esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 5 Optimization Problem and Algorithm
### Optimal Time Allocation Algorithm  
As explained in Section 5.1, outcomes of Algorithm 1, \(\lambda_{i}\) and a search interval [a,b], are used as the input to Algorithm 2. Algorithm 1 works as follows. If \(\lambda_{i}\) is invalid (i.e., an outage occurs at \(U_{o}\) for a given \(\lambda_{i}\)), and \(\lambda_{i}\) should be decreased or increased until the outage requirement of \(R_{U_{o}}\) in (14b) is satisfied. If \(R_{(mBS+sBS),UAV}^{1^{st}}>R_{UAV,U_{o}}^{2^{nd}}\), \(\lambda_{i}\) needs to be decreased to increase \(R_{UAV,U_{o}}^{2^{nd}}\) in \(R_{U_{o}}\) from (7), the next \(\lambda_{i}\) needs to be updated within an interval \([a,\lambda_{i}]\). Otherwise, \(\lambda_{i}\) should be increased and updated in an interval \([\lambda_{i},b]\).  
```
0: Initialization Inputs: \(\lambda_{i},[a,b]\), \(\tau\). Initialization: \(\lambda=\lambda_{i}\), and go to the line2. Phase1: Update a search interval.
1: Check the validity of \(\lambda\) by (14b)
2:if\(\lambda\) is valid then
3:\(a=\lambda\) if \(R^{1 st}_{mBS,U_{m}}>R^{2 nd}_{mBS,U_{m}}\)
4:\(b=\lambda\) otherwise
5:else
6:\(b=\lambda\) if \(R^{1 st}_{mBS,U_{m}}>R^{2 nd}_{mBS,U_{m}}\)
7:\(a=\lambda\) otherwise
8:endif
9:Phase2: Update \(\lambda\).
10:if\(b-a<\tau\)then
11: Save \(\lambda\) as \(\lambda_{opt}\) and break.
12:else
13:\(\lambda=\frac{a+b}{2}\)
14:endif
15: Go to line 1
```  
**Algorithm 2** Optimal time allocation Algorithm  
With an initial \(\lambda_{i}\) and a search interval \([a,b]\), Algorithm 1 determines \(\lambda_{opt}\) based on \(R^{1 st}_{mBS,U_{m}}\) and \(R^{2 nd}_{mBS,U_{m}}\) in (6). When \(R^{1 st}_{mBS,U_{m}}>R^{2 nd}_{mBS,U_{m}}\), maximizing \(R_{U_{m}}\) in (14a) is the same as increasing \(\lambda\); hence, \(\lambda\) should be updated from \([\lambda,b]\) for a valid \(\lambda\) or from \([a,\lambda]\) for an invalid \(\lambda\) in the next step. On the other hand, when \(R^{1 st}_{mBS,U_{m}}<R^{2 nd}_{mBS,U_{m}}\), maximizing \(R_{U_{m}}\) in (14a) is the same as decreasing \(\lambda\); therefore, a search interval needs to be changed to \([a,\lambda]\) for a valid \(\lambda\), and \([\lambda,b]\) for an invalid \(\lambda\) to update \(\lambda\). \(\lambda_{opt}\) can be determined when the length of the uncertainty interval \((b-a)\) is smaller than the predetermined value of \(\tau\).  
The complexity of the optimal time allocation algorithm is equal to \(O(log_{2}(2/\tau))\). It is well-known that the bisection method has a complexity of \(O(log_{2}(W/TOR))\), where \(W\) is the width of the initial interval and \(TOR\) is the desired tolerance or precision (e.g., \(\tau\) in this paper). The number of iterations \(n_{i}\) where the bisection method converges to a root to achieve a required tolerance is upper-bounded as \(n_{i}\leqq n_{i,1/2}\equiv\lceil log_{2}(W/TOR)\rceil\). In addition, the bisection method is optimal in that it guarantees a root in the worst case, which has an absolute error of \(TOR\) within \(n_{i,1/2}\) iterations [35; 36].
## 6 Technical Challenges in IoT Integration for Smart Buildings
### Absence of Regional Proficiency  
The effective adoption and upkeep of IoT systems in smart buildings depend significantly on proficient professionals capable of designing, operating, and troubleshooting these technologies. Nevertheless, a frequent lack of local competence in IoT technology impedes the implementation and longevity of several initiatives. Developing a proficient workforce necessitates investment in educational and training initiatives, which may not be universally available or accessible across all locations. Furthermore, maintaining and disseminating knowledge among team members may be difficult, especially in rapidly changing technology environments. A deficiency of local experience may result in project failures, heightened operating expenses, and dependence on foreign consultants, which may become unsustainable over time.
## 6 Performance Evaluation and Discussion
### Simulation Results
#### 6.2.1 Mean Field Power Control Policy  
The mean field or the collective device distribution for the massive MTC nodes is depicted in terms of the multi-dimensional state dynamics. An individual analysis is performed due to the complex multi-dimensional structure of the mean field as well as the power control policy. The mean field behavior of the MTC nodes is analyzed separately and depicted as different plots according to the observed dimensional dependence on energy, interference, and time. The plots are organized into three distinct scenarios accordingly as an MF distribution with (a) fixed interference but variable energy and time, (b) fixed energy with variable interference and time, (c) fixed time but variable energy and interference, respectively, as shown in Figure 4. Similarly, the impact of state behavior of the optimal power policy is depicted in Figure 5.  
Figures 4 and 5 represent the collective behavior or the mean field of MTCDs as well as the respective power policies in the defined grid for the Lax-Freidrichs scheme. The random pattern in the structure of both the mean field and power distribution over entire state space is due to the randomness in interference and player interactions. Hence, deducing anything meaningful about the power control strategy from the mean field and power distributions seems difficult. This information can be obtained by sampling the distributions at distinct time intervals to gain insight into the behavior of MTC players in the discussed MFG design.  
To determine the impact of interference and player interactions on the mean field and the transmit power levels of MTC users the behavior of mean field and power distributions at different time instances is plotted in Figures 6 and 7. After some time, a convergent behavior in the mean field and power distribution can be observed, in spite of differing levels of interference induced at different time instances. This equilibrium behavior for the mean field and power distribution near final stages of the time domain indicate the sufficiency of the proposed MFG based power control design for MTC devices. A comparison with respect to the number of required iterations for the convergence of the proposed power control scheme provides further insight into the equilibrium of the proposed MFG.  
Figure 4: Mean field distributions for respective scenarios (**a**) \(\beta_{\max}=6\times 10^{-6}\) W, (**b**) \(\rm E_{\max}=0.5\,J\), (**c**) \(\rm T=5\,s\).  
Figure 5: Power distributions for respective scenarios (**a**) \(\beta_{\max}=6\times 10^{-6}\) W, (**b**) \(\rm E_{\max}=0.5\,J\), (**c**) \(\rm T=5\,s\).  
Figure 6: Mean field distributions at varying time instances with equilibrium evolution, (**a**) \(\rm T=2\,s\), (**b**) \(\rm T=3.5\,s\), (**c**) \(\rm T=5\,s\).
## 4 Discussion  
### Comparison with Prior Studies  
Comparing our results with previous studies, it becomes evident that data augmentation plays a crucial role in enhancing the performance of CADx systems. [PERSON] et al. [14] showed a performance of a 0.81 AUC and 0.77 accuracy in classifying EGC using narrow-band magnification images. [PERSON] et al. [15] showed an accuracy of 0.87 in classifying EGC using narrow-band magnification images. [PERSON] et al. [16] showed an accuracy of 0.82 in classifying BGU, EGC, AGC. Furthermore, they achieved an external verification accuracy of 0.72 with the depth-of-invasion classification performance for T1a and T1b. [PERSON] et al. [17] used images of lesions that were rotated by 90\({}^{\circ}\) to double their size and overcome data imbalance. The entire image dataset was then augmented four times by flipping it vertically and horizontally. Consequently, its study showed an accuracy of 0.77 for external validation in classifying the invasion depth of EGC.  
In our study, we achieved an accuracy of 0.83 in classifying T-stages 1 and 4, and an accuracy of 0.90 in distinguishing EGC from normal (NOR) cases. Thus, our study validates the effectiveness of the data augmentation techniques proposed. However, it is important to note that each study varies in terms of data quality, scale, and research objectives. Consequently, the performance figures in each study do not represent absolute values. A comparative overview of the performance of existing studies is presented in Table 6.
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 4 Simulations and Results Analysis
### Studied Scenarios
#### 4.2.3 Effect of the Confirmed Traffic  
Figure 8 shows the obtained results of the LPTX metric in the case of confirmed traffic while varying the payload length and the number of gateways with a frequency of 1 packet/min.  
Figure 6: Performance results for a payload of 10 and 100 bytes while varying the number of gateways with a frequency of 1 packet/min—unconfirmed traffic.  
Figure 7: Pkt-sent results for a payload of 100 bytes while varying the number of gateways with a frequency of one packet every 3, 5, 7, and 9 min—unconfirmed traffic.  
We can see in Figure 8a that with a low payload (10 bytes in this case), using a low number of GWs led to a high LPTX value. We note that the difference between using one GW and five GWs never exceeded 1000 lost packets, and this was the advantage of using more gateways. However, we see the opposite behaviour in Figure 8b, which shows the LPTX value when using a high payload (100 bytes). In this case, with one GW, we achieved a shallow LPTX value compared to the case of using five GWs, mainly when the number of EDs increased. We can explain this by the fact that using more gateways increased the number of sent packets since the ED used a low \(SF\) value, allowing it to send more packets. In addition, the number of retransmissions naturally increased when an ACK was required at the ED (confirmed traffic).  
Figure 8c,d show the obtained LPTX value for the case of two and five gateways, respectively, while varying the payload length. In Figure 8c, we can see that with a low payload length (10 to 50 bytes), we obtained a very high LPTX value compared to the case of using a high payload length (more than 60 bytes), mainly when the number of EDs increased. In Figure 8d, we can see that the LPTX value remained roughly the same for five gateways, even if we vary the payload length.  
In summary, our study of these three scenarios has made it possible to answer our initial questions. Typically, adding gateways implies that the network performance decreases in the case of high traffic. So, we can add more GWs without losing performance only if we choose the right payload length.
## 3 Results
### Intercomparison between UTOPIA and NOAH Simulations Initialized with GLDAS
#### 3.3.5 Soil Moisture (VWC)
In particular, this may be true considering the simulations performed with forcing given by observations versus GLDAS. Latent heat fluxes have also been analyzed in [2] over forest sites, showing that LHF exhibited distinct seasonal changes and that the modeled values resulted to be overestimated before the initiation of the growing season. Also, UTOPIA LHF values exhibit an overestimation of maxima for all the different boundary conditions. Regarding the mean values, they are overestimated, except for the experiment EXP1b. The results of simulations of temperature of upper soil layer differ from those of the other variables. The experiments EXP1a/b (boundary conditions: vineyard observations) give, in particular, minima and maxima quite different from observations: minima were slightly underestimated, and maxima were widely overestimated. Since the range of simulations driven by GLDAS2.0 is usually lower for all variables, if compared with those driven by GLDAS2.1, this variable is the only case in which the outputs of UTOPIA driven by GLDAS2.0 are closer to observations than those of GLDAS2.1. Measurements of soil water content were performed only in CO site, and minimum observed value was rather low (0.09 m\({}^{3}\)water m\({}^{-3}\)soil), whereas maxima were similar to those simulated. Simulations of all experiments overestimated mean values and minima. For deep soil layers, UTOPIA minima were fixed, in all simulations, at the values of the wilting point for the considered soil (respectively 0.155 m\({}^{3}\)water m\({}^{-3}\)soil for loam soil, 0.250 m\({}^{3}\) m\({}^{-3}\) soil for clay loam soil, 0.1794 for silty loam soil and 0.2181 for silty clay loam soil). When soil moisture is lower than the wilting point in the root layer, transpiration is not effective, and gravitational drainage is negligible, as it depends from soil moisture with a power law [37]. The only process able to extract moisture from this soil layer is thus the evaporation from the surface, that depends on the moisture itself (the resistance increases with low moistures). Thus, it is quite difficult to extract water from a soil in which the VWC is below the wilting point, and for this reason the soil moisture may remain constant. This is what happens in UTOPIA simulations, while in the case of NOAH model, there are some additional mechanisms allowing to extract water from a soil below the wilting point threshold. The observations carried out at CO site show some values which are quite small, well below the wilting point threshold for that particular soil type, this behavior seems in contrast with the physical process of water removal from soil by transpiration. We can hypothesize that the concept of fixed wilting point for a given soil type may be wrong since some plants (present between the vineyards) may have a lower wilting point with respect to vineyards, and this effect could eventually explain the VWC lowering in such a dry environment.
## 5 LPWAN Application Requirements
### Location Identification  
Location identification for devices is a crucial requirement. Location accuracy plays a key role in applications such as logistics and livestock monitoring. The resolution range may vary from a few centimeters to meters. Monitoring and security for sensing unusual events such as changed device location and facilitating the proper level of authentication need to be supported. Location identifications can be achieved by global positioning system (GPS), GPS-like systems, or running smart algorithms with the help of network infrastructure.
## 2 Background
### Statistical Bootstrapping  
Bootstrapping is a statistical technique introduced in the late 1970's that enables researchers to make data-based inferences without strict distributional assumptions for univariate and multivariate data. It involves two distributions: the underlying distribution of the data (for example, normal or binomial) and the distribution of a computed statistic (in our case, Stuart-Kendall \(\tau_{C}\) correlation). New \(m\) data sets are formed by Monte Carlo resampling, each one containing the same number of observations \(n\) as the original data set [22]. Monte Carlo resampling is performed through randomly selecting points in the original data set and copying them into the new data set, until there are \(n\) points in the new data set.  
That way, a data sample of the original data set might appear one time, multiple times, or not at all in the new data set. Such an operation is performed \(m\) times; and for each new data set created, it is necessary to perform the computed statistic operation. Now, we have a distribution of computed statistic results, from which we can obtain a confidence interval through several techniques, such as percentile [23], bias-corrected (BC) [24], bias-corrected and accelerated (BCa) [25], and approximate bootstrap confidence (ABC) [26], among others.  
The authors have chosen to use the percentile method since it suffices for the performed analysis and due to its easiness of implementation. The percentile method consists of plotting the frequency histogram of the \(m\) computed statistics of the new sampled data sets, and the 95% confidence interval will consist of the values between the 2.5 th and 97.5 th percentiles. These percentiles represent the lower and upper bounds of the confidence interval, respectively. The resulting confidence interval yields a range of values within which the true parameter value is likely to fall with a certain level of confidence, in our case, 95%.  
The idea behind such process is to estimate the sampling distribution of a statistic by repeatedly sampling with replacement from the observed data. The ultimate goal is to make inferences about a population parameter or the distribution of a statistic even when you have a limited amount of data, as long as the distribution of the limited data set somewhat resembles the real distribution of the real-world variable.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 4 DYNAATIC Multiple RPL Instances for Multiple IoT ApplicatIons (DYNASTI)
### DYNASTI Architecture  
DYNASTI aims to schedule RPL instances to modify the traffic class of applications. Instances are activated and deactivated to accommodate, for example, the appearance of a new sporadic application, which also affects the instances of applications that are already running. The architecture of the DYNASTI solution is shown in Figure 3 and is composed of the following parts:  
* Instance Scheduler.
* Sporadic Event Detector.
* Instances Database.
* Message Controller.
* RPL.  
Figure 3: Flowchart of the mechanisms of the DYNASTI solution in the sensor nodes.  
Figure 2: Relationship diagram between applications, instances, and traffic classes.  
Initially, the settings of the instances (from regular and sporadic applications) are stored in the Instance Scheduler (step 1). The Sporadic Event Detector is responsible for monitoring the appearance or termination of sporadic applications, generating events for the Scheduler (step 2).  
The Instance Scheduler, after receiving the event from step 2, must select which instances to activate or deactivate--in Section 4.3, we detail the strategies adopted by the Scheduler. Then, the status of the instances is updated in the database (step 3) and a notification is sent to the _sink_ node describing the instance changes (step 4) so that it is disseminated to the other nodes--this process is further described in Section 4.4.  
Finally, the Message Controller queries the _status_ of the instances in the database and filters the application and control messages of the disabled instances (step 5). Thus, the Controller is the effective component to perform the deactivation of the instances, preventing any message from being sent on the network, which saves resources.  
It is worth mentioning that the sink does not have the Event Detector component, the role of the _sink_ in the architecture is to assist in the network initialization (creating the DODAGs, initial status of the instances) and disseminating the information for performing the scheduling of instances in the network.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 6 Interpolation  
Interpolation techniques are essential for enhancing the smoothness and continuity of spatial audio experiences, especially when dealing with incomplete or sparse HRTF datasets. Various papers discuss the development of interpolation algorithms to improve HRTF accuracy and performance. The interpolation methods in the literature can be classified into three main categories, i.e., nearest-neighbour approaches, techniques based on functional models, and neural network-based interpolation. An overview of these categorisations is shown in Figure 5 and Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nearest Neighbour** & **Functional Models** & **Neural Networks-Based** \\ \hline Linear interpolation & Filter\&Delay systems & Autoencoder-based \\
[27; 171; 172; 173; 174; 175; 176; 177; 178; 179; 180] & [181; 182] \\ \hline Bilinear interpolation & Principal component analysis (PCA) & Generative adversarial network (GAN) \\
[25; 26; 183; 184] & [185; 186; 187] & [188] \\ \hline Tetrahedral interpolation & IIR filter model & Feature-wise linear modulation (FiLM) \\
[23; 24] & [189; 190] & [37] \\ \hline Vector base amplitude panning (VBAP) & Karhunen–Loeve expansion & Convolutional neural network (CNN) \\
[34; 191] & [192] & [66; 193; 194; 195] \\ \hline Inverse-distance weighting (IDW) & Spherical harmonics decomposition (SHD) & Spherical network \\
[28; 196] & [29; 30; 197; 200; 201; 202; 203; 204; 205] & [206; 207] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of the HRTF interpolation methods.  
Figure 5: Categorisation of the methods for HRTF interpolation presented in this paper.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Open Source Solutions  
The CMS framework and its associated architectures are implemented using open-source solutions, standard interfaces, and industry best practices. This design choice ensures the framework remains general and easily integrated with existing systems. From the interoperability and standardization challenges perspective, using open solutions provides an extensible and non-proprietary alternative for the sector. This addresses the lack of open frameworks and a common standard in the field. Similarly, in tackling technology maturation and deployment challenges, the framework mitigates the issue of the current absence of a unified operations platform for general and practical applications. Adhering to open standards facilitates broader adoption, prototyping, and real-world deployment.
## 3 Related Works
### Models Applying Continuous Time or Space Based Representation  
[PERSON] and [PERSON] have developed a distributed optimization environment for V2X-based intelligent transport systems. The model focuses on intersecting vehicle movements to reduce the energy demand of mobility flows and increase transport efficiency [40]. The developed system is made up of two controlling levels. The higher-level control layer iteratively predicts the minimum travel time for all vehicles at every node and selects the appropriate lane to minimize waiting time. The lower-level control layer is an optimization problem that focuses on energy consumption by manipulating the longitudinal motion of vehicles. As a conclusion, the authors demonstrated the performance of the developed system by simulating different intersection scenarios.  
[PERSON] et al. presented a distributed adaptive sliding mode controller (DASMC) that reliably mitigates the effects of various unspecified perturbations [41]. On the way to an intersection, all vehicles use the movement data of neighboring vehicles to perform DASMC in a decentralized manner, resulting in a low-risk joint solution. To deal with high traffic volumes causing unfavorable spillback, the model uses an iterative feedback algorithm to determine the amount of incoming traffic allowed at the junction. The performance of the developed traffic management concept has been demonstrated in several simulations.  
[PERSON], [PERSON], and [PERSON] [42] analyzed the control of wirelessly communicating vehicles by introducing the collision region method and decoupling the control problem into a high-level integrated timing and a decentralized motion planning task. Their demonstrations showed that the new multi-level control concept can obtain close to optimal solutions compared to system-level approaches. Also, the traffic efficiency achieved by the method is much better.