esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Studies on the Effectiveness of SA-TLGDs
### SDOF-Wind Turbine with Single SA-TLGD Attached
The assumed magnitude for the frequency shift is quite common in real structures and caused, for instance, by temperature effects and stiffness variations within the operating life of structures. Figure 7 clearly indicates that due to the assumed shifts in the fundamental frequency the maximum head displacement \(w\) increases from the minimum value 0.05 m for the optimally tuned SA-TLGD, to around 0.08 m.  
To account for the expected frequency shifts that occur in real structures and that lead to a detrimental detuning of the installed vibration absorber, the SA-TLGD is equipped with a total of three gas volumes \(V_{01}\), \(V_{02}\), and \(V_{03}\) at each side of the tube (cf. illustration in Figure 1). Beside the optimal eigenfrequency of the SA-TLGD, \(f_{A,opt}=0.54\) Hz listed in Table 2, that corresponds to the unchanged natural frequency \(f_{S}=0.55\) Hz, the optimal eigenfrequencies that correspond to the shifted natural frequencies yield from Equations (22) and (24) to \(f_{A,opt}^{-5\%}=0.51\) Hz and \(f_{A,opt}^{+5\%}=0.57\) Hz. Subsequently, the required sizes of the gas volumes are calculated, based on the chosen SA-TLGD dimensions listed in Table 2, from Equation (9) to \(V_{01}=3.34\) m\({}^{3}\) (\(L_{g1}=4.26\) m), \(V_{02}=0.39\) m\({}^{3}\) (\(L_{g2}=0.49\) m), and \(V_{03}=0.35\) m\({}^{3}\) (\(L_{g3}=0.45\) m) to achieve the corresponding vibration frequency of the SA-TLGD. It is noted that the separated gas chambers are connected via controllable valves (cf. again Figure 1) and that, for instance, the required gas volume to achieve the lowest eigenfrequency \(f_{A,opt}^{-5\%}=0.51\) Hz relates to the gas volume size of \(V_{01}+V_{02}+V_{03}=4.08\) m\({}^{3}\). Figure 5: Frequency response functions (FRF) of the SDOF-wind turbine with and without activated optimally tuned SA-TLGD (steady state response of the horizontal tower head displacement \(w\)). Figure 6: Frequency response function (FRF) of the activated optimally tuned SA-TLGD (steady state response of the fluid mass displacement amplitude \(u\)). In practical applications, the air chamber volumes \(V_{0i}\) can be redesigned with a smaller or larger cross-sectional area than \(A_{T}\), i.e., basically the geometric shape of the air chambers is arbitrary and therefore adjustable to the available installation space of the structure. Obviously, the cross-sectional area of the tube \(A_{T}\) must be kept constant at least up to the maximum dynamic vibration amplitude \(u=U_{max}\) of the fluid mass.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 3 Survey of Existing 5G Satellite Integration (SATis5) and Associated Challenges
### SATis5 Technical Challenges
* Forward/Return Link Budget: LEO satellite power constraint and characteristics of eNB (evolved NodeB), gNB, and narrow-band UE must be incorporated into the link budget calculations of the LEO satellite communication links associated with satellite feeder link and satellite-to-UE links. * Battery Life: As specified in 3 GPP requirements, the battery life associated with mMTC-NB-IoT-UE is around 10 years. However, for SATis5 services, the battery life is expected to last less than 10 years. This is because the SATis5 devices will require:\(\odot\)  Longer RTT which requires a longer wake-up period time requires performing the access procedures and data transmission;  
\(\odot\)  Higher transmitted power to close the link. The commercial satellite industry is currently working on the technical solutions addressing the above challenges and designing the next-generation satellite systems that are integrated seamlessly into the current and future 5G systems. The following section provides an overview of existing SATis5 testbeds and commercial projects addressing SATis5 challenges.
## 4 Dynamic Testing
### Light Oxidation
#### 4.2.2 Light Oxydation: Statistical Analysis of Instantaneous Resistances  
The results of the statistical analysis of instantaneous resistances are shown and plotted against the current in Figure 16. For each zone considered, we calculated the median and interquartile range (IQR) of resistance values \(R(t)\) under the different experimental conditions. Several interesting conclusions can be drawn as follows:  
* Effect of oxide thickness: Resistance tends to increase with oxide thickness. In particular, zone L2, which has a thicker oxide layer than the other two zones, shows particularly high resistance values, up to around 85 \(\Omega\), especially for low currents (20 mA). On the other hand, zones L1 and L3, with thinner oxide layers, show resistances relatively close to each other under all test conditions, and well below those recorded for zone L2.
* Current effect: (i) Resistance tends to decrease with the increasing current intensity, irrespective of oxide thickness and test conditions. The median decreases with the increasing intensity, a trend also observed in statistical maps. In zones L1 and L3, resistance decreases gradually from around 13 \(\Omega\) at 20 mA to approximately 3 \(\Omega\) at 100 mA. In zone L2, resistance is reduced from 24 \(\Omega\) at 20 mA to around 7 \(\Omega\) at 100 mA.  
Figure 15: Examples of singularities in the \(U\)-\(I\) maps (framed in red). (**Top**): map from test on zone L3 with I = 20 mA, F = 200 N and v = 25 cm/s. (**Bottom**): extracts from the \(U\)(\(t\)) and \(I\)(\(t\)) time signals illustrating these singularities (circled in red).  
2. Dispersion of the voltage-current characteristics is reduced with the increasing current. This is also observed in the statistical maps.
3. As the current intensity increases, the effect of force and speed on contact resistance becomes less pronounced.  
This result underlines the fact that current is a key determining factor influencing contact resistance more than any other experimental parameter.  
* Effect of force: as force increases, the contact resistance tends to decrease and the voltage-current characteristics are less dispersed. This can be attributed to an increase in the number of asperities coming into contact and a widening of the contact zones, mainly due to the flattening of asperities under the effect of pressure.
* Effect of speed: as speed increases, the contact resistance tends to rise and the voltage-current characteristics become more dispersed. In fact, increasing speed leads to accelerated renewal of the contact surface, thus reducing the number and size of asperities involved.  
Figure 16: Representation of statistical analyses of contact resistances \(R(t)\) obtained for different oxide thicknesses (4 \(\mu\)m, 12.06 \(\mu\)m, 8.6 \(\mu\)m) as a function of current intensity, showing median values of contact resistances (\(\bullet\)) and their IQR intervals (I).
## 2 Methodology
### FSC Network Based on Global and Local Knowledge Distillation
#### 2.2.1 Pre-Train of Teacher Network  
The teacher model consists of a backbone convolutional neural network and two linear classifiers. The backbone network \(f_{\theta}(\bullet)\) is used for feature extraction of images, one classifier \(L_{w}(\bullet)\) is used for predicting the base class of image samples, and the other classifier \(L_{r}(\bullet)\) is used for predicting the rotation category in self-supervised tasks. Additionally, each classifier is followed by a Softmax layer. M image samples are randomly selected from the base class dataset, and each image is rotated at 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), with its corresponding rotation label as \(\hat{y}_{m}=[0,\,1,\,2,\,3]\).  
When image \(x_{m}\) is fed into the teacher network, the d-dimensional feature representation \(f_{\theta}(x_{m})\) is extracted by the backbone network. The classification scores of the base class prediction classifier and the rotation prediction classifier for the features are expressed as \(S_{b}\) and \(S_{r}\), as shown in Equation (1):  
\[\begin{cases}S_{b}=L_{w}(f_{\theta}(x_{m}))\\ S_{r}=L_{r}(f_{\theta}(x_{m})).\end{cases} \tag{1}\]  
Furthermore, the aforementioned classification scores are transformed into base class and rotation class prediction probabilities through a Softmax layer, as shown in Equation (2):  
\[\begin{cases}p(y_{m}=c|x_{m})=\frac{c^{\delta_{bc}}}{\sum\limits_{c=1}^{c}{ \varepsilon_{bc}}{\varepsilon_{bc}^{\delta_{bc}}}}\\ p(\hat{y}_{m}=r|x_{m})=\frac{c^{\delta_{rr}}}{\sum\limits_{r=1}^{c}{ \varepsilon_{rr}^{\delta_{rr}}}},\end{cases} \tag{2}\]  
where \(S_{bc}\) denotes the \(c\)-th element of the score vector \(S_{b}\), \(S_{rr}\) denotes the \(r\)-th element of the score vector \(S_{r}\), \(C_{b}\) denotes the number of base class labels, and \(p(y_{m}=c|x_{m})\) and \(p(\hat{y}_{m}=r|x_{m})\) are the probability output values of the base classifier and the rotation classifier, respectively. The cross-entropy loss function and the self-supervised loss function are calculated to obtain the training loss function, as shown in Equation (3):  
\[L(\theta,w,r)=-\sum\limits_{m=1}^{M}\sum\limits_{c=1}^{C_{b}}{y_{mc}\log p(y_ {m}=c|x_{m})}-\sum\limits_{m=1}^{M}\sum\limits_{r=1}^{4}{g_{mr}\log p(\hat{y} _{m}=r|x_{m})}, \tag{3}\]  
Figure 1: Network architecture of our proposed method. The input pairs that produce activations in the pre-trained teacher network produce similar activations in the student network. Global and local distillation bridges the gap between the feature representation of the student and teacher.  
where \(y_{mc}\) denotes the \(c\)-th element of the one-hot encoded vector of \(y_{m}\), and \(\hat{y}_{mr}\) denotes the \(c\)-th element of the one-hot encoded vector of \(\hat{y}_{m}\). Based on the loss function in Equation (3), the parameters of the teacher network are optimized to complete the pre-training process.
# Concentrating Solar Collectors for a Trigeneration System--A Comparative Study
###### Abstract  
The objective of this study is the investigation of different solar concentrating collectors for application in a trigeneration system. Parabolic trough collectors, linear Fresnel reflectors and solar dishes are the examined solar concentrating technologies in this work. The trigeneration unit includes an organic Rankine cycle coupled with an absorption heat machine that operates with LiBr/water. The analysis is performed throughout the year by using the weather data of Athens in Greece. The results of this work indicate that the selection of parabolic trough collectors is the best choice because it leads to the maximum yearly system energy efficiency of 64.40% and to the minimum simple payback period of 6.25 years. The second technology is the solar dish with the energy efficiency of 62.41% and the simple payback period of 6.95 years, while the linear Fresnel reflector is the less efficient technology with the energy efficiency of 35.78% and with a simple payback period of 10.92 years. Lastly, it must be stated that the thermodynamic investigation of the system is performed with a created model in Engineering Equation Solver, while the dynamic analysis is performed with a code in the programming language FORTRAN.  
concentrating collector; trigeneration; organic Rankine cycle; absorption chiller; linear Fresnel reflector +
Footnote †: journal: Applied Sciences
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis  
### Principle Verification Test  
To verify the principle of the proposed tunnel deformation monitoring technology, a tunnel deformation process was designed and simulated as shown in Figure 9.  
The image acquisition module was placed on a high-precision micro motion platform, which is a one-dimensional motion platform with a 1 \(\upmu\)m accuracy to enable the image acquisition module to move with the moving platform and simulate the deformation process of the tunnel. The laser transmitter module was arranged on the other side, with the laser pointing to the filter imaging screen. If the change in the coordinates of the laser spot center [35] in the image coordinate system is proportional to the tunnel deformation, then the correctness of the tunnel deformation monitoring principle is proved.  
In the experiment, the micro motion platform was moved at an interval of 5 mm, and the displacement of the micro motion platform and the corresponding abscissa values of the laser spot center in the image coordinate system in pixel units were recorded.  
The data relation curve is shown in Figure 10. In this curve, the \(x\)-axis is the displacement of the micro motion platform, and the \(y\)-axis is the measured displacement, namely the abscissa value of the laser spot center.  
According to the data relation curve, the displacement of the micro motion platform was linearly correlated to the measured displacement, and the linear correlation coefficient was close to 1, which demonstrates the correctness of the principle.  
Figure 10: Data relation curve.  
Figure 9: Sketch map of the principle verification experiment. 1. moving direction; 2. micro motion platform; 3. image acquisition module; 4. laser transmitter module.
# Cloning and Characterization of Two Iridoid Synthase Homologs from _Swertia Mussotii_
## 2 Results
### Functional Characterization and Substrate Preferences of Recombinant SmIS1 and SmIS2
The Km of SmIS1 for 8-oxogeranial was similar to the Km values previously reported for CrIS and OeIS, which are in the 1 uM range. However, the catalytic efficiency of SmIS1 was reduced compared with that of CrIS and OeIS. Compared with SmIS1, SmIS2 had lower affinity for 8-oxogeranial but higher affinity for NADPH.
## 6 The Framework
### General Architecture  
Our system architecture of the SCwA- CCS (Semantic-Based Context-aWare Agents for the Composition of IoT with Fog and Cloud Services) is a semantic multi-agent architecture that is composed of the following agents (as illustrated in Figure 9): Customer, Semantic Context Broker, Context Supervisor, Knowledge, and SCwA-CCS Agent.  
* **Customer**: The enterprise that wants a personalized IoT-ERP, which communicates with the system through GUI developed in Java.
* **Semantic Context Broker**: Acts as a semantic mediator between a customer and different service providers, with six kinds of agents interacting and collaborating to realize and manage concrete composition processes. The context broker is composed of the following four main agents: 1. **SCwA-CCS Agent**: Allows the customer to specify their requirements and receive their responses. This agent calls the following agents:
* _Virtual Business Process Planner_: Agent generates virtual business process. It is implemented using JESS as an inference engine and SWRL as an inference rule.  
Figure 9: An overview of the proposed approach.  
* _SCwA-IFCCS-3S_: Agent discovers IoT, fog, and Cloud services in terms of customer preferences and energy constraints.
* _ERP-CCS-3S Agent_: Finds the optimal concrete composite service.
* _Context Supervisor Agent_: Monitors context changes (_customer context, service context, IoT devices context, environment context, and usage context_).
* **ERP-CCS-3S Agent:** Generates an optimal business process in the form of a Concrete Composite Service (CCS) based on QoS and energy constraints, costumer's preferences, and description of IoT, Fog, and Cloud services.
* **SCwA-IFCCS-3S Agent:** Discovers local and Cloud services with respect to a customer's needs and energy constraints, and generates possible composite services. It calls _ERP-ICS_ and _ERP-FCS Agent_ for discovering IoT Fog services, respectively, and calls _ERP-CCS Agent_ for discovering Cloud services.
* **Context Supervisor Agent:** Monitors customer's context in real-time by reading the data from the sensors. It also enables service context monitoring (network connection, Cloud server load rate, available service provider, QoS, etc.) and the IoT context (availability, energy capacity, etc.) to provide the desired services for the customer.
* **Knowledge Agent:** Manages Cloud services and customer profiles that are defined by the user. It also ensures continuous updates of the ontology model.
* **Service Executor Agent:** Calls composite services and execution context management. It also takes over the management of IoT, Fog, and Cloud services.
* **The Services and Client Repository:** Made up of five sets of data descriptions: data from the sensor network, definition of IoT services for all smart interactive devices, definition of Fog service providers, and Cloud service providers, and a description of customer profiles using our ontology.
## 4 Discussion
### Areas where NDVI Decreased but WUE Increased  
The research found that, from 2001 to 2010, 8.34% of the area of the Loess Plateau had reduced vegetation coverage but increased WUE, mainly distributed in Baotou, Hohhot, Lanzhou, and Wuzhong in Inner Mongolia, and the vegetation type was mainly grass [30]. The area of this region rose to 11.28% between 2011 and 2020, gradually encroaching on the Loess Plateau's southeast.  
This is due to the high latitude, low temperature, and sufficient sunshine in this area, which is suitable for grassland growth [16; 31]. Compared with other vegetation types, grassland has less evapotranspiration, but sufficient precipitation renders the vegetation unable to absorb it [32]. Accordingly, the ET in this area decreases, while the GPP maintained an increasing trend.
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
###### Abstract  
The extraction of shale gas via horizontal drilling presents considerable challenges, primarily due to the accumulation of cuttings within the annular space, resulting in increased friction, torque, and potential drilling complications. To address this issue, the study proposes an experimental setup aimed at simulating cuttings transport under various operational conditions, with a particular emphasis on gas wells. The methodology encompasses the modulation of the drilling fluid flow rate and the drill's rotational speed to examine the transport velocity of cuttings. Furthermore, the study analyzes the impact of annular eccentricity on return volume, transport time, and cuttings bed height. Critical initiation velocities for cuttings across different motion modes were also determined, and theoretical calculations were compared with empirical data. The findings indicate that an increased flow rate of drilling fluid and higher rotation speed substantially improve the transport of cuttings, thereby minimizing bed formation, whereas increased eccentricity hinders this process. The results revealed that the theoretical model showed a greater overestimation of the start-up velocity for spherical particles, with average errors ranging from 15.50% to 17.56%. In contrast, the model exhibited better accuracy for non-spherical (flaky) particles, with errors between 8.63% and 9.61%. Under non-rotating conditions, the average error of the model was approximately 8.32%, while the introduction of drill tool rotation increased the average error to 11.94%. These results have the potential to optimize operational parameters in shale gas well drilling and may contribute to the development of specialized borehole purification tools.  
horizontal well; flaky cuttings; cuttings transport; annular eccentricity; non-spherical particles 2020 2024 14 9559 10.3390/app14209559
# A Survey of LoRaWAN for IoT: From Technology to Application
## 3 Applications and Deployments  
This section gives an overview of the current deployments and uses cases that make use of LoRa and LoRaWAN as communication protocol. Several application studies are presented and described in detail.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Key**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Required in Joining Type**} & \multicolumn{2}{c}{**Generated from**} \\  & & & **OTAA** & **ABP** \\ \hline \multicolumn{5}{c}{Keys needed after activation} \\ \hline \multirow{3}{*}{NwkSKey} & Is used to encrypt all MAC packets only and to calculate MIC of data packets & Yes & Yes & \begin{tabular}{c} Generated from AppKey and join-accept message \\ Generated from AppKey \\ and join-accept message \\ \end{tabular} \\  & Is used to encrypt/decrypt & Yes & Yes & \
\begin{tabular}{c} Generated from AppKey and join-accept message \\ Generated from AppKey \\ and join-accept message \\ \end{tabular} \\  & \multicolumn{3}{c}{Identifiers} \\ \hline AppEUI & 64-bit globally unique application ID & Yes & No & Stored beforehand \\ DevEUI & 64-bit globally unique device ID & Yes & No & Stored beforehand \\ by the network server & Yes & No & Stored beforehand \\ DevAddr & 32-bit unique device address & Yes & Yes &
\begin{tabular}{c} Received by \\ join-accept message \\ \end{tabular} \\  & in the current network & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: Over the air activation procedure according to lor [11].
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
## 6 Conclusions  
In this paper, we proposed a method for residual echo suppression considering harmonic distortion and temporal correlation. The proposed method estimates residual echo taking account of not only the linear echo estimates in the harmonically-related bins but also the linear echo estimates, residual echo estimates, and the microphone signals in adjacent frames. To utilize the microphone signal without the adverse effect of the near-end signals, the DTD module is utilized. Experimental results showed that the proposed method improved the ERLE of the HDRES in the far-end single talk period by 4.43 dB for the simulated data and 1.53 dB for the real-recorded data, and the PESQ scores of the HDRES in the double-talk periods by 0.051 for both simulated data and 0.049 for the real-recorded data, which may justify the increase of the computational complexity.  
Conceptualization, H.S. and J.W.S.; methodology, J.W.S.; software, H.S.; validation, J.W.S.; formal analysis, J.W.S.; investigation, H.S.; resources, J.W.S.; data curation, H.S.; writing--original draft preparation, H.S.; writing--review and editing, J.W.S.; visualization, H.S.; supervision, J.W.S.; project administration, J.W.S.; funding acquisition, J.W.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Research Foundation of Korea grant number NRF-2019R1A2C2089324 and Samsung System LSI (SLSI-201801 GE002S).  
The authors declare no conflict of interest.
## 2 Background  
### Vehicular Ad Hoc Networks (VANETs)  
Vehicular ad hoc networks (VANETs) are a special form of mobile ad hoc networks (MANETs) that enable communication between vehicles and roadside infrastructure without relying on a central management system. The main objective of VANETs is to improve road safety, traffic management, and the overall driving experience by facilitating communication between vehicles (vehicle-to-vehicle or V2V) and between vehicles and infrastructure (vehicle-to-infrastructure or V2I).