esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Current Approaches to Open Challenges of F Estimations from Proximal to Airborne Scale
### Influence of the Spatial Measurement Scale  
When current in situ approaches and protocols (cf. Section 3) are applied, temporally continuous but spatially discrete stationary F measurements can achieve reliable results. However, the representativeness of spatially discrete measurements of larger areas or the whole landscape is often limited. Consequently, the spatial extrapolation of discrete observations is hampered by the concurrent and compounding effects of the vegetation structure and composition, topography, and canopy morphology and the intrinsic spatial variability in vegetation canopies. The spatial composition of differently behaving plants or land cover types increases the complexity. Spatial heterogeneity can thus compromise a chosen sampling design for field measurements that should ideally represent surface variation and not local variations around the location of the instrument. Consequently, cross-scale assessments and comparisons of F require the consideration of spatial heterogeneity to adequately select sampling schemes (e.g., location of measurement, spatial extent to be measured) and properly operate field and airborne spectroradiometers [147; 148]. Additionally, the combined analysis of spatial heterogeneity for individual traits with theoretically expected covariances of traits would enable the identification of the superimposing effects in retrieved trait information (e.g., insufficiently compensated canopy structural effects during F retrievals) [76; 125].  
Another spatial scaling effect that matters is the choice of the ground sampling distance since the composition of plants and land cover types in the sensor's field of view determines the pixel-wise retrieved trait information (e.g., F, chlorophyll content). For instance, the authors of [93] used high spatial resolution (0.4 m) UAV hyperspectral imagery with a relatively low spectral resolution (6.4 nm FWHM) to approximate F760 using the FLD method from pure tree crown and aggregated pixels (50 m). They found that only a poor relationship is evident between pure crown F and F for aggregated trees, including crowns, shadows, and background. Similar results were found in [149], in which the authors investigated the effect of spatial resolution (aggregation) with the FluorFLIGHT RTM using the high-resolution (0.6 m) hyperspectral data of an oak forest study site and 3 FLD. They concluded that the F signal retrieved from mixed pixels was significantly affected by illumination effects and the canopy structure. Those effects are intrinsic to all radiance spectra retrieved from aggregated pixels, irrespective of the sample size, and they become increasingly critical with increasing levels of aggregation (pixel size) [89]. Further research is needed to investigate the spatial distribution of F in heterogeneous environments, along with the interaction of different traits (and what we can learn from it) and the right measurement scale to understand the physiological processes and their interaction.
# Resource Allocation Schemes for 5G Network: A Systematic Review
## 6 Open Research Issues and Trends in 5G
### Minimization of Latency  
The number of transmission delays may be elevated by increasing the number of BSs. It is essential to inquire about the scheduling delay and effect of transmission, as these can particularly contribute to the proposed schemes for real-time processing capability. It is also essential to discuss the trade-off between delay and performance triggered by coding across multiple-fading blocks.
## 4 Data Analysis and Results
### Thermal Environment Analysis  
A temperature and humidity analysis was conducted based on the meteorological data, which is presented in the three-dimensional diagram in Figures 23-26. Beijing has distinct seasons, in which the winter is long, dry, and cold, the summer is hot and rainy, and the autumn is comfortable.  
Figure 21: Wind frequency, direction, and speed diagram.  
Figure 23: _Cont._  
Figure 22: Annual wind frequency chart.  
Figure 23: _Cont._  
Figure 23: (**a**) Monthly temperature chart; (**b**) three-dimensional temperature map; (**c**) monthly humidity chart; (**d**) three-dimensional humidity diagram.  
Figure 24: Hourly temperature distribution.  
The system type, number of internal occupants, calorific value, and activity schedule were key in the building model, which was a student dormitory building with four-person rooms. Weekdays were from Monday to Friday, leaving the room empty from 8:00 to 12:00, and from 14:00 to 17:00. Figure 24 presents the hourly temperature distribution. The daily heat gain and loss situation was obtained through measurements such as the proportion of the heat generated by the comprehensive temperature, the heat from direct solar radiation, the heat from cold air infiltration, the heat from internal personnel and equipment, and the heat from interregional heat and loss. The actual heat gain of the scheme and various proportional indexes were simulated. For rooms with relatively extreme temperature distributions, corresponding active and passive coping strategies were adopted to analyze the temperature change of non-air-conditioned rooms, facilitating the rationalization of functional partitioning and the optimization of the design scheme.  
Figure 25: Energy gains breakdown.  
Figure 26: Thermogram of the adaptability index.  
The simulation results of all the visible thermal regions are shown in Figure 25. The heat conduction of the envelope constituted 32.2%, the heat generated by direct solar radiation and the comprehensive temperature constituted 27.1%, the heat gained or lost by air penetration constituted 59.3%, which is directly related to the air tightness of the doors and windows. The heat loss over the entire year was mainly caused by the building envelope, accounting for about 60% of the total proportion. The natural heat gain throughout the year came mainly from solar radiation, which accounted for about 30%.  
The thermogram of the adaptability index is presented in Figure 26, where the abscissa was outdoor temperature, the ordinate was indoor thermal condition, and the middle line represented the ideal state of the housing interior area. The dots represented the trend of the discrete points for the heat gains and losses of the housing area. The heat loss of the rooms was serious, and attention should be paid to the insulation effect of doors, windows, balconies, and other enclosures in the building in winter to save energy consumption and to improve indoor thermal comfort.
## 4 Analysis and Findings of Research Questions
### Research Question 2: What Are the Detection Mechanisms of APT Attacks on Mobile Phones?
#### 4.3.1 AI Techniques/Algorithms Used to Detect APTs in Personal Computers
\begin{table}
\begin{tabular}{c c} \hline \hline
**Techniques/Algorithms** & **Article** \\ \hline Machine Learning & [23; 119; 122; 156; 157; 171] \\ Deep Learning & [158; 172; 176; 177; 178] \\ Game Theory & [159; 161; 162; 163; 164; 165; 166; 167; 168; 169; 161; 162; 163; 164; 165; 166; 167; 168; 169; 169] \\ Fuzzy Neural Networks (FNNs) & [169] \\ Adoptive Evolving Fuzzy Neural Networks (EFuNNs) & [170] \\ Support Vector Machine (SVM) & [119; 156] \\ K-Nearest-Neighbors (KNN) & [119; 156] \\ Logistic Regression & [156] \\ Naive Bayes & [119] \\ Random Forest & [119; 119; 156] \\ Bayes Net & [119] \\ Fractal-Based Machine Learning Algorithm & [122] \\ Learning-Based Aggregation Analysis Mechanism & [157] \\ Embedded Deep Neural Network & [158] \\ Anomaly Behavior Detection & [123] \\ \hline \hline \end{tabular}
\end{table} 
Table 11: AI Technique Algorithms Used in Detecting APTs on Personal Computers. reducing the impact of the APT on infrastructure in the majority of instances. The proposed methodology presented a theoretical scenario aimed at demonstrating the efficacy of the approach across various attack models. The study leveraged concepts from the field of structural controllability and game theory to support its findings. Finally, the study presented in [181] examines the dynamics of the interaction between a cyber forensic investigator and a strategic attacker through the lens of game theory. The present study pertains to a Bayesian game of incomplete information that is conducted on a multihost cyber forensics investigation graph, wherein both players traverse a series of actions. The classification of attackers into two distinct types has been established, including those that employ anti-forensic measures and those that do not. This categorization is derived from a probabilistic model that is constructed using historical incident reports. The investigator can formulate an effective investigative approach, known as the investigator's optimal randomized plan (IRP), by factoring in the ambiguity surrounding the attacker's classification and the potential benefits and costs associated with each course of action.
## 3 Architecture and System Evaluation  
### Hardware and Software Setup  
We propose an edge computing topology that consists of an edge server, a Wi-Fi access point, and three end devices. The end devices are three Raspberry Pi's 4 B models, two with 4 GB RAM and one with 2 GB RAM. The first Raspberry Pi with 2 GB RAM has a Google Coral TPU accelerator as an inference machine and the other two have a CPU-based TensorFlow-Lite inference machine. The edge server software is written in Python and it is responsible for offloading object detection tasks to the three end devices based on the needs of the user. Specifically, the server sends sets of images to the end devices to apply an object detector on the transmitted images. The end devices send the results (i.e., labels for every processed image and the bounding boxes) back to the server. The object detection is performed by YOLOv5 [15], which is a deep learning object detector [30]. The YOLO model takes as input an n x n array of image pixels. Each pixel is a float or int value, which down-samples the array by a specific number to give a grid. These grid cells propose the labels and the bounding boxes for every object contained in the dataset. As a result, a set of bounding boxes of the recognised objects along with their labels and their confidence values are generated. The proposed edge computing topology consists of an edge server which is a laptop with an i7-6700 HQ CPU processor with 8 GB RAM, the three Raspberry Pi's described above, and a wireless access point which are shown in Figure 1.  
Figure 1: An illustration of our testbed.
# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 3 Results
### Results and Discussion
To estimate validity of the performance of the models, K-fold cross-validation is used, and the total effectiveness of our LSTM and Bi-LSTM models is calculated by averaging the results of all five folds, as shown in Table 8.  
Table 7 compares measured and predicted soil moisture values, as well as error estimation results from our LSTM and Bi-LSTM models using K-Fold cross-validation. In both the LSTM and Bi-LSTM models, the error discrepancy between measured and predicted soil moisture values is quite small, according to the results. The LSTM model, on the other hand, has a larger error between predicted and measured soil moisture values than the Bi-LSTM model. In terms of cross-validation error, the LSTM results in all five trials, as well as the averaged overall error estimation, are lower than Bi-LSTM, which is  
Figure 8: Result of soil moisture value forecasting for (**a**) the next 1 h and (**b**) the next 30 min. Figure 9: Zoom in of the red box in Figure 8 (**a**) and Figure 8 (**b**). a 0.72% RMSE error and a 0.52% cross-validation error. The RMSE error for Bi-LSTM is 0.76%, and the cross-validation error is 0.57%. The proposed soil moisture forecasting using LSTM and Bi-LSTM models accurately predicts soil moisture value, according to the results. However, while modeling the proposed model, we had to test the learning rate in each case individually using the Adam optimizer, which took some time. The Adam optimizer is, even still, working on the proposed model's construction; however, the Adam optimizer works best on different datasets and requires drastically different learning rate schedules. Furthermore, these two models use a small dataset for training, testing, and validation, which may have an impact on model performance, and they use data from a single location.
# An Adaptive Model Predictive Voltage Control for LC-Filtered Voltage Source Inverters
## 6 Conclusions  
An adaptive MPC based on the augmented state-space model and the adaptive observer in a three-phase LC-filtered VSI has been proposed in this work. Unlike the conventional ones, all system uncertainties and disturbances are considered and estimated by the adaptive observer in the proposed control method. The adaptive observer can also provide the load-current-sensorless operation, which reduces size and cost and simultaneously improves the system reliability. Moreover, extensive simulations and experiments have examined both control methods' performance under different load conditions and uncertain parameters. Results confirm significant advantages of the proposed adaptive MPC over the conventional one regarding the steady-state error and THD of the output voltage.  
Conceptualization, H.G.-K., P.D., and F.B.; methodology, H.G.-K.; software, H.G.-K.; validation, H.G.-K.; formal analysis, H.G.-K. and P.D.; investigation, H.G.-K., P.D. and F.B.; resources, P.D. and F.B.; data curation, H.G.-K.; writing--original draft preparation, H.G.-K.; writing--review and editing, H.G.-K., P.D., and F.B.; visualization, H.G.-K.; supervision, F.B. and P.D.; project administration, H.G.-K.; funding acquisition, F.B. and P.D.; All authors have read and agreed to the published version of the manuscript.  
The work is supported by the Reliable Power Electronic-Based Power System (REPEPS) project at the Department of Energy Technology, Aalborg University as a part of the Villum Investigator Program funded by the Villum Foundation.  
Not applicable.  
Data available in a publicly accessible repository.  
The authors declare no conflict of interest.
# A Novel Alternating \(\mu\)-Law Companding Algorithm for PAPR Reduction in OFDM Systems
## 4 Proposed Scheme  
Behind understanding the characteristics of the original \(\mu\)-law, as it applies to reduce the PAPR, we know that the \(\mu\) parameter will affect the performance of PAPR and BER simultaneously. Hence, the choice of \(\mu\) parameter is an essential issue. As the \(\mu\) increases, PAPR will decrease, but the BER performance will be sacrificed. Therefore, we referred to a possessive selectivity scheme with a component-wise mechanic to alleviate the conflict between PAPR and BER in the original vector-based parallel \(\mu\)-law method. We name it the SULC scheme. Although this scheme's PAPR and BER performance is well balanced and improved, its complexity still has room for improvement. Therefore, we proposed a novel AULC scheme to reduce the complexity further.  
For a while now, there has been no precise rule for selecting the value of the \(\mu\) parameter in the \(\mu\)-law companding schemes. To obtain a reasonable \(\mu\) parameter estimate for PAPR and BER performance balancing, we utilize the Monte Carlo method and the numerical analysis of the proposed approach to arrive. Moreover, it is proved by experiments that our proposed schemes can reduce the PAPR significantly and maintain the performance of BER at a small and wise choice \(\mu\) parameter value. In this study, we made a trade-off between PAPR and BER; then, we chose the optimal value of the \(\mu\) parameter as 3 and applied it to our experiments in this article. Furthermore, we define a more obvious balance parameter, \(\eta\), to evaluate and verify this best choice. The following will describe the SULC and the proposed AULC algorithms.
# Trajectories and Resource Management of Flying Base Stations for C-V2X
## 3 System Model
### Radio Channel and Physical Layer
Assuming an ideal antenna without side lobes and with constant gain, the area potentially covered by the UAV under uniform propagation conditions is thus a circle of radius \(r=h\cdot\tan\alpha\). The antenna gain depends on \(\alpha\) as well: it is equal to \(G_{\alpha}=29\),\(000/(\alpha)^{2}\). We added to such gain 3 dB, to account for a minimum offset for the case in which \(\alpha\) is large. Note that, the higher the UAV height, the larger is the UAV covered area and the number of vehicles that it may be able to serve. If, instead, we fix \(h\), the larger is \(\alpha\) and the larger is the UAV coverage range, but the smaller is the antenna gain. The terrestrial and aerial components compute the signal to noise ratio (SNR) and signal to interference ratio (SIR) of the different links to take decisions on RRM. In order to achieve a satisfactory quality of service, SNR and SIR for the single vehicle \(v\) have to overcome a minimum threshold of \(SNR_{\text{min}}=10\) dB and \(SIR_{\text{min}}=2\) dB, respectively. The SNR and SIR are computed as  
\[SNR_{\text{v,n}}=\frac{P_{r\{v,n\}}}{2\cdot N_{0}\cdot B_{\text{subc}}} \tag{6}\]  
\[SIR_{\text{v,n}}=\frac{P_{r\{v,n\}}}{\sum_{j=1}^{N_{\text{TBS}}}P_{r\{j,n\}}} \tag{7}\]  
where \(P_{r\{v,n\}}\) represents the useful power received at terminal \(v\) on subcarrier \(n\), \(B_{\text{subc}}\) is the equivalent bandwidth of one subcarrier, \(N_{0}\) is the noise spectral density, \(P_{r\{j,n\}}\) is the interfering power coming from interferer \(j\), and \(N_{\text{TBS}}\) is the number of interfering TBSs. For the computation of SIR at TBSs, we assumed that the interference coming from the UAV was negligible thanks to the proposed RR scheduling (see later). The computation of throughput for each vehicle followed the link budget considerations. For both the cases of TBSs and UAV, the throughput achieved by user \(v\), \(T_{v}\), was computed according to  
\[T_{v}=\min\left(\frac{B}{N}\sum_{n=1}^{N}c_{v,n}\log_{2}(1+X_{v,n}),T_{\text{ max}}\right) \tag{8}\]  
\[X_{v,n}=\min(SNR_{v,n},SIR_{v,n}) \tag{9}\]where \(c_{v,n}\) equals one when subcarrier \(n\) is assigned to user \(v\) and zero otherwise. \(N\) is the total amount of subcarriers and \(B\) the bandwidth. Since the throughput was computed with the Shannon formula, we fixed a maximum achievable capacity per link, \(T_{\max}\).
# Performance Assessment of a Renovated Precast Concrete Bridge Using Static and Dynamic Tests
## 2 Bridge Description  
The bridge over the Ruzin Dam has been in operation since 1967 and was renovated in 2017 (Figure 1) as a result of its full closure in 2016 due to its critical state. The reason for the closure and for the necessity to renovate was the permanent (visible) displacement in the middle of the main span. The bridge structure consists of two parallel tapered box-girders with various heights between 1.4 m and 4.0 m. Pre-cast slabs are embedded between the two box-girders. The width of the bridge comprises the road surface (7.5 m) and the walkways on both sides (1.75 m); overall, the bridge is 11.0 m wide. The total length of the bridge is 154.0 m.  
The bridge was repaired by changing the original structural system. The reconstruction was carried out by reinforcement of the original joint in the middle of span no. 2 and by the addition of external pre-stressed cables (Figure 2). Pre-stressed cables consist of two groups. The first group of cables passes over the outer side of the box-girders and is fixed by additionally mounted steel deviators (two cables per box-grider). The second group of cables is placed inside the box-girders (two cables per box-grider). Therefore, the overall strengthening consists of eight cables (15 \(\times\) LS15.7/1860; for geometric and material characteristics, see Tables 1 and 2).  
Figure 1: The bridge over Ruizin Dam: (**a**) photo; (**b**) side-view; (**c**) cross-sections (dimensions in millimeters).  
The pillars with heights of 29 m and 24 m are monolithically connected to the load-bearing structure. A new sloping abutment with an approach slab was built in 2017. Renovation of the wing walls was realized by coupling reinforced concrete walls. Concrete C30/37 and steel B500 (B) were used.  
The bridge is of great importance in the regional road network. According to Slovak Road Administration data [19], in 2015, more than 3300 vehicles passed over the bridge daily. Therefore, bridge closure could cause significant complications since alternative routes are more than 30 km longer.
# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 2 Research Questions and Hypotheses  
Empirically, we have the following question for our proposed study.  
Q. What is the effect of language similarity and available resources inside PLMs? We hypothesize that:  
1. A cross-lingual transfer is more successful for typologically similar languages than for typologically different languages.
2. A large annotated dataset in a distant-family language can overcome typological differences, unlike a small annotated dataset in a close-family language.  
To answer the research question, which is to examine the effect of typology on the performance of cross-lingual sentiment analysis, we trained models using English and Slavic language datasets. The training involved the combination of diverse language datasets. We calculated the effect of utilizing a language during training and its effect on final performance in several different combinations. The outcomes were compared to previously published research. We determined the optimal language combination for sentiment transfer.  
This paper's contributions are as follows:  
1. Initially, we propose a framework for unified deep learning that utilizes existing data labels from high-resource languages on low-resource datasets. We conduct rigorous experiments on languages within the same language family. We investigate how effectively sentiment classification abilities could be transferred.
2. Second, we demonstrate that, given multiple large-scale training datasets, our framework is superior to a straightforward setup for fine-tuning. Finally, we devise the optimal method for jointly training sentiment analysis systems in order to address the issue of insufficient resources for target languages.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 9 Results and Discussion
### Spectral Efficiency Performance
The weight-based user-scheduling algorithm takes into account both the distance and received signal strength and allocates optimal power using an iterative GA approach to increase the spectral efficiency. The SIC-based interference cancellation technique is used at the receiver in order to mitigate interference in a multi-tier heterogeneous network. Thus, the proposed method, which uses weighting to assign different power levels to multiplexed users to minimize interference [42], performed better in terms of spectral efficiency compared to the minimum distance scheduling (MDS) and MCS algorithms.