esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Experimental Results for CBA Algorithm  
Initial experimentation was required to find a good confidence value \(\delta\). These experiments used a high arrival rate of jobs, though it is possible to repeat these experiments for other arrival rates if desired. A few values were tested in the range \([0.01,0.95]\), however, the search for the optimal value was purposely shallow. This was due to the possibility that the search is not possible for systems implementing a similar approach in the future; instead, the experiments were designed to find a rough approximation which was more generalized (i.e., how optimistic should similar algorithms be for best results). The results of the parameter optimization are shown in Figure 4. The results of using different \(\delta\) values were compared to a baseline of \(\delta=1\) which implied complete certainty in the recorded job averages as \(\log{(1)}=0\), so \(\hat{\mu}_{i}=\hat{r}_{i}\). The graph shows that a high confidence level (0.95) was the most successful which implies there was little variation in the data and, therefore, the true mean was not plausibly significantly higher than previous readings. However, experiments using \(\delta\leq 0.75\) performed worse than \(\delta=1\) which shows that it was better to be slightly overconfident in previous results than to be very optimistic in higher possible mean reward. Optimizing the parameter further is considered a secondary objective to the problem addressed by this paper, as such further experimentation in this area is recommended for systems with different assumptions which may affect reward distributions.  
To evaluate the scheduling algorithm in different situations, the arrival rate of jobs was adjusted to realize larger periods of time with high/low activity compared to the small periods as described in Section 3.2. The small periods of time realized as nights and weekends were kept the same length for consistency; the scheduler must handle a higher number of jobs in the same amount of time. In total, 50 days and nights were simulated of which 14 days were weekends. In a single day, the number of jobs which arrive depends on the arrival rate: 20 jobs arrived at the high rate, 12.5 jobs (on average) arrived at the medium rate, and eight jobs arrived at the low rate. In these experiments, we considered the \(Val_{total}/Energy_{total}\) recordings of the previous experiments and also the percentage of jobs which provided no value to the customer: either they finished computation after computation or are rejected due to \(\hat{r}_{i}=0\).
## 3 Results
### SEM/EDX Microstructural Analysis  
Micrographs of the C-S-H gels in water-soaked pastes are reproduced in Figure 8a-c. The EDX analysis (ten analyses/gel) showed that the Ca/Si ratio in the OPC gel was 2.0, compared to 1.7 in M1 and 1.9 in M2. The findings also showed that adding SCMs induced aluminium uptake in the gel, as reported earlier by other authors who used pozzolanic additions to partially replace clinker [22; 24; 36; 57]. Inasmuch as the aluminium may have replaced Si in the gel structure, those findings would explain the lower Ca/Si ratio in the pastes bearing SCMs. Compact portlandite plates were observed in all the pastes analysed, although less frequently and in smaller mean sizes in paste M1 and M2 than in OPC, due to the pozzolanicity of the former. Those findings were consistent with the XRD data.  
Magnesium in the form of brucite predominated in the surface layer observed to form in all the 56-d pastes exposed to seawater (Figure 9a-c), corroborating the XRD findings. Brucite, the result of the reaction between portlandite and the MgCl\({}_{2}\) in seawater, was present on specimen surfaces only. The layer was thicker in the OPC (e = 107.1 \(\upmu\)m) than in blend M1 (e = 82.4 \(\upmu\)m) or M2 (e = 83.5 \(\upmu\)m). [PERSON]'s salt formation was also observed in seawater-soaked pastes OPC, M1 and M2, corroborating the XRD findings (Section 3.3).  
Figure 8: Calcium silicate hydrates (C-S-H gels) in 56-d water-soaked pastes: (**a**) OPC (\(\times\)6000); (**b**) M1 (\(\times\)6000); (**c**) M2 (\(\times\)10,000).  
Plates identified as [PERSON]'s salt on the grounds of composition (Al, Cl and Ca) and morphology [60,65] were observed in the 56-d pastes exposed to 0.5 M NaCl (Figure 10).  
The pastes exposed to the 0.3 M Na\({}_{2}\)SO\({}_{4}\) solution were observed to form gypsum deposits clustering primarily in pores (Figure 11a), where the conditions for nucleation were most favourable [66,67]. Typically elongated needle-like ettrinigite (Figure 11b) was also identified [68,69], primarily inside pores [70], favoured by pressure conditions and the presence of the necessary ions [71,72]. For those very reasons, pores, together with cracks, are the sites where ettrin  
Figure 11: (a) Gyspum deposits (\(\times\)7000) and (b) needle-like ettrinigite (\(\times\)5000).  
Figure 9: Brucite formation in 56-d seawater-soaked pastes: (a) OPC (\(\times\)200); (b) M1 (\(\times\)200); (c) M2 (\(\times\)250).
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 4 Simulation  
### Simulation Setup  
To validate the proposed methods, we constructed a simulator modeling a Kanpei orchard similar to real-world conditions. The simulator was built using GAZEBO and ROS kinetic on Ubuntu 16.04, running on a system with a 64-bit Intel(R) Core(TM) i7-7700K CPU running at 4.20 GHz. All The algorithms were implemented in Python.  
The mobile manipulator consisted of Robotis' RH-P12 gripper mounted on an Open-Manipulator pro, which was placed on Clearpath Robotics' Ridgeback UGV equipped with Omni-directional wheels, as shown in Figure 13. The Kanpei trees were strategically placed inside obstacles formed by other trees to complicate the approach for the mobile manipulator. The fruits were modeled to resemble their real counterparts, with a diameter of approximately 10 cm. The modeling of Kanpei trees and the orchard is illustrated in Figure 14.  
It was assumed that the positions of all the trees and fruits were known through orchard mapping via a vision system. The harvesting scenario has two parts: First, the closest three fruits to the initial position of the robot were selected for harvesting. Fixed positions of UGV and the orientation for approaching a target fruit of the end-effector for the target fruits were selected using IRM. Then, the UGV path to the selected position and harvesting paths of the manipulator for each fruit from that position were generated using MPAPF and executed. Second, the same procedure was repeated for harvesting three fruits placed behind trees. Harvesting paths of the manipulator were generated only from initial basket placement to fruits and repeated to return to the initial in reverse.  
Figure 14: Simulator model of orchard and kanpei.  
Figure 13: Mobile manipulator;Ridgeback UGV with Open –Manipulator pro and RH –P12 gripper.
## 1 Introduction  
Cloud computing, despite its widespread adoption, struggles to satisfy the latency and bandwidth demands of IoT applications, necessitating the integration of edge and fog computing to complement cloud capabilities [1, 2]. These paradigms, by situating computation closer to data at the network's edge, address latency and bandwidth issues and foster a cooperative dynamic among edge, fog, and cloud layers [3, 4]. In this synergy, the device-enhanced edge model enables utilising idle computational resources of IoT devices themselves [5]. This approach not only alleviates the pressure on traditional edge servers but also promotes flexibility and resource efficiency. But the deployment of a cooperative device-enhanced edge-fog-cloud architecture presents significant challenges, including task allocation across heterogeneous resources, dynamic node availability, and maintaining Quality of Service (QoS).  
Building upon the Honeybee model [6], we propose an integrated architecture for coordinating resources across edge, fog, and cloud tiers, and empirically evaluate its efficacy. This paper aims to investigate the following research questions within this architecture:  
* RQ1: How does node collaboration across edge, fog, and cloud layers impact overall task performance?* RQ2: How does adjusting task-sharing parameters affect the system's behaviour?
* RQ3: How can dynamic node availability, where heterogeneous nodes may leave or join without prior warning, be handled in a cooperative edge, fog, cloud setting?  
Our contributions include a conceptual architecture for dynamic edge-fog-cloud collaboration, the development of a real-world prototype, and an empirical evaluation demonstrating the approach's effectiveness in a physical testbed.  
The paper is organised as follows: Section 2 outlines the background and motivation for our architectural approach. Section 3 reviews related work in computation offloading. Section 4 details the Honeybee architecture and its enhancements. Section 5 presents our experimental evaluation, followed by conclusions and future work directions in Section 7.
# Phase Center Corrections for BDS IGSO and MEO Satellites in IGb14 and IGSR3 Frame
## 4 Results and Validation
### Validation
#### 4.4.1 Orbit Consistency  
Orbit boundary disclosure (OBD) is used for the evaluation of the orbit consistency. The average 3D RMS of the BDS-3 OBD for each satellite with different antenna models are shown in Figure 6. It is clear to see that the OBDs of BDS satellites have been reduced by using the two kinds of calibrated PCO and PCV estimates, i.e., F14 and FR3, compared to that of using CSNO released values. Moreover, the discrepancy of OBDs between F14 and FR3 is marginal (only 4 mm on average), as expected, as the PCO shifts along radial direction have been assimilated by the clock parameters.  
We also observed that the OBDs were reduced by about 55.0% from 176.6 mm to 79.5 mm for BDS-2 MEO, whereas the reduction for BDS-2 IGSO was about 21.7% from 356.0 to 278.9 mm. With respect to the approximate 13.2% reduction for BDS-3 CAST MEO satellites, the OBDs were reduced by 30.9% for BDS-3 SECM satellites, making 3D OBD achieve around 90.0 mm for both kinds of MEO satellites. In addition, we also observed that the OBD reduction was smaller for C35-C37 than that of others, possibly due to fewer ground stations available in the process of estimating PCC and precise orbit determination.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
###### Abstract  
This study proposes a risk measurement approach to assess collision risks in mixed traffic flow, focusing on the integrated behavior of car-following and lane-changing. A new surrogate safety measure (SSM), denoted as \(R_{\text{total}}\), is developed to provide a comprehensive risk assessment. Numerical analysis is used to determine the weights of parameters within \(R_{\text{total}}\), and its validity is substantiated using an empirical dataset, with a risk threshold of 0.49 established when the time to collision (TTC) is set to 2 s. The study incorporates scenarios of connected and automated vehicle (CAV) degradation and evaluates the influence of penetration rates, perception-reaction time (PRT), and lane-changing modes on risk levels. Simulation results reveal that a CAV penetration rate between 0.4 and 0.6 represents a critical range where collision risks significantly increase, reflecting safety dynamics under CAV degradation. Furthermore, in scenarios involving lane-changing, the degradation of the following vehicle in the target lane poses the highest risk. At lower PRTs, the penetration rate exerts a more significant influence on collision risks. \(R_{\text{total}}\) has been validated across various scenarios, showing strong applicability and more sensitive trends than other SSMs, making it well-suited for assessing long-term comprehensive traffic flow risks. These findings offer practical guidance for traffic management to establish real-time risk prediction and warning systems for identifying high-risk car-following and lane-changing behaviors. Future research can explore the applicability of the proposed risk index in more complex traffic scenarios and its effectiveness across different levels of vehicle automation and connectivity.  
mixed traffic flow; surrogate safety measures; degradation of CAV; perception-reaction time; driving behavior +
Footnote †: journal: applied sciences
# Measurements of Room Acoustic and Thermo-Hygrometric Parameters--A Case Study +
## 1 Introduction
Among the important research works are the following: In the context of voice alarm systems, the role of temperature and humidity in sound reproduction systems was discussed by [PERSON] et al. [16], who showed that high-frequency reverberation time was positively correlated with changes in temperature and humidity and was negatively correlated with speech-related parameters (e.g., STI). Air absorption effects in outdoor noise transmission were analysed in the ISO 9613-1 [17] standard, where direct correlations with temperature, relative humidity, and static pressure were assessed. However, this did not provide a method by which these effects can be evaluated. This article explores the effects of varying temperature, relative humidity, and air velocity on acoustic parameters through an experimental activity carried out in a practice room. A statistical analysis was carried out using R to obtain relationships between changes in acoustic parameters and changes in thermal and humidity variables.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 3 Location-Based Deferred Broadcast Scheme
### LDBD Broadcast Algorithms  
The broadcast issue of wireless links in ad-hoc NDN is handled using an intermediate layer between NDN network layer and MAC layer via _network face over ad-hoc_. The purpose of using this layer is to enhance the functionalities of NDN layer by adjusting ad-hoc scenario without changing default NDN forwarding behavior. The intermediate layer, called Link Adaptation Layer (LAL) is illustrated in Figure 3, where the proposed intelligent broadcast scheme LBDB is implemented. Both interest and data packets will be processed by Link Adaptation Layer.  
The Link Adaptation Layer interfaces with NDN network layer via the NDN data structures of CS and PIT. Different from default wired NDN, in ad-hoc NDN, FIB is not used because the interest is forwarded by broadcasting instead of using routing information. The Link Adaptation Layer maintains two additional queues, namely, _Interest Tx Queue_ and _Data Tx Queue_. The two queues are used to conduct deferred broadcast by buffering the pending interest and data chunks that are waiting to be passing to the MAC layer for transmissions.  
Figure 3: Ad-Hoc Named Data Networking (NDN) Architecture.  
Figure 2: Rebroadcast Forwarding Priority.  
The workflows of the LBDB algorithm for handling interest and data packets are given in Figure 4. The left of Figure 4 shows the steps in forwarding an interest. After a node receives an interest, it will first check its _Interest Tx Queue_ by matching the data name carried in the interest packet. If a matched entry is found in _Interest Tx Queue_, it indicates that the same interest has been received earlier and is waiting to be rebroadcast by this node. Receiving the same interest again means the interest is now being broadcasted by another node with a higher forwarding priority. As such, this node will discard the received interest. If a matching entry is not found in the _Interest Tx Queue_, _Data Tx Queue_ will be checked by matching the data name. If a matching entry is found in the _Data Tx Queue_, it indicates that the requested data has been received by this node and is waiting to be broadcast (note, this is because data is sent via broadcast in ad-hoc NDN). As such, forwarding the received interest is no longer necessary. The interest is then dropped. If a matching entry is not found in the _Data Tx Queue_, the interest is passed to the upper layer. Following native NDN forwarding process, PIT and CS will be checked. The data forwarding workflow is similar. It is shown in the right of Figure 4. These steps show the difference in processing an interest or a data packet before actually sending them between the native NDN and the ad-hoc NDN, that is, adding them to interest or _Data Tx Queue_ and waiting for additional time.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 3 The Threshold-Based OSD Decoding Algorithm
```
1:\(C_{min}\gets C_{0}\)\(\triangleright\)\(C_{0}\) is the initial codeword
2:\(f_{min}\gets f_{0}\)\(\triangleright\)\(f_{0}\) is the discrepancy of \(C_{0}\)
3:\(C_{final}\gets null\)
4:if\(f_{min}\leq f_{th}\)then
5:\(C_{final}\gets C_{min}\)
6:return\(C_{final}\)
7:else
8:\(i\gets K\)
9:while\(i\geq 0\) and \(|Y_{i}|\leq f_{min}\)do
10: Flip bit i, form a new codeword \(C_{cur}\)
11:\(f_{cur}\leftarrow\)disCaI\((C_{cur})\)\(\triangleright\)disCaI\((\mathbf{C})\) calculate the discrepancy of \(C\)
12:if\(F_{cur}\leq f_{th}\)then
13:\(C_{final}\gets C_{cur}\)
14:return\(C_{final}\)
15:endif
16:\(f_{min}\leftarrow\)min\((f_{min},f_{cur})\)
17:\(i\gets i-1\)
18:endwhile
19:\(i\gets K\)
20:while\(i\geq 1\)do
21:\(j\gets i-1\)
22:while\(j\geq 0\) and \(|Y_{i}|+|Y_{j}|\leq f_{min}\)do
23: Flip bit i and bit j, form a new codeword
24:if\(f_{cur}\leq f_{th}\)then
25:\(C_{final}\gets C_{cur}\)
26:return\(C_{final}\)
27:endif
28:\(f_{min}\leftarrow\)min\((f_{min},f_{cur})\)
29:\(j\gets j-1\)
30:endwhile
31:\(i\gets i-1\)
32:endwhile
33:endif
34:\(C_{final}\gets C_{init}\)
35:return\(C_{final}\)
```  
**Algorithm 1** Flipping process. We note that, when calculating \(f\) for a new codeword \(X^{N}\) formed by flipping one or two bits of \(\overline{X}^{K}_{1}\) in the algorithm, there is no need to perform the matrix multiplication \(X^{K}_{1}G_{2}\) to obtain \(X^{N}\). Rather,we use \(\overline{X}^{N}\oplus G_{2}(flipPos,:)\) to obtain \(X^{N}\) because this process involves binary vector addition with \(flipPos\) containing the indices of the flipped bits. Since this step contributes to the main computation complexity of the whole algorithm, changing from matrix multiplication to vector addition speeds up the decoding process.
# Investigation into the Yaw Control of a Twin-Rotor 10 MW Wind Turbine
## 3 WT Yaw Mechanism
### Electrical Motor Mathematical Model  
DC electrical motors that provide feedback signal control are called servomotors; they respond quickly and have precise angular positions. The back EMF (\(V_{e}\)) induced in the armature circuit by the permanent magnet can be calculated as follows [40]:  
\[V_{e}=K_{e}\cdot\omega=K_{e}\cdot\dot{\theta} \tag{12}\]  
The produced torque from the motor is directly proportional to the motor current, as follows:  
\[T=K_{t}\cdot i(t) \tag{13}\]  
Figure 3: WT mechanical system.  
Assuming no mechanical or electromechanical losses, the mechanical and electrical power are equal, as follows:  
\[T\cdot\omega=V_{e}\cdot i(t) \tag{14}\]  
\[\therefore\,\,\,K_{t}\cdot i(t)\cdot\omega=K_{e}\cdot\omega\cdot i(t)\,\,\to\,\,K_{t} =K_{e}=K \tag{15}\]  
By applying [PERSON]'s law voltage,  
\[V=V_{e}+R\cdot i(t)+L\cdot\frac{di(t)}{dt} \tag{16}\]  
the following can be substituted into Equation (12):  
\[V=K\cdot\omega+R\cdot i(t)+L\cdot\frac{di(t)}{dt} \tag{17}\]  
Laplace transform is applied to obtain the armature current in the S-domain:  
\[V(s)-K\cdot\omega(s)=(Ls+R)\cdot I(s) \tag{18}\]  
\[\therefore\,\,\,I(s)=V(s)-K\cdot\omega(s)\,\,\frac{1}{(Ls+R)} \tag{19}\]  
To calculate the motor's torque, [PERSON]'s second law can be applied as follows:  
\[T=J_{m}\cdot\ddot{\theta}+b\cdot\dot{\theta}+T_{Load} \tag{20}\]  
Laplace transform is applied as follows:  
\[T(s)=J_{m}\cdot s^{2}\cdot\theta(s)+B\cdot s\cdot\theta(s)+T_{Load} \tag{21}\]  
\[\therefore\,\,\theta(s)=(T(s)-T_{Load})\,\,\frac{1}{(Js+B)}\frac{1}{s} \tag{22}\]  
The input signal to the DC motor mode is volt \(V(s)\), and the output is the angular position \(\theta(s)\). Hence, the open loop transfer function of the DC motor with no load is as follows:  
\[\frac{\theta(s)}{V(s)}=\frac{K}{s((Ls+R)(Js+B)\cdot K^{2})} \tag{23}\]  
For angular position control of the DC motor, a closed loop with feedback and a controller law are required. Figure 4 shows a simplified block diagram model. The motor's response was tested by applying step input.  
As mentioned above, the motor used in this work was a separately excited DC motor, selected due to its high starting torque and variable speed drive capability. Four separately excited DC motors with a gear ratio of 1:60 were used to overcome the load torque resulting from the different thrust levels on the tower top and to control the position of the WT's side booms. The motors were selected with capacity to cover the torque required to overcome the twisting moment induced by the difference in thrust. Table 2 shows the DC motor specifications [39].  
Figure 4: Simplified DC motor closed-loop TF.
## 4 Discussion  
The improvement in spectral efficiency in next-generation MLR-type systems is very important, because, by increasing said efficiency in a channel, it is possible to represent the same amount of information in a lower bandwidth, or more information in the same width of belt, without the need to require expensive equipment to offer long distances or, failing that, a greater number of regenerators, which translates into expenses. For a transmission system, the highest spectral efficiency channel for a given bandwidth and an optical signal-to-noise ratio (OSNR) is governed by the Shannon limit, below which reliable transmission is not possible for any transmission rate, since infinite signal quality cannot be provided with respect to the noise floor [6].  
The importance of robust modulation formats, such as DPSK, is highlighted in the face of noise phenomena and attenuation losses, where these degradations are not significant enough to affect the performance of the system, which, when combined with formats such as RZ-OOK, can double the transmission capacity without incurring a power penalty due to the effect of chromatic dispersion and PMD, therefore it is widely used to transmit these high bit rates. Higher order modulation formats and super spectral channels are key to maximizing spectral efficiency in next-generation optical transport networks. A flexible DWDM grid is key to efficiently accommodate media channels that require approximately 50 GHz of contiguous spectrum [16].  
If you want to use MLR networks in a system, it is recommended to transmit channels of 10 Gbps and 40 Gbps, due to the fact that they have a greater number of bits transmitted in a certain bandwidth, indicating a good performance of the spectral density of the information, translating into spectrally efficient systems.  
In previous works, the analysis of homogeneous networks was carried out where the spectral efficiency is lower than that obtained in the heterogeneous network studied in this article. For example, to transmit a throughput of 40 Gbps using 2.5 Gbps channels, you need at least 380.18 GHz and only have a spectral efficiency of 10.52%. To transmit the same throughput, you need four channels of 10 Gbps, 187.25 GHz of spectrum, and then an efficiency of 21.36% is obtained; and with a single 40 Gbps channel they occupy 138 GHz reaching an efficiency of 28.99%.