esa-sceva/satcom-chunk-collection huggingface dataset:
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 5 Implementation and Results
### Results  
The setup of the scenario explained above illustrates how the proposed model covers the system requirements exposed in Section 3. The use of the single device (\(D2\) streetlight) for accessing different services with different permissions is a good example of how the model covers the _Application-scoped_ requirement. This device has read and write permissions in one service and just write permission in another. Furthermore, the resources the device can access are different in each service.  
On the other hand, using two types of clients (a web browser application and a set of devices), we test the _Client-independent_ requirement. Thanks to the designed model, any client capable of creating an OAuth 2.0 token can authenticate in the system and thus can send authorized requests to the back-end service.  
The configuration of custom permissions by HTTP verbs and paths allows us to decide which kinds of actions (write, read, delete, etc.) are allowed for each specific resource. In this use case, we have just configured this kind of permissions, but as explained before, the use of XACML policies enables the use of advanced authorization decisions based, for instance, on the HTTP headers or the  
Figure 5: Smart city diagram.  
requests content. This makes the architecture flexible and easily adjustable to the requirements of each use case.  
Finally, removing the authorization checks from the device and delegating them to external components (IdP, PEP and PDP) decreases the computational load in the devices. As explained before, this is critical when talking about sensors and actuators with light capabilities. Moreover, as outlined in Section 2, the policy enforcement point protects the access to the object of the authorization architecture. In the deployed use case, sensors act as subjects, and therefore, the performance comparison with devices acting as PEPs does not directly apply here.
## 4 Biosensors in the Field of Healthcare
### Wireless Power Transfer to Medical Implants
#### 4.3.2 Mid-Field WPT  
Currently, the inductive coupling technique is not suitable for powering implantable medical devices. Therefore, mid-field (MF) WPT (Figure 8D,E), has been introduced by [PERSON] et al., presents as an innovative solution capable of delivering efficient WPT to millimetre-sized deep tissue implants [148]. MF uses a specially patterned metal plate (dimensions 6 \(\times\) 6 cm) with four slots to create an ideal field pattern. The metal plate is located in the air in close proximity to the skin. It serves as a cornerstone of the MF technique by inducing intensive and adaptive energy transmission through propagating modes in tissues. In particular, four independent radiofrequency (RF) ports send exciting signals through the structural slots on the metal plate, which manage the targeted surface of the field, thereby enhancing efficiency. With this innovative approach, milliwatt power can be transferred to micro-implants greater than five centimetres (>5 cm) in depth. Body tissue safety is importantly considered where MF continues to offer a lower risk of overheating and damage to surrounding tissues [143; 149; 150]. Consequently, the MF approach is currently the most preferred form of WPT for deep-body implants.
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 4 Discussion
### Classification Algorithm  
Threshold analysis of the combined acceleration method integrates two distinct stages. The first stage utilizes a combined acceleration interval counting method, facilitating quick activity level evaluation. The second stage, utilizing variance analysis of combined acceleration, delves deeper into motion characteristics, effectively distinguishing behaviors of varying intensities. The combined acceleration threshold analysis method leverages both strengths, significantly improving activity level classification accuracy.  
[PERSON] et al. [16] explored the influence of acceleration on diverse horse activity levels (walk, trot, and center). Their research offered valuable insights into the effects of acceleration on gait, contributing to a better understanding of equine motion. [PERSON] et al. [29] conducted an objective study on horse gait using an accelerometer. The results indicated that this accelerometer could effectively assess horse gaits. This study validates the application of acceleration analysis. It demonstrates how to translate these theoretical insights into practical animal activity level classification applications. This study employs MPU6050 sensors to capture horse motion data. It distinguishes low-activity, medium-activity, and high-activity behaviors through threshold analysis.  
In equine gait classification, [PERSON] et al. [17] achieved up to 97% accuracy by training machine learning models on raw accelerometer data and feature-extracted data. [PERSON] et al. [30] focused on unsupervised deep learning techniques, using deep embedded clustering (DEC) methods and discrete Fourier transform (DFT) preprocessing for automatic equine gait classification, reaching an overall accuracy of about 83%. This research introduces a horse activity level classification method based on combined acceleration threshold analysis, surpassing the unsupervised deep learning approach with a classification accuracy of 91.57%, although lower than the machine learning-based approach. The unique aspect of this method is its capability to be directly deployed on hardware without relying on extensive server computations, making it particularly suitable for scenarios with limited computational resources or where processing is required. Despite a slight compromise in accuracy, this method offers a novel and practical solution for monitoring animalactivity levels with its simplicity, efficiency, and ease of deployment, which is especially valuable in resource-constrained environments.
# Optimized Computation of Tight Focusing of Short Pulses Using Mapping to Periodic Space
## 2 Materials and Methods
### Mapping to and from the Computational Subregion
Finally, we estimate the expected benefit of using the method. According to the discussion above, the method reduces both computational time and memory demands by a factor of \(R_{0}/D\) (neglecting the logarithmic factor in the computational complexity of the FFT routine). While the choice of \(R_{0}\) depends on the required level of accuracy, an estimate appropriate in many cases is 10 times the wavelength of the radiation. For long pulses this would mean \(R_{0}\sim 10\lambda\). However, since the method is designed for very short pulses (with duration equivalent to a few cycles) we assume an octave-spanning spectrum and account for the shape of the pulses by using the longest wavelength component \(L\), i.e., \(R_{0}\sim 10L\). As we will see further (see Section 3.1) a relative error in the peak field amplitude of less than 1% is reached at \(D\approx 2L\). Thus we estimate that the described method can reduce the computational time and memory demands by a factor of order 10 (Section 3.1), depending on the level of required accuracy.
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 2 Material and Methods
### Nature-Inspired Optimization Algorithms
#### 2.2.1 Genetic Algorithm (GA)  
A simple genetic algorithm (GA) is a stochastic method that searches in wide search spaces, subject to some probability values. For these reasons, it can converge to the global minimum or maximum, depending on the specific application, and can skip possible local minima or maxima. Genetic algorithms were proposed by [PERSON] [24] who tried to find a method to mimic the evolutionary process that characterizes the evolution of living organisms. This theory is based on the mechanism representing the survival of the fittest individuals over a population. Genetic algorithms have been widely applied to several disciplines, such as feature selection [39], biomedicine [40], mechanical systems [41], etc.  
Initially, a population of candidate solutions (chromosomes), satisfying the constraints of the problem, is randomly constructed. In each iteration (generation) of the algorithm, a fitness is assigned to each chromosome, which is a real number value that indicates how suitable the solution for the problem under consideration is. In each generation, a set of operators, aiming to produce new unexplored solutions, is applied to the entire population. The most popular operators are:  
Selection is an operator applied to the current population like the one used by natural selection found in biological systems. The fitter individuals are promoted to the next population and poorer individuals are discarded.  
Crossover is the second operator and follows selection. This operator allows for solutions to exchange information that the living organisms use to reproduce themselves. Specifically, two solutions are selected to exchange their substrings from a single point and after, according to a predefined probability \(P_{c}\). The resulting offsprings carry some information from their parents.  
Mutation is the third operator and follows crossover. According to this operation, a small part of the chromosome is changed (e.g., a single bit of a binary string is flipped) according to a predefined probability \(P_{m}\).  
Elitism is the procedure according to which a fraction of the fittest chromosomes in a generation is ensured to be maintained in the next generation.  
After the application of the above operators to the current population, a new population is formed and the generational counter is increased by one. This process is repeated until a predefined number of generations is attained or some form of convergence criterion is met.
## 3 Results
### GPP Validation
#### 3.1.1 Eight-Day GPP
The GMAO_GLASS GPP algorithm provided the smallest values of bias for all biomes, and the Tower_GLASS GPP algorithm exhibited the highest values of R\({}^{2}\) for most biomes, except croplands (CRO); for croplands, no algorithm fitted the Flux_Tower GPP more effectively than the GMAO_GLASS GPP algorithm because it provided the highest value of R\({}^{2}\) (0.35) and the lowest values of both bias (\(-5\) gC\(\cdot\)m\({}^{-2}\).8-d\({}^{-1}\)) and RMSE (89%). Figure 2: (**a–_r_**) Time series of eight-day GPP derived from the tower estimates (i.e., Flux_Tower GPP) and four different experimental groups (i.e., MOD_GMAO GPP, MOD_Tower GPP, GMAO_GLASS GPP and Tower_GLASS GPP) at all eighteen sites. The full name for each site is listed in Table 1. The units of the mean and bias are gC\(\cdot\)m\({}^{-2}\).8-d\({}^{-1}\), and the RMSE is expressed as a percentage. Significance levels: \({}^{**}\)\(p<0.01\).
## 3 Energy Conserving Issues in Wireless Networking-Based IoT
### Energy Conserving Issues for WLAN-Based IoT  
Basic energy conservation for WiFi devices is achieved through IEEE 802.11 Power Save Mode (PSM) as illustrated in Figure 5. In _Ad hoc_ Traffic Indication Map (ATIM) window after the start of each beacon interval, a node remains awake for the exchange of ATIM requests and responses. A node sends an ATIM request message if it has a packet to send to an intended receiver. The receiver sends acknowledgement and will stay awake during the rest of the beacon interval for packet reception. Otherwise, it can go into a sleep mode for power saving. The existing IEEE 802.11 PSM may not be adequate for battery-operated constrained IoT devices and multi-hop IoT network settings [66, 67].  
In the case that IoT devices use WiFi to reach the 3 GPP core network and eventually IoT servers, device power consumption due to congestion in WiFi networks should also be kept in mind [63, 64, 65]. When IoT devices use WLAN type technologies such as the IEEE 802.11 family standards, they may construct multi-hop topologies to enlarge their reachability. In such cases, there may arise the issue of increased power consumption due to severe collisions in multi-hop communications [66, 67]. The standardization efforts, _i.e._, IEEE 802.11 ah, are being made to make a more suitable choice for IoT. Collision avoidance schemes and their implementation regarding the multi-hop communication collision issue are necessary for IEEE 802.11 ah [72, 73].
## 6 Conclusions  
In this paper, the containment control problem of MASs is solved using the MFAC approach. Based on the dynamical linearization technique, the heterogeneous unknown nonlinear agents are transformed into CFDL data models, and then the novel distributed MFAC algorithm was designed to ensure that the followers are driven into the convex hull of the leaders. Further extension includes the results under time-switching topologies and dynamic leaders. The proposed algorithm is data-driven, i.e., only I/O data rather than \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **MFACC, \(\lambda\) = 0.5** & **MFACC, \(\lambda\) = 1** & **MFACC, \(\lambda\) = 2** & **Method in [12]** \\ \hline ISE, \(\beta\) = 0.1 & 119.8 & 189.2 & 327.8 & 793.5 \\ ISE, \(\beta\) = 0.2 & 120.0 & 188.8 & 327.4 & 794.6 \\ ISE, \(\beta\) = 0.5 & 120.7 & 188.2 & 326.1 & 800.6 \\ ISE, \(\beta\) = 1 & 125.6 & 188.5 & 324.9 & 824.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison in Example 3. Figure 11: Outputs of agents with the method in [12] for Example 3: (**a**) the method in [12], \(\beta\) = 0.1; (**b**) the method in [12], \(\beta\) = 0.1.  
systems models are used in the controller design. Three numerical examples are given to validate the effectiveness of this method. This paper brings the discussion of coordinated MASs MFAC schemes from the single leader field to multiple leaders field along with a novel stability analysis method. Although the proposed method acquires several advantages, it still has limitations facing real applications. In the future, the containment control problem in the event of sensor failure and time delay will be investigated. Moreover, the algorithm should be validated by practical experiments. Conceptualization, Y.R; methodology, Y.R. and H.J. ; software Y.R. and Y.X. ; validation, Y.R. and D.L. ; formal analysis, Y.R. and S.X. ; investigation, S.X. ; resources, L.W. ; data curation, D.L. ; writing--original draft preparation, Y.R., H.J., D.L. and Y.X. ; writing--review and editing, Y.R., Y.X., S.X. and L.W. ; visualization D.L. ; supervision, H.J., D.L. and L.W. ; project administration, Y.R. ; funding acquisition, H.J. and S.X. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China under Grant 62103057, R&D Program of Beijing Municipal Education Commission under Grant KM202210009011. Not applicable. All data generated or analyzed during this study are included in this article. Author [PERSON] was employed by the company China Mobile Group Design Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript:
## 4 Conclusions  
The synthesis of highly crystalline Li\({}_{4}\)Ti\({}_{5}\)O\({}_{12}\) material that shows high electrochemical performance was successful using a salt-assisted solid-state reaction with LTO made up of micron-sized, homogenous particles. The XRD pattern results showed that the NaCl addition enhanced the crystallinity of the LTO material. Increasing the NaCl addition from 0 to 6 wt % improved the crystal formation of the LTO material. However, the optimal amount of NaCl addition proved to be 4 wt %. The excellent charge-discharge capacity of LTO with addition of NaCl at 4 wt % was 145.6 mAh/g, and the compound showed good stability during a subsequent cycling test. The favorable benefits of this method are its facile synthesis and low cost for large-scale production, as well as a performance that features a high discharge capacity and good stability. This compound shows promise for use as an anode material in lithium ion batteries.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**LTO** & **Lattice Constant (Å)** & \multicolumn{3}{c}{**FWHM**} & \multicolumn{3}{c}{**Crystallite Size (nm)**} \\ \hline
4 wt \% NaCl & 8.362 & 0.154 & 0.137 & 0.168 & 54.57 & 63.53 & 53.11 \\
4 wt \% NaCl (post mortem) & 8.357 & 0.161 & 0.142 & 0.175 & 52.23 & 59.22 & 48.05 \\ \hline \end{tabular}
\end{table}
Table 4: The structural parameters of LTO material before and after cycling.  
Figure 7: Thermogravimetric and differential scanning calorimeter (TG-DSC) curve of LTO formation.  
**Author Contributions:** Conceptualization, H.W., M.N., and A.J.; Data curation, S.U.M., C.S.Y.; Formal analysis, S.U.M., C.S.Y.; Investigation, M.I.P., E.R.D., H.W., and M.N.; Methodology, A.P., S.U.M., C.S.Y.; Project administration, A.P.; Supervision, A.P.; Validation, H.W., M.N., and A.J.; Visualization, S.U.M., E.R.D.; Writing--original draft, S.U.M., C.S.Y.; Writing--review and editing, A.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is financially supported by Indonesian Ministry of Education, Culture and Higher Learning through World Class Research scheme (WCR) with contract no. 112/UN27.21/HK/2020.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] for assistance in this research.  
**Conflicts of Interest:** The authors declare no competing financial interests.
## 3 Materials and Methods
### Interference and Network Optimization Difficulties
This includes deploying access points strategically to minimize congestion, using directional antennas to focus signals and reduce interference, and implementing security measures to prevent unauthorized access. In addition, new technologies such as edge computing and network slicing can help to reduce latency and improve the reliability of IoT device communication. Table 4 outlines some of the main types of interference that can impact 5G networks and the IoT. Interference can occur due to a variety of factors, such as noise, multipath, co-channel interference, interference from other devices or wireless networks, and jamming. One of the key aspects of network performance that interference can affect is coverage. Interference can reduce the coverage of a wireless network by increasing the signal-to-noise ratio (SNR) threshold required for reliable communication. When there is noise, multipath, or interference from other wireless networks present, signals may not reach as far or penetrate as deeply into buildings, resulting in reduced overall coverage. Another aspect that can be affected by interference is latency. Interference can increase latency by introducing delays in signal transmission or reception. For instance, multipath interference can cause signals to arrive at a receiver at slightly different times, leading to signal distortion and increased latency. This delay can be particularly problematic for real-time applications, such as gaming or video conferencing. Interference can also impact the availability of a wireless network. When there is co-channel interference or interference from other devices, it can cause collisions or channel saturation, leading to reduced availability. This can result in packet loss or connection drops, making it difficult for users to access the network and resulting in a poor user experience. Access to a wireless network can also be affected by interference. Interference can make it harder for devices to connect to a network, leading to connection failures or reduced bandwidth. This can be particularly problematic in areas with high device density, such as urban environments. Interference can also affect modulation and coding. When there is interference present, the quality of the signal can degrade, making it more difficult for the receiver to demodulate and decode the signal. This can result in errors and reduce the overall throughput of the network.
## 2 System Model
### Steered Response Power Phase Transform (SRP-PHAT)  
The SRP method, \(P_{SRP}(\mathbf{X})\), can be treated as the output power of a filter-and-sum (F-S) beamformer, one which is steered to a set of source positions defined under a spatial grid. \(\mathbf{X}=\left[x,y\right]^{\text{T}}\) is a vector, and represents the (x, y) position coordinates of the target acoustic source [46].  
The phase transform (PHAT) is a weighting technique whose inclusion in the formulation of SRP helps to emphasize the phase information of the involved signals, thereby avoiding peak spreading [46]. For a microphone array with \(N\) elements, \(P_{SRP}(\mathbf{X})\) can be expressed as the summation of individual generalized cross correlation (GCC) functions, \(R_{mm}^{GCC}(\tau(\mathbf{X}))\):  
\[P_{SRP}(\mathbf{X})=\sum_{n=0}^{N-1}\sum_{m=0}^{N-1}R_{mm}^{GCC}(\tau(\mathbf{X})) \tag{1}\]  
Unlike simplified scenarios, in realistic acoustic propagation environments, the signals received from different microphone elements do not simply differ from each other because of the delay that is dependent on the relative position of the source with respect to other microphone elements. This is due to the impairments caused by multipath effects like reverberation as well as the presence of ambient and diffuse noise. Hence, the mapping from SRP-PHAT to 3D positional coordinates using simple regression methods suffers from inaccuracies.
## 2 XFEL and Focusing Optics  
The experimental platform was installed at BL3-EH5, which is 200-m far from the exit of the last undulator [2; 17]. At BL3, XFEL pulses are normally generated by a self-amplified spontaneous emission (SASE) scheme [18; 19]. The photon energy ranges from 4 to 20 keV with a pulse duration of around 8 fs in full width at half maximum (FWHM) in horizontal polarization [12; 13]. The bandwidth (\(\Delta E/E\)) can be selected from two operation modes: a pink and monochromatic beam. The pink beam, which preserves the original SASE-XFEL spectrum, has the bandwidth of \(\sim\)\(5\times 10^{-3}\) and provides the largest photon number in the all operation modes, which is more than \(10^{11}\) photons/pulse. The spectrum of SASE-XFEL has many spike structures which change shot-by-shot. A single-shot spectrometer installed at EH1, which is an upstream experimental hutch of the EH5, is available for data correction to compensate an influence caused by shot-by-shot spectral changes [20]. A monochromatic beam with the bandwidth of \(\sim\)\(1\times 10^{-4}\) is made by a double-crystal monochromator (DCM) using two silicon (111) crystals. The photon number decreases proportional to the narrowing of the bandwidth. In addition to these two basic operation modes, a self-seeded XFEL operation has been achieved for generating a bright quasi-monochromatic beam [21]. Moreover, two-color XFEL, which produces two XFEL pulses of different energies with a delay time up to 300 fs, is also available [22].  
In order to obtain an intense signal and a high signal-to-noise ratio, an XFEL pulse with a high photon flux should illuminate a limited area inside a pumped region by irradiation of a high-power laser pulse. For this purpose, a Kirkpatrick-Baez (KB) optics was installed for sub-micron focusing of XFEL. A large convergence angle of X-rays degrades a resolution of XRD measurement. Therefore, in order to avoid the degradation, the vertical convergence angle of the focused XFEL was suppressed to less than 1 mrad. As shown in Figure 1, the vertical and horizontal focal sizes were measured to be 480 nm and 430 nm, respectively, by a knife-edge scan method. Line-focused XFEL pulses are also provided by using only one elliptical mirror of the KB optics. Moreover, a larger focal spot up to several tens of microns is available by detuning the KB optics.  
Figure 1: Intensity profiles of focused X-ray free electron laser (XFEL) pulses in (**a**) vertical direction and (**b**) horizontal direction. Focal spot sizes, which are derived from Gaussian fitting of differential of knife-edge scan results, are (**a**) 480 nm and (**b**) 430 nm, respectively.