esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 5 Solving a Pseudo-Static Scenario
### Checking the Fitness of Individuals: Check Function  
This function is aimed at computing all the factors of the objective function of the problem for a candidate solution, i.e., the constraints of coverage and speech quality, and the value of the objective function. As shown in Algorithm 2, the input of the _Check_ function is the 3D coordinates of \(D\) drones (\(\mathcal{X}\)) and users' location \(\{\mathbf{w}_{k}\,|\,k\in\mathcal{U}\}\). Each drone in the candidate solution is assessed in three steps by three different functions. First, the collection of ground users associated to the AP is determined through the _Associate_ function. Then, the QoS level of associated users is evaluated with the _QoS_eval_ function. Finally, UAV endurance is determined (i.e., _Endurance_eval_ function). Next, we elaborate on these functions.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 3 ML for Wind Energy Prediction
### Machine Learning and Wind Energy Forecast
#### 3.2.3 Lasso Regression
* MAE (0.1410): With a MAE of 0.1410, the predictions average from the actual values by roughly 0.1410 units. This implies somewhat minimal error, although RMSE (which penalizes more significant errors) indicates somewhat more fluctuation in the errors. Cross-Validation Results for Lasso Regression:
* Mean R\({}^{2}\) (0.6132 \(\pm\) 0.0533): With an average R\({}^{2}\) score of 0.6132--rather close to the test set R\({}^{2}\) of 0.6110--the 10 cross-valuation folds Although the performance of the model fluctuates somewhat throughout the few cross-valuation folds, the standard deviation (\(\pm\)0.0533) indicates minimal fluctuation that suggests consistency. * Adjusted R\({}^{2}\) (0.6108 \(\pm\) 0.0533): With a mean of 0.6108, the modified R\({}^{2}\) is also rather consistent; it indicates that the model can generalize effectively over several folds and is not overfitting. * Mean MSE (0.0313 \(\pm\) 0.0052): With a tiny standard deviation (\(\pm\)0.0052), the average MSE over the cross-valuation folds is 0.0313, somewhat near to the test set MSE of 0.0319. This indicates that the model is not unduly sensitive to several subsets of the data and is rather steady in performance. * Mean RMSE (0.1769 \(\pm\) 0.0722): Again, revealing a comparable average prediction error, the RMSE from cross-validation (0.1769) is once more near to the test set RMSEof 0.1787. Though it is still reasonable, the standard deviation (\(\pm\)0.0722) indicates far more fluctuation in mistakes between folds than in MSE. * Mean MAE (0.1399 \(\pm\) 0.0105): With cross-validation, the average MAE (0.1399) is rather close to the test set MAE (0.1410). Furthermore, showing consistency in the prediction accuracy across several subsets is the low standard deviation (\(\pm\)0.0105). The cross-validation outcomes closely align with the test set findings, indicating that the model generalizes effectively and is not overfitting the data. The minimal standard deviations for all measures indicate the model's stability across various data splits.
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
## 3 System Model  
The system model employed in this study aims to accurately simulate the challenging propagation environments encountered in SAGINs. These environments are characterized by fluctuations in received signal strength, delay, and Doppler effects due to multipath propagation and high mobility, such as those encountered in vehicular communications, high-speed trains, unmanned aerial vehicles (UAVs), and non-terrestrial networks (NTNs), as shown earlier.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 4 User's Demands Awareness
### User-Demand-Aware Clustering Algorithm
The strength of social relationship can be reflected by the attributes of social relationship such as the degree of trust and the demand similarity. The greater the strength of social relationships, the stronger the social stability between users. The relationships between D2D communication devices in social domain and physical domain are shown in Figure 2, and a stable D2D communication link can be established only when both the social and the physical link have strong stability. Figure 2: The relationships of D2D communication devices in social domain and physical domain. In the same grid, users are likely to request the same video file if users have similar video viewing demands, even receiving files in the same D2D multicast cluster. That's to say, the strength of social relationships can be expressed by the demand similarity between users, and the demand similarity can be calculated from the video request probability. In the Equation (7), the probability of any user \(u_{i}\in U_{C}\) requesting video file \(f_{l}\in F\) can be calculated by:  
\[P_{i,l}^{request}=\lambda P_{l}^{pop}+(1-\lambda)P_{i,l}^{interest} \tag{14}\]  
According to (14), the probability vector of user \(u_{i}\in U_{C}\) requesting videos can be expressed as:  
\[\mathbf{P}_{i}^{request}=\left( \begin{array}{ccc}P_{i,1}^{request}&\ldots&P_{ i,|F|}^{request}\end{array} \right) \tag{15}\]  
The probability vector \(P_{i}^{request}\) can reflect the video viewing demands of user \(u_{i}\in U_{C}\). Where \(|F|\) is the number of videos in the set \(F\). As mentioned, the strength of social relationships can be evaluated by the user's demand similarity, which is more realistic than simply using user's interest similarity. Therefore, the strength of social relationship between user \(u_{i}\) and user \(u_{j}\) can be calculated by:  
\[s_{i,j}=\frac{\mathbf{P}_{i}^{request}.\mathbf{P}_{j}^{request}}{||\mathbf{P}_ {i}^{request}||||\mathbf{P}_{j}^{request}||}=\frac{\sum\limits_{l}^{|F|}P_{i,l} ^{request}P_{j,l}^{request}}{\sqrt{\frac{|F|}{\sum\limits_{l}^{|F|}\left(P_{i,l }^{request}\right)^{2}}\times\sqrt{\frac{|F|}{\sum\limits_{l}^{|F|}\left(P_{j,l }^{request}\right)^{2}}}}} \tag{16}\]  
Due to the non-negative probability, the product of the probability vectors requested by user \(u_{i}\) and user \(u_{j}\) is also non-negative, namely \(s_{i,j}\in[0,1]\). According to Equation (16), the social relationship strength matrix \(\mathbf{S}\) about set \(U_{C}\) can be expressed as:  
\[\mathbf{S}=\left[ \begin{array}{ccc}s_{1,1}&\ldots&s_{1,|U_{C}|}\\ \ldots&\ldots&\ldots\\ s_{|U_{C}|,1}&&s_{|U_{C}|,|U_{C}|}\end{array} \right]_{|U_{C}|\times|U_{C}|} \tag{17}\]  
where, \(|U_{C}|\) is the number of users in the set \(U_{C}\), and if \(i=j\), then \(s_{i,j}=1\).
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 4 Simulations
### BER Performance Analysis for Random Locations  
In this subsection, we evaluate the average BER performance of our proposed APC, GRPA, FPA, and TDMA.  
We assume that the PD receiver is randomly located on the Ceiling. We randomly selected 30 points from the Receiver Plane as the PD receiver's locations to simulate their average BER performance. For two LED transmitters scenario, three LED transmitters scenario, and four LED transmitters scenario, the simulation results are illustrated by Figures 4b and 5a,b, respectively. From the three figures, we can observe that the APC strategy we proposed can obtain best BER performance among the four schemes. In addition, the FPA scheme and GRPA scheme can hardly be applied to our considered uplink NOMA VLC system.
# Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard
## 3 The Effects of Interference  
This section shows the effects of CWI on the different stages of the communication using DVB-S2 standard. This effect is seen mostly in four stages: the signal constellation, the Symbol Time Recovery (STR) [24], the frame synchronization process [25] and the Carrier Frequency Recovery (CFR) [10]. These four steps can be found almost in all typical narrowband receivers. Figure 1 displays the main block diagram of the DVB-S2 receiver and its major components [10].  
Symbol time recovery (STR) adjusts the correct time of downsampling, which allows selected samples to have the maximum energy and a correct position at the center of the eye diagram. DVB-S2 standard uses the Gardner algorithm [24] to perform the STR process. After the STR block, the frame synchronization utilizes an appropriate correlator and searches the received frames, symbol by symbol, to find the start of the frame and the inserted physical layer (PL) header which contains information about the communication mode. The DVB-S2 transmitter inserts a unique word for the start of frame (SOF) at the beginning of a frame. When the correlator reaches the SOF position, a high peak is used as an indicator for correct detection [25]. After the frame synchronization, the receiver can detect the position of the pilots, thus, data aided Carrier-Frequency-Recovery (CFR) can be fulfilled. Usually, CFR is completed in two sequential steps. Both steps are based on inserted pilots. The first step is called coarse-carrier-recovery that removes a large part of the frequency offset. When the residual carrier offset is small enough, a more precise algorithm can be utilized to completely compensate for the carrier frequency offset. It should be mentioned that there is a mode, under good channel situation, that the transmitter turns off the pilots and the receiver must utilize blind algorithms to track the frequency offset and Doppler. Thus, the effect of CWI on the phase-locked loop (PLL) [26; 27; 28] based synchronizer is also investigated.  
Besides mathematical analysis, in this research, all blocks are implemented in Matlab simulations to study the CWI effects on each block. In the simulation environment, we do not utilize the analog to digital converter and all numbers are in the discrete-time domain, thus, for simulation results sampling frequency can be ignored and all utilized frequencies are normalized in [[[[[[[[[[[[[[[[[[[[[[[[[[[[[[ ]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]].
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 4 Measurements
### Methodology
#### 4.1.2 State Duration Measurements  
All state duration measurements were done using the EFM32 GG-STK3700 Giant Gecko Starter Kit from Silicon Labs [22]. The setup is shown in Figure 4. Using the Gecko board, an OpenUSB pin was connected to pin PB9 of the Gecko board, enabling the Gecko to measure how long the connected OpenUSB pin was made low. The OpenMote firmware would then make the connected pin low at the beginning of the measurement and high at the end of the measurement. The output was sent over Serial Wire Output (SWO) to the console in the proprietary software Simplicity Studio on the connected computer, where post-processing of the duration data was applied [23]. The duration measurements were averaged in case variability between different measurements was noticed.  
Figure 4: Setup used to measure state durations with a connection from the PB9 pin on the Gecko board (bottom) and to the PD2 pin on the OpenUSB (top).
# Research on a Dynamic Calibration Method for Photogrammetry Based on Rotary Motion
## 6 Conclusions  
In this paper, a photogrammetric dynamic parameter calibration method is proposed, and a calibration system is built. It is verified by the calibration experiments that the spatial dynamic parameter fitting method of the superimposed dynamic circular motion of static target points can achieve accurate expression of spatial dynamic position and meet the needs of dynamic calibration of photogrammetry systems. In the process of dynamic calibration of a photogrammetry system, the key to dynamic position fitting lies in the accuracy of circular motion parameters, and the accuracy of the rotational speed and shaft position is the main system errors. Simultaneously, the measurement frame rate of the measurement system should be fully considered during the calibration process to ensure that the sampling frequency of the relative position rotation is matched with it. The experimental results show that when the real-time measurement frame rate of the photogrammetry system is 4 frames/s, the maximum rotational speed of the target that can be accurately measured cannot exceed 4.0 rpm; in this state, the speed value calculated by the photogrammetry system is consistent with the standard value, and the speed fluctuation is within 8%.  
Conceptualization, J.O. and T.X.; Methodology, X.G. and W.Z.; Software, J.Q.; Validation, T.X. and X.H.; Formal analysis, J.O.; Investigation, X.G.; Resources, J.O.; Data curation, Y.L.; Writing--original draft, J.O.; Writing--review & editing, J.O.; Visualization, J.O.; Supervision, T.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Background Knowledge
### Augmented Reality  
Particularly in the CH (Cultural Heritage) domain, AR has proved to be rather effective in presenting cultural heritage information, enhancing visitors' experience with various types of multimedia, combining entertainment and education [5; 6; 7; 8; 9]. Many recent studies have shown that AR is recognised as a technology with increasing value for innovation and enhancing the visitor experience [8; 9; 10]. AR enhances location-based learning because it helps learners connect historical sites with their prior knowledge and provides additional new knowledge relevant to them [11]. It is a technology that can \"animate\" archaeological sites, \"repair\" damaged status and objects, \"reconstruct\" monuments that have been lost over the years and provide the exhibits of the museums with the ability to \"talk\" to the visitor and give more information about their story (digital storytelling).  
Integrating AR techniques with cultural heritage applications results in significant improvements for the cultural user's experience [12]. First, AR applications provide information no longer printed on information boards. Therefore, there is no need to alter the cultural site while AR software can easily modify or update digital data. In the same way, AR applications can provide \"unlimited\" and dynamic information to users. Furthermore, as a state-of-the-art technology that can display customised digital information in users' own devices, AR can improve customer satisfaction, generate positive word of mouth and increase attendance for the cultural institutions. Since AR applications usually allow real-time visitor experience sharing through online community applications, integrating AR techniques in cultural applications can also generate higher attendance and awareness [13].  
Nowadays, various cutting-edge techniques are used in culture heritage AR apps to improve user experience, such as context awareness to deliver information relevant to the user's work or digital storytelling that allows users to listen to their object of interest's story. The usefulness of digital storytelling in the context of AR experiences has been underlined by several studies [14; 15]. This project examines the benefits of integrating AR techniques in a painting that \"interacts\" with visitors through digital storytelling, enhancing the relations between the user, narration, and context.
## 6 Discussion  
In this section, some PLLs have been compared in terms of processing time, frequency overshoot, and performance capability. Table 3 represents the results as below:  
* The computational complexity of proposed PLL is lower than the others, which causes higher system response speed.
* The frequency overshoot of proposed PLL is lower than the others, which makes the more accurate solution obtainable.
* The processing time of proposed PLL is not the least in all PLL models, however, it is less than two of them.
* The DC offset can be considered in the proposed PLL, which will increase the system reliability.
* The harmonics effects could be implemented in the proposed PLL to raise the recovering performance. The minimum THD represent the lowest THD that is measured using a RL diode-bridge load.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 4 MROR System and Network Model
### Mobility-Enhanced Channel Availability Modeling
#### 4.2.3 Route Stability  
In order to ensure the proper design that maintains a stable route, the optimal _mguard_ distance \(\ell_{i}\), that maximizes \(\varpi_{i}\),, has to be determined. Thus, since Equation (9) represents the average fraction of areas where channel \(i\) is available, it thus means that the fraction that is not covered can also be approximated as \(\Omega_{i}\left(\ell_{i}\right)\approx e^{-f\left(\ell_{i}\right)}\), where \(f\left(\ell_{i}\right)=\delta_{p,i}\gamma_{busy,i}\pi\left(Z_{e,i}+\ell_{i} \right)^{2}\). With respect to the average OFF period, if we assume the overhead incurred on switching to be negligible, _i.e._, \(T_{sw}\ll\gamma_{off}^{-1}\), then, \(\omega_{i}\) can also be approximated as \(\omega_{i}\approx 1-\frac{vT_{s,i}}{\ell_{i}}\). Thus, the \(\varpi_{i}\) can be written as:  
\[\varpi_{i}\left(\ell_{i}\right)\approx\Omega_{i}\left(\ell_{i}\right)\omega_{ i}\left(\ell_{i}\right)\approx e^{-f\left(\ell_{i}\right)}\left(1-\frac{vT_{s,i}}{ \ell_{i}}\right) \tag{18}\]  
However, it can be shown that \(\frac{\partial^{2}\Omega_{i}\left(\ell_{i}\right)}{\partial\ell_{i}^{2}}<0\). Then, by setting the first-order derivative of \(\Omega_{i}\left(\ell_{i}\right)\) to zero, we get:  
\[\frac{\partial^{2}\Omega_{i}\left(\ell_{i}\right)}{\partial\ell_{i}^{2}}=e^{- f\left(\ell_{i}\right)}\left(-2\gamma_{p,i}\rho_{busy,i}\pi\left(Z_{e,i}+\ell_{i} \right)\left(1-\frac{vT_{s,i}}{\ell_{i}}\right)+\frac{vT_{s,i}}{\ell_{i}^{2}} \right)=0 \tag{19}\]  
In order to mathematically simplify the problem, \(-2\gamma_{p,i}\rho_{busy,i}\pi\left(Z_{e,i}+\ell_{i}\right)\) in Equation (19) approximates to zero to give us the quadratic equation:  
\[\left(Z_{e,i}-vT_{s,i}\right)\ell_{i}^{2}-Z_{e,i}T_{s,i}\ell_{i}-\frac{vT_{s, i}}{2\gamma_{p,i}\rho_{busy,i}\pi}=0 \tag{20}\]  
After solving Equation (20), the optimal _mguard_ distance, \(\ell_{i}\), that maximizes \(\varpi_{i}\) can be given as:  
\[\ell_{i}^{*}=\frac{T_{s,i}vZ_{e,i}+\sqrt{\left(T_{s,i}vZ_{e,i}\right)^{2}+ \frac{2T_{s,i}v\left(Z_{e,i}-T_{s,i}vZ_{e,i}\right)}{\pi\delta_{p,i}\rho_{busy,i}}}}{2\left(Z_{e,i}-T_{s,i}v\right)} \tag{21}\]
# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 5 Conclusions  
Existing computer vision technology demonstrates low accuracy in classifying pet breeds in practice scenarios. We identify the primary cause as excessive simple data within the datasets utilized for model training and testing, and delineate the consequent drawbacks. To address this challenge, we have constructed a complex image dataset termed CPI, tailored for breeds classification of pet cats and dogs in real-world scenarios (especially complex environments). We have also introduced a lightweight breeds identification system for pet cats and dogs, leveraging the PBI-EdgeNeXt technology. Building on this foundation, we utilize the CPI dataset to apply knowledge transfer on three different SOTA models (ResNet34, DenseNet121, Swin Transformer) and compare the PBI-EdgeNeXt model with several lightweight SOTA models commonly used on edge devices. The results indicate that knowledge transfer based on the CPI dataset can significantly enhance the capability of the identification model to categorize complex images in real-world scenarios. This improvement is particularly notable when compared to the Oxford-IIIT Pet dataset, achieving higher accuracy in breeds identification of pet cats and dogs in such scenes. The PBI-EdgeNeXt model demonstrates the highest classification accuracy among the compared models, achieving SOTA level. Furthermore, we showcase the feature saliency maps of PBI-EdgeNeXt for extracting selected complex images, which prove that after fine-tuning on the CPI dataset, our model merely focuses on the regions of interest (pet cats or dogs), while other redundant features and interferences are suppressed. Therefore, we conclude that the PBI-EdgeNeXt architecture is highly suitable for the classification of pet breeds in real-world scenarios, particularly in complex environments.  
Z.L.: Contribution: methodology, research design; H.X.: Contribution: resources; Y.L.: Contribution: verification, manuscript writing; Y.Q.: Contribution: resources; C.W.: Contribution: verification, software. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article. The data supporting the findings of this study are available upon request from the first author, [[PERSON]], upon reasonable request.  
The authors declare no conflict of interest.
## 7 Results
### Effect of TTL  
As can be seen in the subsequent plots, the time-to-live of the messages has a significant impact on the metrics to a certain extent, as the longer a message exists, the higher the probability it has to be delivered. Any metric value, however, tends to plateau as more TTL is granted. We found that the TTL value at which the metrics began to settle in a notable way is around 300 s. This means that adding more time-to-live to the messages will not normally add any improvements. Also, depending on the router, some of them will exhibit a better performance when the TTL is smaller than that of the settlingpoint. Therefore, at least a minimum of TTL = 300 s is advised when evaluating router performance to capture the complete behavior.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Energy Efficiency Protocols
#### 3.1.3 Energy Efficiency Protocols Utilizing Hardware/Software Improvements
DyLoRa was implemented in real-world deployments and the results showed an approximately 41.2% energy efficiency improvement. In [54], the authors explored ADR mechanisms in dense networks configuring the communication parameters of LoRa. They implemented and evaluated the ADR mechanism of LoRaWAN, which dynamically manage communication parameters. They assumed that the communication channel is severely affected by small-scale fading. To counterbalance this, the authors presented an improved version of ADR that significantly optimizes the reliability and the energy efficiency in fading wireless channels. Additionally, they introduced a network-aware approach that configures the link parameters which are the SF, the TP, code rate, center frequency, and BW based on the global knowledge of the nodes in the network in order to improve the delivery ratio on very dense networks. Table 4 summarizes the basic features of all the forementioned energy efficiency algorithm in LoRaWAN. In the following comparison tables, S represents simulation and T represents a real-life testbed.
# Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs
## 3 Results  
As an example of application to a real problem, we have applied the proposed method to estimate both the magnetic susceptibility and the remanent magnetization of a sequence that includes Pre-Salt carbonates and igneous rocks from Campos Basin, Brazil. The Campos Basin is one of the most important and prolific offshore Brazilian oil provinces, currently accounting for about 18% of the Brazilian total hydrocarbon production which is dominated (about 79%) by the Pre-Salt sequence [21]. The magnetic data used in this estimate was acquired in well 1-RJS-755-RJ, located at 2950 m water depth about 200 km off the coast of Rio de Janeiro state (see Figure 4). This well has identified the presence of oil in carbonate reservoirs of the Pre-Salt section.