esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
On the other hand, the DFG approach can be exploited to transfer the spectral purity and wide mode-hop-free tunability characteristics of the currently available telecom laser systems to the THz range. Generally, the THz frequency range, achievable with a DFG setup, coincides with the phase-matching (PM) bandwidth of the process, which, in accordance with [PERSON]'s uncertainty principle, is limited by the finite interaction area within the nonlinear material [30]. Several different PM geometries have so far been adopted, including tilted-front-pulse generation [31], collinear and non-collinear quasi-PM in periodically-poled LN crystals [29], and Cherenkov generation [32; 33]. The intensity-dependent self-beam-defocusing effects in LN [34; 35], incompatible with the achievement of an adequate PM, have hampered the possibility to produce broadly-tunable and spectrally pure CW THz radiation by means of bulk nonlinear crystals. Conversely, by exploiting the confining properties of an MgO-doped LN waveguide, we recently realized an ultra-narrow-linewidth THz spectrometer capable of absolute frequency measurements of molecular transition in the whole 1-7.5 THz range [36]. The current accuracy (\(\sim\)10\({}^{-9}\)), achieved in a direct-absorption configuration, was limited by the Doppler linewidth of the measured absorption profiles. However, improvement of the order of magnitude is possible by implementing more advanced setups [26; 37]. To this purpose, higher THz power levels, in the order of a few uW and not yet available with our source, are desirable. The goal of this paper is to clarify the centrality of the guided-wave approach in overcoming the main difficulties of CW THz-DFG, and, at the same time, to identify a suitable strategy in order to overcome the existing criticalities, as well as to obtain, if possible, the desired power levels. To this purpose, here we provide a comprehensive characterization of the source described in [36], and we discuss its spectral coverage and efficiency on the basis of the three-coupled-wave theory.
## 2 Background and Related Work
### Approaches for Detecting Program Similarity
#### 2.4.2 Cosine  
In data science, the cosine similarity calculates the similarity between two vectors. It can be applied to calculate the similarity between two programs with tokenisation. The cosine similarity approach for program similarity calculation can be outlined through the following steps:  
* Preprocessing and tokenisation: During this phase, we eliminate extraneous structural elements and tokenise the source code.
* Construct frequency vector: We compute the token frequencies and store them in a vector representing term frequencies.
* Similarity score calculation: As shown in Equation (1), we calculate the similarity score between two source programs by applying the cosine similarity function between two frequency vectors (denoted as vectors **A** and **B**) from the previous step. \[\cos(\textbf{A},\textbf{B})=\frac{\textbf{A}\cdot\textbf{B}}{\|\textbf{A}\| \|\textbf{B}\|}=\frac{\sum_{i=1}^{n}\textbf{A}_{i}\textbf{B}_{i}}{\sqrt{\sum_ {i=1}^{n}{(\textbf{A}_{i})^{2}}}\sqrt{\sum_{i=1}^{n}{(\textbf{B}_{i})^{2}}}}\] (1)
###### Abstract  
As the discussions on sixth-generation (6G) wireless networks progress at a rapid pace, various approaches have emerged over the last few years regarding new architectural concepts that can support the 6G vision. Therefore, the goal of this work is to highlight the most important technological efforts in relation to the definition of a 6G architectural concept. To this end, the primary challenges are first described, which can be viewed as the driving forces for the 6G architectural standardization. Afterwards, novel technological approaches are discussed to support the 6G concept, such as the introduction of artificial intelligence and machine learning for resource optimization and threat mitigation, cell-free deployments, and novel physical layer techniques to leverage high data rates. In the same context, open-access protocols for flexible resource integration, security, and privacy protection in the 6G era, as well as the digital twin concept, are discussed as well. Finally, recent research efforts are analyzed, with an emphasis on the combination of the aforementioned aspects towards a unified 6G architectural approach. To this end, limitations and open issues are highlighted as well.  
6G; resource management; O-RAN; artificial intelligence; machine learning; threat mitigation; organic 6G networks; massive MIMO systems; cell-free networks; security and privacy
# Optimization of Sensor Placement for Modal Testing Using Machine Learning
## 4 Application of the Random Forest Sensor Selection Approach to a One-Dimensional Structure
### Densely Meshed Cantilever Beam (Case 1)
Each column in the charts represents a mode, and the rows display the different global parameter choices (a: raw ODS, b: normalized ODS, c: average FRF). Sensor locations do not vary between modes but are plotted on top of the different mode shapes to visually evaluate the ability of the sensors to measure a given mode shape. Figure 6: Variance as a function of the number of features for case 1. Figure 7: Sensor locations along the beam for case 1. All three sensor sets obtained using the different choices of output parameters can predict the first four modes with relative accuracy. Starting at mode 4, mode peak clipping can be noted with all global parameter options. As the mode number increases (Figure 9) the predictions made using the machine learning modeling fail to capture the behavior of the first third of the beam, as all methods weight the free end of the beam more heavily. The method is able to capture the number  
Figure 8: Modes 1–4, as predicted using the first 10 features, case 1. (**a**): Raw ODS, (**b**): normalized ODS, (**c**): average FRF. Figure 9: Modes 6–7, as predicted using the first 10 features, case 1. (**a**): Raw ODS, (**b**): normalized ODS, (**c**): average FRF. of nodes for each mode and does not exhibit large aliasing errors in the representation of mode shapes. At mode 7 and above, the aliasing of the modes along the length of the beam starts to become apparent, which is expected with only ten sensor placements on the structure.
## 6 Protocol Verification Logic Analysis
### Message Exchange  
The message exchange between the client and server can be given as:  
\(A\)\(\rightarrow\)\(S\): \(A\), \(K_{a}\)  
\(S\)\(\rightarrow\)\(A\): \(K_{b}\)  
\(A\)\(\rightarrow\)\(B\): \(\{K_{ab}\}_{K_{b}}\)  
\(B\)\(\rightarrow\)\(A\) : \(\{K_{ba},V_{b},\lambda_{c},K_{ab}^{\prime}\}\)\(T_{l},Mode\}_{K_{a}}\)  
\(A\)\(\rightarrow\)\(B\) : \(\{K_{ab},V_{a},\lambda_{a},P_{a}\}_{K_{ab}^{\prime}}\)  
\(B\)\(\rightarrow\)\(A\) : \(\{K_{ba},V_{b+1},V_{f}\}_{K_{ab}^{\prime}}\)  
\(K_{ba}\) and \(K_{ab}\) are shared session keys. \(K_{ab}^{\prime}\) are temporal session keys used in the subsequent communication for cryptography purposes. In the first message, the public key of client A is sent, which is verified and responded to by server S with a public key of fog server B. Later, B sends a challenge in the form of a temporal session key for the subsequent process. The challenge-response achievement then marks the successful completion of the protocol.
## 5 Discussion
### SANAE: Phase Fluctuations without Amplitude Scintillations
The presence of refractive effects at L-band may be due to the combination of such patches with the observed precipitation, which is the main source of localized and strong E-field with magnitudes significant enough to drive instabilities. The combination of observations from diverse instruments suggests that, close to the boundaries of the auroral electrojets (Figure 6) or along the edge of the auroral oval (Figure 8), it is possible to suppose the existence of irregularities of the order of the first Fresnel L-band. Indeed, when the GNSS receiver recorded phase fluctuations without amplitude scintillations SuperDARN recorded echoes back from the ionosphere (not shown) near/inside the receiver's field of view, confirming the presence of irregularities with a spatial scale of the order of tens of meters [47]. According to the results described in the previous section, we would have expected to also observe amplitude scintillations in these regions, triggered by the irregularities of scale sizes up to the first Fresnel radius, including those revealed by SuperDARN. Our results suggest that the lack of amplitude scintillation can be ascribed to the recording of TEC values that are lower during phase fluctuations (\(<\)12 TECu, Figure 2a) than during both amplitude and phase scintillations (\(>\)20 TECu, Figure 2a). In fact, low TEC values would imply that the energy associated with the electric field gradient corresponding to the observed electron density irregularities was not sufficient to trigger amplitude scintillations. Since the electric field gradients depends on both TEC gradients and magnetic field variations, we suggest that electric field variations may cause amplitude scintillations. Indeed, the amplitude of a signal carries the information about the energy of the system in which it flows through. So, if the signal crosses a region in which an intense energy variation exists, the signal's statistical parameters (i.e., autocorrelation) and, hence, its amplitude will be modified proportional to the intensity of the crossed energy variation [83]. Such link between amplitude scintillations and electric field gradients is similar to the driving mechanism behind the low-latitude occurrence of \(S_{4}>0.1\)[84; 85]. Figure 8: Polar-view maps in AACM latitude and MLT of the austral auroral radiance as measured by SSUSI instrument in the LBHL band at 01:10 UT (**a**), 18:07 UT (**b**) and 20:46 UT (**c**) on 8 September 2017. Each map also reports the auroral oval boundaries location (red dashed contours), as retrieved from the auroral radiance data, the \(\sigma_{\varphi}\) greater than 0.25 rad (magenta diamonds), recorded at SANAE IV base during the DMSP passage, and all the \(\sigma_{\varphi}\) values having elevation angle \(\alpha=30^{\circ}\) (purple circles). GNSS data were projected to 350 km altitude. Each map covers 00:00–24:00 MLT and \(\,150^{\circ}\,|-190^{\circ}\,|\) AACGM Lat, the magnetic noon/midnight is at the top/bottom.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 7 Applications and Contributions
### Expanding Renewable Forecasting  
Accurate and timely predictions are critical for real-time energy management for smart and zero-energy cities, where balancing supply and demand, integrating renewable energy into the grid, and reducing reliance on fossil fuels are paramount. The enhanced accuracy of WSPFS offers the potential to improve energy forecasting reliability, thereby supporting more informed decision-making in grid management, energy trading, and policy development. Furthermore, the system's efficiency in terms of computational time and memory usage positions it as a viable solution for large-scale, real-time applications in renewable energy, setting new benchmarks for predictive accuracy and operational efficiency.  
The impact of this paper extends beyond wind energy, with potential applications across other renewable energy sources. The integrated prediction framework developed for wind speed and power can be adapted to address prediction challenges in other sectors, such as hydropower or solar energy. For example, the hybrid ML and DL models employed for wind power forecasting, based on wind speed, could be repurposed for solar energy forecasting by incorporating variables such as solar irradiance and temperature. Similarly, in hydropower, essential predictors such as water flow and reservoir levels can be incorporated into ensemble algorithms, enhancing prediction accuracy, and improving energy management across multiple renewable sources. Moreover, WSPFS can be adapted to various geographic regions, increasing the reliability of energy supply forecasts globally. Its flexibility to account for local environmental conditions makes it a valuable tool for optimizing energy management across diverse locations, ultimately improving the precision of renewable energy forecasts worldwide.
# A Cumulant-Based Method for Acquiring GNSS Signals
## 4 Simulation and Experimental Results  
In this section, we will show the acquisition results for GPS and Galileo signals using both the traditional method and the proposed method. For the traditional method, we refer to the usual parallel code-phase search algorithm. See [14] for more details.
## 3 Results  
### Validation of FE Model  
The force-displacement curve of the intact tibia model obtained from mechanical testing and finite element simulation results under different loads are plotted in Figure 3b,  
Figure 3: (**a**) Experimental equipment for mechanical testing coupled with 3D-DIC of the pig tibia model; (**b**) force–displacement curve of mechanical testing and FEA data of pig tibia model; tibial displacement field distribution by FEA simulation and DIC testing under different physiological activities: (**c**) standing; (**d**) walking; (**e**) jogging; (**f**) running.  
revealing a non-linear mechanical response of the tibia, with a displacement of 0.936 mm at 1500 N loading. Figure 3c-f present the displacement field contour plots from finite element simulations under simulated physiological loading conditions of standing (375 N), walking (750 N), jogging (1000 N), and sprinting (1500 N), along with the corresponding displacement field contour plots captured using the DIC device. The simulated displacement patterns (denoted by black dashed lines) exhibit high consistency with DIC-measured actual displacement distributions, both demonstrating cumulative displacement from distal to proximal regions, with maximum displacement occurring at the proximal tibia and progressively decreasing towards the distal end. The finite element simulation yielded a maximum displacement of 0.925 mm under 1500 N loading, showing a 1.18% error compared to experimental measurements.  
Finite element simulation results of stress and strain distributions in the intact tibia under different physiological activities are presented in Figures 4a-d and 4e-h, respectively, showing both surface and cross-sectional simulation results of the tibial model. The simulation results reveal that under physiological loading, stress was predominantly concentrated in the cortical bone region subjacent to the medial tibial plateau, while strain was observed in both cortical and cancellous bone regions of the medial tibial plateau subjacent area. The maximum stress and strain values from the simulations are statistically summarized in Figure 4i.j. As the simulated physiological loads increased, corresponding to different activity intensities, both peak stress and strain values in the tibia demonstrated progressive elevation. From standing (lowest load) to running (highest load), the maximum stress in the intact tibia increased from 3.94 MPa to 15.76 MPa, while the maximum strain rose from 0.017% to 0.067%.
# Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model
## 3 Experiments and Discussion
### Parameter Setting and Analysis  
Since the FFT-Informer model is a combination of FFT and the improved Informer model, the main parameter settings of the FFT-Informer model were analyzed in two steps, as shown in Table 1. First, the parameter settings of feature extraction were analyzed by FFT. In order to ensure the flexibility of the experiments and verify the effectiveness of FFT, window lengths and sliding distances were fixed, which can be adjusted. For the SMC data set, the window length was set to 100 and the sliding distance to 100. However, due to the frequent vibration amplitude of the ASCE data set, it was difficult to fit the function with a long feature window, so the window length in the ASCE data set was 50.  
Secondly, the parameters of the Informer model were analyzed. In this model, the attention window was used to capture the relationship between feature vectors in the window and long-term dependence. The training window length, which can also be adjusted, should be set beyond three times the forecasting length to ensure the correlation between the forecast and historical data. The training window length was initially set to 96 by trial and error, and the value of the predicted length was adjusted to verify the performance of long-term prediction in a one-forward operation. It is worth noting that feature vectors are predicted by the Informer model and can be transformed into prediction values by Equation (3), which greatly extends the prediction length as shown in the last line of Table 1. In addition, the number of Multi-heads was set to 8, and the dimensions of \(q_{i},k_{i},v_{i}\) were set to 512, which achieved the best results [24]. When the encoder-decoder layers are two, the model runs faster and the prediction effect is better.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC2: Urban Environment
#### 5.2.4 Urban Link Count  
The \(p_{90}\) link count for both lamp post cases is nine hops. It is similar to the rural scenario, showing that the hop count does not depend on the environment. The hop count is only slightly affected by vegetation, rising to 5%, while the rain raises it by 10%.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 2 The Principles of MALDI-TOF MS
### Laser Desorption/Ionization  
Upon drying, the co-crystallized matrix and sample are irradiated with a pulsed ultraviolet laser, typically a nitrogen laser emitting at 337 nm. The matrix absorbs the energy from the laser pulse and undergoes rapid sublimation, carrying the analyte molecules into the gas phase. During this process, the matrix also facilitates ionization--primarily via proton transfer mechanisms--resulting in the formation of mainly singly charged ions of microbial proteins. This soft ionization ensures minimal fragmentation, preserving the integrity of the protein ions for mass analysis [37; 47; 48; 49].
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 2 State of the Art
### Related Works  
A considerable number of papers on the topic of PLC simulations have been published. In this paper, related works are described in five parts. The first part deals with simulations in general. These are mainly comparison studies of network simulators with focus on routing protocols. These works serve as the main motivation for using the NS-3 tool. The current situation of simulation software tools, their usability, usefulness, advantages, and disadvantages are covered. The second and third parts contain works dealing with NPLC and BPLC simulations for smart grid and smart metering systems with the aim of finding relevant simulations of standardized PLC systems. The fourth part contains real measurements of PLC under real conditions with corresponding simulations. This article contributes in this regard with Section 7, where simulations of a real scenario for a smart grid system are provided. Analysis in this part of related works is focused on simulation of standardized systems which correspond with real PLC systems required in the field of smart grid and smart metering. The fifth section on related works analyses several measurements of PLC technology used in a smart grid application. The following list summarizes the points targeted in the analysis:  
Section 2.3.1 Simulation in general:  
* The need for simulations of communication technologies in general.
* Available simulation tools, their sustainability and potential use.
* The need for further development of simulation tools for PLC technologies.
* Usability of PLC simulations in the field of smart grid and smart metering.  
Section 2.3.2 Simulations of BPLC systems:  
* Works focusing on simulation models of PLC systems.
* Complex and partial simulations of PLC systems.
* Overview of simulation results in available literature.
* Discussion of the benefits of some works.  
Section 2.3.3 Comparison of real measurement and corresponding simulations:  
* Simulation in comparison with measurement results of the real topology of a PLC system.
* Simulated infrastructure of smart grid and smart metering architecture.  
Section 2.3.4 Measurements of real PLC systems:  
* Available measurement results of PLC systems as possible inputs for verification of simulation results.
* Methods for and results of measuring PLC systems under real and laboratory conditions.  
Each sub-section above contains one or more tables with clear lists of references according to the key content key of the paper. These tables summarize the analyzed works with reference number, year of publication, brief description of the focus of the work, and the simulation environment. The content type, the targeted type of powerline and the frequency band of the OFDM communication are also included.
# A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm
## 1 Introduction
The advantages and disadvantages of the PID controller parameter combination were judged by comparing the fitness values, and then the individual with the optimal fitness value was selected as the global optimal parameter combination at the current iteration number. In this paper, the improved QUATRE algorithm was combined with the parameter tuning of the PID controller:  
* An improved QUATRE algorithm was proposed based on the original QUATRE algorithm. We combined the linear population size reduction, continuously reduced the population size according to the linear function, and adjusted the control parameters online to achieve better algorithm performance. * The improved QUATRE algorithm was used to optimize the PID parameters, and the performance index function integral of time-weighted absolute error (ITAE) was used as the fitness function to find the optimal value through the improved optimization algorithm. At this time, the best individual was the global optimal parameter combination to achieve the optimal control of the control system.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 3 Problem Statement
### Our Proposed Model  
The mechanism of our proposed scheme is different from the mechanisms of the traditional scheme. For example, the smart device user generates an owner key that is unique for each smart device user, and sends the encrypted file and owner key to the semi-trust server. Practically, our scheme only requires one round of communication between the smart device user and semi-trust server side, while others require multiple rounds. Thus, our work reduces the communication cost as much as possible.  
The upload stage begins once the semi-trust server has received the file and owner key from the smart device user. During this phase, the semi-trust server computes the metadata for the file by generating two keys by using a cryptographic hash function. One of these keys is a challenge key while the other is a verification key. The semi-trust server uses the challenge key with the file to generate the metadata. Finally, the semi-trust server sends the file and metadata to the IoT-cloud system and deletes the file from its local storage.  
The semi-trust server employs the integrity checking stage to verify the integrity of the out-sourced data. This process begins with the semi-trust server sending a challenge key to the IoT-cloud system. The IoT-cloud system computes the audit key using the metadata with the challenge key of the file and sends the audit key to the semi-trust server. The semi-trust server receives the audit key and checks the verification key. If these two keys match, then the file is stored correctly. Figure 2 shows the mechanism of the proposed scheme.  
To reduce the integrity checking costs, this paper proposes a scheme based on the cryptographic hash function, key chain method, and low complexity mathematic operation. This scheme would be cost-effective and considerably more efficient than the bilinear map-based schemes.  
Figure 1: Architecture of the traditional scheme.