. The most significant bits are shifted by the average value of the change interval to minimize the effect of this operation on the character change interval. Thus, the mean square error values are minimized in theory. The contributions of the proposed data hiding method are listed:  
* This paper proposes a new reversible data hiding method based on dual images and center shifting technique for image steganography. In the proposed technique, data hiding and extracting processes can be performed completely lossless. * The proposed technique ensures a resolution for the fall of the boundary problem. * Even if the two stego images are obtained from third parties, the secret data cannot be obtained unless the original cover image can be created. * The reversible data hiding algorithm is a higher payload than the similar existing algorithms. * The proposed technique is resistant to the RS analysis technique according to the experimental results. The remainder of our article is organized as follows. In Section 2, related works are presented in detail, which contains the dual image based reversible data hiding technique and center folding strategy algorithm. The proposed method, dual image and center shifting technique for image steganography, is represented in Section 3. The experimental results and comparisons to evaluate the proposed algorithm is given in Section 4. Finally, the conclusion is presented in Section 5.
# Establishment of a Standard Method for Boundary Slip Measurement on Smooth Surfaces Based on AFM
## 3 Standard Process of Measuring Boundary Slip  
### Experimental Process  
#### 3.1.1 Sample and Condition  
Deionized water made by Milli-QA10 system, a polished wafer covered by silicon dioxide (direction 1-0-0), borosilicate glass spheres (BSGMS, Cospheric LLC, Santa Barbara, USA), AFM probe (ORC-8, SNL, Bruker, Santa Barbara, USA), AFM (Dimension 3000, Bruker, Santa Barbara, USA), epoxy resin (Ergo 7200, Kisling, Zurich, Switzerland) and drop volume meter (MAIST, Vision, Beijing, China) were used in the experiment. The nominal spring constant of ORC8-10 was 0.1 N/m, the resonant frequency was 19 kHz, typical width and length were 40 \(\upmu\)m and 200 \(\upmu\)m, and the rectangular cantilever was coated by reflective gold. Although there were tips (around 3\(\upmu\)m high) on the cantilever, they were covered when the spheres were stuck to the end of cantilever.  
During the experiments, to realize the measurement of liquid environments, a colloidal probe was installed in a poly vinyl chloride liquid holder (DTFML-DD, Bruker, Santa Barbara, USA). In the holder, there were piezoelectric ceramics components, which drove the colloid probe movement in the liquid environment. Before measurement, a certain volume of liquid was dropped in between the liquid holder and the sample. The liquid formed a half-moon-shaped droplet. The liquid holder made volume of droplet greater as well as prevented pollution caused by evaporation.  
Experiments were operated on the hydrophilic silica surface. First, the silicon wafers were placed into configured Piranha solution (H\({}_{2}\)O\({}_{2}\) and H\({}_{2}\)SO\({}_{4}\) 3:1 mix) for about 30 min, and then a large amount of ionic solution was used to clean the wafer. Next, acetone and ethanol were used for ultrasonic cleaning of silicon wafers for at least 5 min each, which were then dried with clean compressed air.