esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Design of Small-Aperture Optical Terminals for Free-Space Links
## 1 Introduction  
The use of optical frequencies for free-space signal transfer has become desirable in the pursuit of wireless high-speed communications and stable frequency transfer for metrology, where the bandwidth and stability of current radio- and microwave-frequency techniques are now major bottlenecks [1; 2]. Free-space optical (FSO) signal transfer generally consists of transmitter and receiver terminals separated by some amount of atmosphere, through which a laser beam propagates. The terminals will often be in relative motion, such as a link between a fixed ground station and an aircraft or sea vessel. Furthermore, atmospheric beam propagation is hindered by turbulence, which imparts deflections on the beam and perturbs its wavefront. These effects scale with frequency [3] and are detrimental to the applications of FSO links, stemming from the large power losses and fluctuations they cause, particularly when coupling the beam into optical fibre. FSO communications systems can employ a variety of techniques to overcome these power fluctuations, such as signal amplification [4], bit-rate adaptation [5], or FSO-tailored forward error correction codes [6]. Additionally, the magnitude of these power fluctuations themselves can be reduced by receiving the free-space beam through multimode or few-mode fibre. However, high-sensitivity and capacity phase-coherent data communications schemes require coupling the beam into single-mode fibre (SMF) [7]. This is also the case for coherent metrology. In these scenarios, the optical terminals used for FSO demonstrations must be carefully designed to minimise the SMF-coupled power fluctuations.  
Kilometre-scale, terrestrial links are often used to demonstrate cutting-edge FSO capabilities, or for 'last mile' access where laying optical fibre is impractical [8]. There have been a number of coherent FSO communications [9; 10; 11; 12] and metrology [1; 13; 14] demonstrations conducted in the literature. In practice, the optical terminals used for these demonstrations are not described in detail, nor necessarily optimised for the FSO link they are deployed on. It is common to see the same terminal design being used for a number of FSO demonstrations, such as in [10; 13] and [1; 15].  
This paper describes the optimal design of an SMF-coupled optical terminal for a range of FSO link lengths and turbulence conditions. An embedded, fast-steering tip/tilt(TT) mirror corrects for any angular offsets in the beam, providing first-order atmospheric turbulence suppression and fine target tracking. The design enables terrestrial FSO links of up to 10 km in a low complexity and cost-effective manner and is intended for sub-0.2 m diameter apertures.
### Remotely Sensing CDOM and \(S_{g}\)  
As interest in CDOM has grown in recent years, numerous empirical ocean color algorithms for retrieving CDOM within limited geographic regions have emerged, e.g., [25; 26; 27; 28; 29; 30]. A smaller number of more generally applicable, global empirical algorithms have also been developed, including one for retrieving a unitless index of CDOM prevalence, though it does not retrieve \(a_{g}(\lambda)\) or \(S_{g}\) and depends upon Case 1 assumptions. More recently, [PERSON] and [PERSON] published global empirical algorithms for both \(a_{g}(\lambda)\) and \(S_{g}\)[31; 32]. These were optimized and tested using field data aggregated in NOMAD (the NASA bio-Optical Marine Algorithm Dataset version 2 [33]) and the synthetic ocean color dataset developed by the International Ocean Colour Coordinating Group (IOCCG) for the purpose of algorithm development and validation [34].  
Other approaches to retrieving CDOM remotely depend on the premise that sea-surface reflectance is approximately inversely proportional to the total absorption coefficient [16; 35; 36], which can be linearly separated into various contributions by particulate and dissolved constituents. This forms the basis to semi-analytical ocean color algorithms (SAAs) for retrieving constituent absorption, e.g., [37; 38; 39], but, as already mentioned, owing to the similarity in spectral shape of non-algal particulate (i.e., detrital, microbial, and sedimentary) absorption and \(a_{g}(\lambda)\), SAAs generally retrieve only their sum, \(a_{dg}\)[17]. To circumvent this difficulty, empirical methods are sometimes added to SAAs to help distinguish non-algal from dissolved absorption [40; 41; 42; 43; 44; 45].
###### Abstract  
In the rehabilitation field, clinicians are continually struggling to assess improvements in patients following interventions. In this paper, we propose an approach to use gait analysis based on inertial motion capture (MoCap) to monitor individuals during rehabilitation. Gait is a cyclical movement that generates a sufficiently large data sample in each capture session to statistically compare two different sessions from a single patient. Using this crucial idea, 21 heterogeneous patients with hemiplegic spasticity were assessed using gait analysis before and after receiving treatment with botulinum toxin injections. Afterwards, the two sessions for each patient were compared using the magnitude-based decision statistical method. Due to the challenge of classifying changes in gait variables such as improvements or impairments, assessing each patient's progress required an interpretative process. After completing this process, we determined that 10 patients showed overall improvement, five patients showed overall impairment, and six patients did not show any overall change. Finally, the interpretation process was summarized by developing guidelines to aid in future assessments. In this manner, our approach provides graphical information about the patients' progress to assess improvement following intervention and to support decision-making. This research contributes to integrating MoCap-based gait analysis into rehabilitation.  
motion capture (MoCap); inertial measurement unit (IMU); clinical applicability; motion data analysis; patient-specific; patient-level; spasticity; hemipares
## 5 Analysis of HWMP Security Protocols
### Secure Hybrid Wireless Mesh Protocol (SHWMP)
#### 5.2.2 A Secure Scheme for Reactive Routing  
For the given scenario in subsection 5.2.1, SHWMP runs reactive secure routing mechanisms for RANN, PREQ, and PREP as follows:  
The node S is configured as root R. In the proactive RANN mode, the root node will broadcast RANN, as described in Equation (8), which is propagated to D via the intermediate nodes \(\mathrm{I_{1}}\) and \(\mathrm{I_{2}}\), as described in Equations (9) and (10). If D aims to create or update a path to root MP, it unicasts a PREQ frame with Equation (11). The subsequent message transmission along the path will follow Equations (12) and (13). Then, the corresponding PREP response will follow Equations (14-16).  
Proactive PREQ modes, including registration and non-registration, will handle the PREQ/PREP transmission in ways similar to those explained above.  
The security services provided by SHWMP are listed in Table 11. We highlight the security services that are not addressed. SHWMP concentrates on protecting HWMP frames from external attacks, but the protocol is not complete. Only the authentication and integrity of the MF is protected in point-to-point link. GTK is only valid between peering neighbors. The biggest flaw in SHWMP is that it does not provide any protection from internal attacks, nor does it provide the secure end-to-end authentication and integrity for broadcasting and unicasting messages. This protocol also does not have any security schemes for PERR HWMP routing protocol frames. This protocol does not provide the authentication and integrity assurance of NMF from the source node for the intermediate nodes on the transmission path to protect NMF modification attacks. In addition, an anti-replay scheme does not exist in this protocol.
## 3 Proposed Research Methodology
### Models  
Four classification methods were employed: Gradient Boost, XGBoost, logistic regression, and random forest. These are commonly employed in the literature [17; 38; 39; 41; 55] and therefore are reasonable reference points for the purposes of the study, removing the choice of model as a confounding variable when comparing results to those of similar studies. They are also simple, interpretable models that have been demonstrated to scale well to both large datasets and small (which this study is).
## 3 Methodology
### Impedance Control
In our experiments the human intent is interpreted via the force measurements read by the FT sensor, while staying comfortable and intuitive for the human operator. When no assistance strategy is required and we want the robot to freely follow the motion of the human, we can set \(K_{d}=0\) or \(x=x_{d}\). This \"direct teaching mode\" is essentially based on the elimination of the spring component of the robot impedance model (\"minimum impedance\" strategy in Section 5.4) and avoids any restoring forces. If the human holds the tool rigidly then we can assume that the forces are transmitted completely to the robot. If the hand is close enough to the end-effector, we can assume that their positions, velocities and forces are equivalent: \(x_{d}=x_{hd},\,\dot{x}_{h}=\dot{x}\) and \(\ddot{x}_{h}=\ddot{x}\). In admittance control (popularly called position-based impedance control) the input is the force applied by the environment and the output is displacement/velocity as shown in Figure 3. In pHRI tasks, the force is applied by the human on the tool at the end-effector (\(F_{ext}=F_{h}\)) and it is measured by the FT sensor. The difference between the desired force for the robot (\(F_{d}=F_{r}\)) and this external force is applied as input to the impedance model in (6) in order to obtain the desired position for the end-effector \(x_{d}\) as output:  
\[M_{d}(\hat{x}_{d}-\bar{x})+B_{d}(\hat{x}_{d}-\hat{x})+K_{d}(x_{d}-x)=F_{d}-F_{h} \tag{8}\]  
On the contrary, the impedance control (torque-based impedance control) has as input the end-effector displacement/velocity and the desired force as output, as shown in Figure 4. In this case, the desired robot impedance model is the same as in (6) but the difference between the desired robot force and the external/human force is transformed into joint torques for the robot control.
## 4 Discussion
Safety applications could also incorporate a more fluid warning system, such as through a series of proximity alerts that indicate increasing levels of danger associated with proximity to one or more hazards.
## 3 System Model
As for the downlink transmission phase, users distribute tasks through the gateway node, and evenly assign monitoring tasks and dispense resources in the network. In other words, the gateway node first assigns tasks to each cluster head node, and the cluster head node continues to assign tasks to the sensing nodes to complete the monitoring task. The communication energy expenditure of AUSCNs' sensors mainly includes the energy expenditure of transmitting circuit energy, the signal amplification circuit and the receiving circuit. Of course, the transmitting part consumes the most energy, and the larger the amount of data transmitted, the greater the energy expenditure required for transmission. As far as the transmission part is concerned, the signal amplification circuit belongs to power amplification, which consumes the most energy. According to the instructions of the controller, the power is amplified to the required power level and ensures an effective transmission distance. It concerns whether the basic unit bit reception energy expenditure of the receiving circuit remains unchanged. Thus, the more data that are received, the more energy that is consumed. A model of the nodes' communication energy consumption is shown in Figure 2. Figure 2 includes a transmission model and a reception model, where d is the transmission distance. Table 2 shows the system model parameter description. Figure 1: Network clustering structure of AUSCNs. The energy consumption model consists of a transmitting module and a receiving module. \(E_{T}(k,\,d)\) indicates the energy consumed of the transmitter when the sensor node sends k-bit data and the transmission distance is d, where \(t\) is the communication environment parameter. Usually, it ranges from two to four, according to the environment. The energy consumption formula is able to be expressed as follows:  
\[E_{T}(k,d)=kE_{elec}+n\varepsilon_{fs}d^{t} \tag{1}\]  
The energy consumption formula of the n-bit data received by the ordinary nodes is  
\[E_{R}=kE_{elec} \tag{2}\]  
The formula of the total energy expenditure of the nodes in the network is  
\[E_{All}=E_{Tx}(k,d)+E_{Rx} \tag{3}\]  
where \(E\_{(elec)}\) represents the electronic energy parameter of the transmission circuit and the receiving circuit, which is related to coding, modulation, filtering, etc. \(\varepsilon_{fs}\) is the energy amplification parameter in the process of information transmission. According to different communication environments, the value of \(t\) is generally between two and four. The worse the communication environment is, the higher the value of t is.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 5 Conclusions  
This paper proposed a protocol stack that is suitable for a space-based optical backbone network. Then, we used software to simulate this protocol stack, built a hardware platform to test it, and finally analyzed the results.  
The results showed that the proposed protocol stack was well designed to provide efficient control and management for the space-based optical backbone network. It could improve the management efficiency by collecting resources, calculating and building the route path automatically. It could also facilitate data forwarding in the intermediate satellite nodes with limited source and power, by using the AOS frame switching scheme to avoid unnecessary processes, such as unpacking, upper-layer processing and repacking for passing services. Additionally, it supported the use of unidirectional links to improve the link resource utilization. Finally, it could also provide transparent transmission for different kinds of services.  
Conceptualization, Y.Z. and Y.Y.; methodology, Y.Z. and B.G.; software, M.J., Y.W., M.F. and Y.L.; validation, Q.L., B.Z. and W.Z.; resources, B.W. and S.H.; writing--original draft preparation, Y.Z.; writing--review and editing, B.G.; supervision, B.G.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by National Key R&D Program of China (2018 YFB1801702); NSF of China (61622102, 61771074); State Key Laboratory of Advanced Optical Communication Systems Networks.  
Not applicable.  
We acknowledge the support given by [PERSON], [PERSON] and [PERSON] during the project.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 27: Service transmission demonstration.
## 1 Introduction
Such randomness caused by shadowing effects should be taken into account in the MAC protocol design to avoid potential collisions and leverage spacial reuse. Motivated by these observations, in this paper we study the problem of how to mitigate the exposed terminal problem in the presence of log-normal shadowing channels. We propose a location-assisted extension to the IEEE 802.11 MAC protocol for opportunistically scheduling \"concurrent\" transmissions in the neighborhood of a \"free\" transmission, i.e., the transmission between the two nodes that first win the channel with RTS/CTS handshake. We assume node location information as in many prior works (e.g., the class of geographic routing protocols [17, 18]). We assume that such location information can be obtained via the global positioning system (GPS) if such service is available, or by using an effective localization scheme proposed in the literature [19]. However, our main objective is to exploit location information for improved network-wide performance, while localization is not the focus of this paper. We first derive an analysis on the success probability of concurrent transmissions from exposed nodes, which depends on i) the distance between the tagged transmitter and receiver, ii) the distance between the receiver and other interfering nodes, iii) the path-loss exponent, and iv) the parameters of log-normal shadowing. If the computed success probability is larger than a prescribed threshold, concurrent transmissions of exposed nodes are allowed by the proposed scheme. Furthermore, we develop an extension to the IEEE 802.11 MAC to incorporate the above analysis for validating the feasibility of a concurrent transmission, and for scheduling feasible concurrent transmissions. We also describe a simple scheme to estimate the channel parameters if they are not known a priori or if the channels are not stationary. Finally, we implement the proposed location-assisted MAC protocol in ns-2 and compare its performance with the original IEEE 802.11 MAC with extensive simulation studies. We observe considerable gains in throughput and delay achieved by the proposed MAC protocol over IEEE 802.11 MAC, which not only demonstrate the efficacy of the proposed scheme, but also justify the importance of considering channel randomness in MAC protocol design. The remainder of this paper is organized as follows. In Section 2, we review related work on improving the IEEE 802.11 MAC performance. In Section 3, we discuss the shadowing channel model and success probability of a concurrent transmission. We present the location-assisted MAC extension in Section 4 and evaluate its performance with extensive ns-2 simulations in Section 5. Section 6 concludes this paper.
## 3 RQs and Objectives
### SRQs by Development Stage  
To support the initial prototype of the R&D agenda, this study introduces a set of exploratory SRQs. Each SRQ corresponds to one of the agenda's four development stages, focusing on early insights that guide refinement and future studies.  
Stage 1: AI Agent Development  
* **SRQ1.1: What are the specific design requirements and methodologies for the AI agent to meet consulting and capstone needs?**
* **SRQ1.2: In what ways can the AI agent enhance consulting productivity and accessibility, and what roles does it serve?**
* **SRQ1.3: How will the performance and impact of the AI agents be evaluated in cybersecurity consulting contexts, specifically looking at metrics such as accuracy and user satisfaction, among others, as examples of key indicators?**  
Stage 2: Cybersecurity Roadmap Creation  
* **SRQ2.1: How does the agenda structure the process for creating customised cybersecurity roadmaps for small businesses?**
* **SRQ2.2: How does the roadmap align with both educational standards and industry requirements?**  
Stage 3: Resource Development  
* **SRQ3.1: What design principles guide the creation of consulting tools, templates, and training programs within the agenda?**
* **SRQ3.2: How do iterative feedback and human-computer collaboration enhance the usability and effectiveness of consulting resources?**  
Stage 4: Industry Application  
* **SRQ4.1: What insights are gained from applying the R&D agenda's resources in real-world industry settings, and how effective are they?**
* **SRQ4.2: What lessons can be learned from applying the consulting resources with small business clients?**These SRQs are intended as exploratory questions to capture initial findings from each stage. Each SRQ has the potential to evolve into a main RQ for future research, allowing for deeper, focused studies that will further validate and refine the R&D agenda.
## 1 Introduction  
Absolute distance measurement systems, which measure several tens of meters with microns uncertainties, are of significant interest in the field of metrology [1; 2; 3; 4]. With the improvement of the external cavity laser (ECL), systems using frequency scanning interferometry FSI and the ECL play an important role in these fields: absolute distance measurement [5; 6; 7; 8], imperfection detection [9], optical coherence tomography (OCT) [10], and three-dimensional surface profiling [11]. In our paper, attention is focused on absolute distance measurement.  
An FSI-based absolute distance measurement system sends out a frequency-modulated signal to an object and receives the backscattering signal. The output is a low-frequency cosine wave in time domain that is affected by the tuning nonlinearity of the ECL [12; 13]. Hence, in order to obtain precision in the order of microns, it is important for the ECL to provide linear frequency scanning over a broad tuning bandwidth [14]. However, because of the hysteresis and creep of the piezoelectric actuator (PZT), the ECL often shows tuning nonlinearity in practical situations. To eliminate the influence of the nonlinear tuning, many methods had been proposed [15; 16; 17; 18; 19].  
In our paper, we sample the measurement interference signal at equidistant optical frequency points (EIOFs, namely sampling points) of the auxiliary interferometer with a long single mode polarization-maintaining fiber to eliminate the influence of tuning nonlinearity. This method is a common and effective method, but it creates a dispersion mismatch problem that results in target peak broadening. Fortunately, many dispersion compensation methods had been proposed and proved efficiently [20; 21; 22]. In our experiments without cooperate targets, we use an ECL that has been used for five years, so the SNR is low, and mode-hopping signals often occur. These bring new problems: erroneous sampling points and interfering signals. Our paper analyzes the influences of erroneous sampling points and interfering signals, and then an adaptive filtering method is proposed to eliminate the influences completely. In addition, a phase-matching mosaic algorithm and a segmentation mosaic algorithm are adopted to eliminate the phase jump and improve the data processing speed, respectively.  
The article is organized as follows: Section 2 reviews the principle of the FSI-based system and discusses the measurement errors caused by erroneous sampling points and interfering signals. Then, an adaptive filtering method, a phase-matching mosaic algorithm, and a segmentation mosaic algorithm are proposed to eliminate the erroneous sampling points and phase jump and reduce the data processing time. In Sections 3 and 4, the results of the simulation and experiment prove the efficiency of the methods that were proposed in Section 2. Finally, a brief conclusion is given in Section 5.