esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Guiding the Light
### Light Pipes  
Historically, one of the first applications of these photonic dividends from communication industry came in the form of fiber fed (conventional) spectrographs [12]. The simple concept of using an optical fiber to carry the light from the focal plane of the telescope to the spectrograph slit imparted immense flexibility to astronomical spectroscopy in several aspects: a) Size: compact solution for multi-object spectroscopy; b) modularity: the use of fibers isolates the spectrograph from the telescope, enabling quicker change of instruments; c) mechanical stability: due to isolation of the spectrograph, its alignment is more stable and immune from changes in the telescope orientation; d) improved image stability due to image scrambling that occurs due to thousands of uncorrelated total internal reflections; e) improved spectral calibration due to consistency of the illumination pattern between the source, flat field, and calibration sources.  
These features of fiber feeding led to significant developments in multi-object spectroscopy and fiber-fed integral field units. The fiber feeding was limited almost exclusively to optical and near-IR wavebands (0.3-2.4 \(\upmu\)m) due to the absorption properties of fused silica which is the principal material used in optical fibers [13]. Recently, fibers with new materials such as lithium niobate and fluoride glasses (e.g., ZBLAN glass), and chalcogenide glasses (e.g., gallium lanthanum sulphide glass) are being developed in order to extending this range to 10 \(\upmu\)m for space-based applications [13]. On the other hand, NUV guiding (\(\sim\)190-300 nm) has been enabled by high OH-content fibers [14].  
A key aspect in the design of fibers to capture the focused light is the number of modes supported by the fiber, which in turn depends on the refractive index gradient and the cross section of the fiber. The astronomical light received at the focus of a ground-based telescope is seeing-limited for optical and near-IR wavebands (i.e., the FWHM of the spot size is governed by atmospheric turbulence and not the diffraction limit). Capturing the large spot size with maximal coupling efficiency requires the use of large multimode fibers (MMFs, \(\sim\)several tens of modes). Although they act as _light pipes_, they are not suitable for any photonic manipulation of the light (such as filtering, interferometry, and spectroscopy in the photonic realm). The key reason is that most of these technologies, primarily inherited from the telecommunication industry, are developed for single-mode fibers (SMFs) which only propagate the fundamental mode, unlike MMFs which propagate multiple modes with different refractive indices.
## 2 Related Work  
Key management for an industrial plant has lots of challenges [11; 12]. The security requirements of industrial automation system and IT system are different. In [13], the authors gave an overview of IACS and the current research trend. [PERSON] et al. [14] highlighted the importance of security for IACS. In [15], NIST has provided recommendations for designing key management systems. The recommendations specify the factors and mechanisms to be considered for designing the key management framework. Principles of key management are discussed in [16]. Key management problem is approached in a modular and hierarchical manner. The problems encountered due to poor key management for IACS are discussed in [4; 17]. Poor key management leads to significant issues such as key loss or destruction, loss of sensitive data, increased complexity of security management, etc.  
IEC 61850 [18] is the Open International Standard designed to solve the interoperability issues in IACS. Its flexibility and reliability features make it popular across the globe. However, the standard does not address security features. To handle this, IEC 62351 standard has been designed. The IEC 62351 standard [19] consists of 10 parts. Some parts are still under development. Part 9, i.e., IEC 62351-9, is exclusively dedicated to key management profiles. The concrete key management details are not yet given. An efficient key management solution in IEC standards exists as a challenging research area.  
To solve the key management issues in automation systems, few key management solutions are available in the literature. The schemes can be classified into centralized and decentralized schemes. In centralized schemes [20; 21; 22; 23; 24; 25; 26], the KDC plays the role of distributing and generating the secret keys required for secure communication between the communicating entities. In decentralized schemes [12; 27; 28; 29; 30], preshared keying materials are used to generate the secret keys.  
A thorough survey of existing key management schemes for SCADA networks is discussed, as shown in Table 2. To put the references in table form, we selected three parameters (BC, MC, and RTU-RTU) and performed the comparison.  
1. Broadcasting (BC): In SCADA systems, there is a requirement to send the messages such as emergency shutdown, clock information, time synchronization message, open/close all safety valves, etc. to all the devices of the SCADA network. Hence, supporting broadcasting is essential.
2. Multicasting (MC): Multicasting is required to send a message only to the particular level of IACS devices. Many examples can be quoted for the requirement of multicasting in SCADA systems. One such example can be of sending SLEEP message to 15 devices, out of a total of 100 devices.
3. RTU-RTU communication (RR): Unicast communication is required for point-to-point communication between RTU-RTU.
## 3 Numerical Results and Discussion  
To show the effectiveness of the proposed CNEP problem with AC power flow equations, it is tested on the IEEE 6-bus and IEEE 24-bus test systems, which are usually considered as test cases in previous research works in this area, such as [40; 41; 42]. Additionally, the proposed method is implemented in the GAMS software package using the solver of CPLEX/BONMIN for the MILP/MINLP problem [43]. An explanation about these solvers can be found in [44; 45; 46]. Note that the total number of linearization segments of circular constraint is considered to be 180 to obtain a low calculation error.
# Biomechanical Performance of the Cemented Hip Stem with Different Surface Finish
## 3 Modeling of the Stem Cement in Femur
### Finite Element Modeling  
A finite element model of cemented hip prostheses was established to analyze the stress state under physiological gait loadings (Figure 5). The femur model consisted of the femur bone, metal stem, and the cement mantle. The metal stem had the following dimensions: distal diameter = 15 mm, stem length = 166 mm, offset = 45 mm, and neck length = 41 mm. Although the cross-section of the cement mantle varied along the stem shape from the proximal to the distal end, it had a constant thickness of 3.0 mm, which is within the ideal thickness range of 2-5 mm for clinical use [33].  
The entire model was meshed with hexahedron brick elements, resulting in the formation of 35,520 elements and 41,914 nodes. The interfaces between the cement and bone were assumed to be  
Figure 4: Schematic of the spring layer used to model the bonding between the bone cement and metal stem.  
Figure 5: (a) schematic of cemented prosthetic stem [34]; (b) finite element model of the cemented femoral components, including stem, cement layer and femoral cortical bone.  
perfectly bonded, while interfaces between the metal stem and the bone cement were modeled as elastic spring layers. Using this modeling approach, the cement-stem interfaces were characterized by spring elements with adequate stiffness, which was experimentally determined from the bonding specimens. In the case of cemented model with polished stem (0.14 \(\upmu\)m), the normal and tangential stiffness values of the spring elements were assumed to be 19.17 and 8.21 MPa/mm, respectively. The corresponding values assumed for the model with the sand-blasted stem (5.56 \(\upmu\)m) were 15.48 and 13.95 MPa/mm, respectively. With the assumption that the bone cement and metal stem were assumed to be evenly bonded with elastic layers with homogenous interfacial stiffness, in the FE-model, the stiffness constant of each spring element is calculated from the overall stiffness divided by total spring elements on the bonding surface. The materials used for the finite element model were modeled as linear isotropic, with a [PERSON]'s modulus of 210, 15.5, and 2.28 GPa for the metallic stem, compact bone, and acrylic bone cement, respectively. Poisson's ratio was set to 0.3 for all the materials [35].  
The physiological femoral loadings applied to the cemented model were based on a previous study [36], and included the joint forces and muscle forces. The maximum joint forces were calculated as 4.6 times the body weight at 45% of the normal walking gait cycle. The muscle forces induced at this gait instant are primarily derived from the action of the abductor muscles located on the greater trochanter (gluteus medius and gluteus minimus) and ilio-tibia muscles (gluteus maximus and tensor fascia latae). These force components generated at 45% gait phase for a body weight of 70 kg were calibrated and are listed in Figure 6. All nodes at the distal end of the femur bone were constrained in all directions.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 3 Proposed ACS Architecture
### ACS Functions
#### 3.3.1 Multipath TCP  
Both sides of the communication, the OBE and the CCE, run MPTCP with the redundant scheduler [12], which was developed by our research team and added to the Linux kernel (v 3.17.0) in 2015. This scheduler provides E2E redundancy by duplicating and sending every packet through all available network interfaces, as represented by continuous red--LTE--and discontinuous green--WiFi--lines in Figure 3. The advantage of this operating mode is that the same information is sent twice, so the receiver does not need to wait for retransmissions when a packet is lost, thus the overall delay of the communication is reduced. Moreover, thanks to the redundancy offered by MPTCP, the latency of the vertical handover procedure decreases.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 3 LoRaWAN Overview
### LoRaWAN Adaptive Data Rate  
The LoRaWAN protocol offers an Adaptive Data Rate (ADR) mechanism, which allows optimisation of the power consumption, throughput, and performance of nodes in the network by adapting the transmission parameters of the devices. Several studies have been conducted that aim to improve the standard LoRaWAN ADR mechanism proposed by Semtech in [14].  
The power consumption of a LoRa transmission is indirectly proportional to its BW and CR, and directly proportional to its selected SF, packet size, and transmission power. In a typical homogeneous network, the CR, BW, and packet size are kept fixed, similar to what is specified by LoRaWAN [2]. The energy required to transmit a packet will depend on the time-on-air (ToA) and the transmission power. Based on Equation (2), increasing the SF by one halves the data rate of a transmission (thereby doubling the ToA). Based on the information provided in [7], increasing the SF only increases the RF sensitivity of the receiver by between one and two dBm. The transmission power can be adapted between 0 and 14 dBm, which only increases the power consumption by a factor of 1.88. Therefore, due to the quadratic nature of the SF's effect on a packet's ToA, it is always preferred to adjust the transmission power up to the legally allowed limit, before considering increasing the SF.  
\[DataRate=\frac{BW}{2^{SF}}\times CR \tag{2}\]  
A LoRaWAN node can set the ADR bit in an up-link transmission, indicating to the network server that it is in a stable radio channel attenuation environment and is open to adapting its transmission parameters. The LoRaWAN will respond in a down-link message whether it is able to send ADR commands or not. The network server is now able to send ADR requests through MAC commands which form part of the LoRaWAN frame, to which nodes will respond to in future up-link-frames with MAC commands which contain link-adaptive data-rate answers. Nodes are free to disable ADR control at any time if they detect or anticipate unstable/worse radio channel attenuation conditions.  
Research in the field of ADR mechanisms is extensive and includes topics such as enhanced ADR for LoRaWANs with mobility [15] and extending the performance of LoRa by suitable spreading factor allocation [16]. Implementing an optimised ADR mechanism is beyond the scope of this research article; thus, we used the industry standard rule-based LoRaWAN ADR mechanism proposed in [14].
# Hybrid-Based Analysis Impact on Ransomware Detection for Android Systems
## 5 Results and Discussion
### Classes and Methods Frequencies  
Table 5 summarizes the categories and methods discovered during the 22 selected samples' dynamic analysis and their frequencies. The class that should be highlighted is _Base64_, which has the methods _encode()_ and _decode()_. _Base64_ was used by almost half of the samples that have encryption behavior. The _encoding()_ method encrypts all bytes from the specified array using the encoding scheme and writing the encrypted results to an array output. Furthermore, in some cases, the _MainActivity_ class contained the _DevicePolicyManager_ class, which is used to manage policies enforced on a device. Some of the methods included in this class are the two methods _LockNow()_ and _ResetPassword()_, which can be used by the ransomware app and perform the locking and resetting password actions.  
Figure 6 illustrates the frequency of the classes discovered during the dynamic analysis of the 22 selected samples. For example, the class _android.app.Activity_ and its method _StartActivity()_ is reported as the top used method among most ransomware apps. _StartActivity()_ is usually used whenever the app wants to launch a new activity, while the classes _javax.crypto.Cipher_ and _javax.crypto.spec.SecretKeySpec_ and their methods _doFinal()_ and _$init()_ come in the second and third places, respectively. These classes provide cryptographic encryption and decryption features that originate from the Java cryptographic extension (JCE). The public method _doFinal()_ is used to encrypt or decrypt data in a single step or complete a multi-step process. Depending on how this cipher is initialized, the data are encrypted or decrypted. The class _Crypto.Spec.SecretSpec_ is a constructor used to construct a secret key from the given byte array to provide a new pass key to lock the screen.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **MobSF Class Overview** & **Method** & **Frequency** \\ \hline
1 & android.os.SystemProperties & get & 1 \\ \hline
2 & android.app.admin.DevicePolicyManager.resetPassword & - & 1 \\ \hline
3 & android.view.WindowManagerImpl.addView & - & 1 \\ \hline
4 & android.app.admin.DevicePolicyManager.lockNow & - & 1 \\ \hline
5 & java.security.MessageDigest & update & 2 \\ \hline
6 & java.security.MessageDigest & digest & 2 \\ \hline
7 & com.up.net.MainActivity & - & 2 \\ \hline
8 & android.telephony.TelephonyManager & getDeviceId & 4 \\ \hline
9 & android.app.ApplicationPackageManager & setComponentEnabledSetting & 8 \\ \hline
10 & android.util.Base64 & encodeToString & 9 \\ \hline
11 & java.net.URL & openConnection & 10 \\ \hline
12 & android.util.Base64 & decode & 10 \\ \hline
13 & javax.crypto.spec.SecretKeySpec & \$init & 12 \\ \hline
14 & javax.crypto.Cipher & doFinal & 19 \\ \hline
15 & android.app.Activity & startActivity & 27 \\ \hline \end{tabular}
\end{table}
Table 5: MobSF classes, methods and their frequency.
# Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds
## 5 Conclusions  
This article presents a fuzzy reputation-based trust framework for heterogeneous federated testbeds. The reputation system uses QoS and QoE KPIs and modifies the fuzzy VIKOR methodology to compute the reputation score of each testbed. Furthermore, the designed credibility mechanism, based on SLA and monitoring data, protects the testbeds' reputation score from malicious users. The proposed reputation system is compared and is shown to outperform the FTUE reputation framework, which is designed for experimental federated environment based only on numerical QoS and QoE metrics. This comparison underlines the importance of mixing several numerical and fuzzy metrics in the computation of the reputation score.  
In our future plan, we intend to test the introduced fuzzy reputation-based trust framework with several types of testbeds, while using additional and more sophisticated QoS and QoE KPIs. Furthermore, the reputation score produced by the proposed system and the KPIs weights assigned by the users can be utilized by a recommendation algorithm in order to enable potential users to select the appropriate testbeds for their experiments. Finally, the proposed framework could be further extended and adopted on the one hand by infrastructure and/or service providers in promoting and advertising their services to potential users, while on the other hand, potential users may utilize it for selecting the most appropriate service.  
All authors contributed extensively to the work presented in this paper. [PERSON] formulated the original scientific problem of our current research work, contributed to the discussions and analysis of the comparative evaluation results and had the overall coordination in the writing of the article. [PERSON] designed the proposed trust framework and the evaluation experiments, led the discussions and analysis of the comparative evaluation results and had a key leading role in the article writing. [PERSON] developed the code of the trust framework, ran the evaluation and comparison experiment and contributed to the discussions and analysis of the comparative evaluation results. [PERSON] produced the results of the FTUE framework, contributed to the discussions and analysis of the comparative evaluation results and had an active role in the writing of the article.  
Figure 5: Comparison of fuzzy reputation system with the FTUEframework.  
**Funding:** \"This research was funded by the Greek General Secretariat of Research and Technology, Program \"Hellenic Research Infrastructure HELNET\", part of Initiative \"HELIX-National Digital Infrastructures for Research\" under Grant Agreement MIS 5002781.\"  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Ray-Tracing Simulation  
To set the optical properties in ray-tracing simulation, the transmittance and reflectance of the leaf and chamber surface were measured with a spectroradiometer (BLUE-Wave Spectrometer, StellarNet Inc., Tampa, FL, USA) (Figure 2). Because leaf optical properties for different ages or positions showed no differences, the average value of three points was used. The transmittance of the black board was neglected, and the ceiling of the chamber was set as a fully permeable material. Optical properties in the range of 400 to 700 nm were applied in the simulation, considering the spectrum range of the LED used.  
To perform ray-tracing simulation, the virtual growth chamber and LED plate were reconstructed (Figure 1B) based on the dimensions measured by 3D computer-aided design software (Solidworks, Dassault Systemes, Velizy-Villacoublay, France). A total of 640 red LED chips and 96 blue LED chips were mounted on the LED plate, considering dimensions and patterns. For each LED chip, spectral distributions of red and blue LEDs were measured with a spectroradiometer at 1-nm intervals for spectral power distribution (SPD) settings, and for physical light distribution (PLD), a Lambertian distribution with an angle of 60\({}^{\circ}\) was set.  
After virtual growth chamber setting, 3D-SPMs were placed in the virtual growth chamber to perform ray-tracing simulation (Figure 1B) with the observed rotation angle and position. To compare the measured light intensity with the simulated light intensity, virtual light sensors were placed on the light measurement points.  
The ray-tracing simulation was performed using ray-tracing software (OPTISWORKS, OPTIS Inc., La Farlede, France). The total number of rays emitted was set to 200 million, considering the model size. To match the PPFD in the virtual growth chamber with the actual environment, a cylinder-shaped detector was modeled based on the quantum sensor dimension and placed on the datum point. LED outputs were set to 0.009, 0.018, and 0.027 W for red LED chips and 0.02175, 0.0435, and 0.06525 W  
Figure 2: Transmittance and reflectance of a lettuce leaf (**A**) and reflectance of the chamber surface (**B**).  
for blue LED chips, representing PPFDs of 100, 200, and 300 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), respectively. The total photosynthetic photon fluxes emitted were 79.3, 158.6, and 237.9 \(\upmu\)mol s\({}^{-1}\), respectively.
## 6 Simulation Results and Analysis
### Simulation Scenario and Parameter Settings
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Parameter** & **Definition** \\ \hline Standard Deviation of Initial Action Distribution \(\sigma\) & 2.0 \\ Linear Decay Frequency \(\xi\) & 0.2 Hz \\ Minimum Action Standard Deviation \(\sigma_{\min}\) & 0.4 \\ Action Standard Deviation Decay Frequency (per time step) & 5000 Hz \\ Discount Factor \(\gamma\) & 0.99 \\ Clipping Parameter c & 0.2 \\ Policy Update Frequency & 100 Hz \\ Model Saving Frequency & \(1\,\times\,10^{5}\) Hz \\ Number of Running Rounds per PPO Update Cycle & 1 \\ Evaluation Network Learning Rate & 0.001 \\ Proximal Policy Action Network Learning Rate \(\rho\) & 0.0003 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PPO algorithm parameters. * FIFO + FIFO: No frame generation algorithm is applied for either the delay-sensitive or non-delay-sensitive business queues. Instead, the standard First-In First-Out (FIFO) algorithm is used. * DQN + HEFG: The Deep Q-Network (DQN) algorithm dynamically selects the delay threshold for real-time service queues, while HEFG continues to handle non-delay-sensitive services.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 6 Learning Paradigms
### Multitask Learning  
Multitask learning involves learning to solve multiple related classification tasks jointly with one network [75]. By learning shared feature representations, the performance on the individual tasks can be improved and a better generalization can be achieved.  
A natural approach is to train one model to perform ASC and AED in a joint manner [76] as acoustic events are the building blocks of acoustic scenes. Sound events and acoustic scenes naturally follow a hierarchical relationship. While most publications perform a \"flat\" classification, [PERSON] et al. exploited a hierarchical acoustic scene taxonomy and group acoustic scenes to the three high-level scene classes \"vehicle\", \"indoor\", and \"outdoor\" [77]. The authors used a hierarchical pre-training approach, where the network learned to predict the high-level scene class as the main task and the low-level scene class such as car or tram as the auxiliary task. Using a similar scene grouping approach, [PERSON] et al. trained a CNN with several shared convolutional layers and three three branches of task-specific convolutional layers to predict the most likely acoustic scene within each scene group [78]. So far, multitask learning is not common in state-of-the-art ASC algorithms.