esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing  
#### 2.4.1 Definition of Weather Conditions  
Previous studies have pointed out that the relationship between chlorophyll fluorescence and photosynthesis differs between sunny and cloudy weather conditions; we intended to compare the fluorescence-photosynthetic relationship in the two conditions. We adopted the clearness index (CI) during the study period to distinguish the weather conditions [50]. CI was defined at the ratio of the radiation received on the ground to the theoretical radiation on the top of atmosphere at the same time. Weather conditions were differentiated using CI, calculated as  
\[\mathrm{R_{0}=S_{0}\times(1+0.033\times cos\left(2\pi\times\mathrm{DOY}/365 \right))\times cos\left(SZA\right)} \tag{3}\]  
\[\mathrm{CI=R_{g}/R_{0}} \tag{4}\]  
where S\({}_{0}\) represents the solar constant (1367 W/m\({}^{2}\)), DOY is day of year, SZA indicates the solar zenith angle, R\({}_{g}\) is the solar radiation received on the surface of the region, R\({}_{0}\) represents the theoretical radiation at the top of the atmosphere [21]. We applied the ratio threshold of 0.5 to distinguish sunny and cloudy days in this research [21]. When the daily mean CI is greater than 0.5, the day is defined as sunny and cloudy days are otherwise.
## 4 Experimental Validation of the Method
### Results
#### 4.3.2 Collaborative Trajectory  
Considering the collaborative operation and the fact that it can be seen as a sequence of two movements, two Pareto fronts were computed, and the results are presented in Figure 6. The obtained trajectories (in particular, the min-jerk one) were verified taking into account the speed limits, i.e., Table 1, and a danger field (37) given by the volume with \(y<-10\) cm. Thanks to these, the user can select the proper weight according to the needs and kind of movement and give to the robot controller the command for starting the execution of the chosen optimal trajectory.  
Figure 5: Numerical results for the benchmark zigzag trajectory in the joints’ space: (red) trajectory for \(\alpha\to 1\) and (blue) for \(\alpha\to 0\).  
Figure 6: Pareto fronts for the collaborative experimental trajectories: (**left**) picking trajectory; (**right**) passing trajectory.  
To better show the developed method's capabilities, a comparative picture was realized by computing and running three types of trajectories for the given sequence of via-points:  
* a trapezoidal velocity profile generated by the robot's controller;
* a min-jerk trajectory computed considering \(\alpha\to 0\);
* a minimum arc-length trajectory computed considering \(\alpha\to 1\).  
A comparison of the three results is reported in Figures 7 and 8 where the robot's trapezoidal velocity profile trajectory is depicted in blue, the proposed approach is depicted with red, \(\alpha\to 0\) with a dashed line and \(\alpha\to 1\) with a solid line. Figure 7 shows the comparison in the joint space for the first three joints of the robot, while Figure 8 reports a comparison in the operational space in the directory of the operator (\(y\)-axis; see Figure 1). We underline that the constraints in Table 1 are respected for the given trajectories  
The proposed approach is inherently smoother than the robot's trapezoidal speed profile and possibly results in being less stressful for the operator.  
In Table 4, we present a comparison between the maximum attained velocities.  
The trajectory obtained with the proposed approach with \(\alpha\to 1\) showed a shorter path with respect to the one with \(\alpha\to 0\) and the lowest maximum velocity. As a consequence, from the mechanical-risk point of view, the trajectory was safer with respect to the others, confirming the validity of the approach.
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 2 Materials and Methods
### Experimental Design and Setup
\begin{table}
\begin{tabular}{l l l} \hline
**Properties** & **Bifacial Panel** & **Monofacial Panel** \\ \hline Max power (1000 W/m\({}^{2}\)) & 335 Watt & 335 Watt \\ \hline Maximum power voltage (Vmp) & 34.56 V & 34.97 V \\ \hline Maximum power current (Imp) & 9.7 A & 9.63 A \\ \hline Open-circuit voltage Voc (V) & 40.65 V & 42.33 V \\ \hline Short-circuit current Isc (A) & 10.69 A & 10.18 A \\ \hline Performance (\%) & 20.05\% & 20.07\% \\ \hline Temperature coefficient P\({}_{\text{MAX}}\) & \(-\)0.38\%/K & \(-\)0.37\%/K \\ \hline Temperature coefficient V\({}_{\text{OC}}\) & \(-\)0.29\%/K & \(-\)0.28\%/K \\ \hline Temperature coefficient I\({}_{\text{SC}}\) & 0.04\%/K & 0.048\%/K \\ \hline Nominal operating temperature & 46 \({}^{\circ}\)C & 45 \(\pm\) 2 \({}^{\circ}\)C \\ \hline Transparency & Translucent & Opaque \\ \hline Front cover & 2 mm low iron tempered solar glass & Glass \\ \hline Cell matrix & 60 Cells (6 \(\times\) 10) & 60 Cells (6 \(\times\) 10) \\ \hline Module dimensions & 1670 mm \(\times\) 1000 mm & 1665 mm \(\times\) 1002 mm \\ \hline Weight & 20.1 kg & 19 kg \\ \hline \end{tabular}
\end{table} 
Table 1: Bifacial and monofacial panel specifications.
## 5 Results  
### Virtual Sensor Fault-Detection Performance  
The faults undetected by the virtual sensor are represented in the time-series data for the chiller energy consumption (represented as red dots in Figure 8). The fault-detection performance of the virtual sensor was evaluated using the four performance metrics listed in Table 5.  
All temporary faults that occurred in Cases A-1 and A-2 were detected to evaluate the fault-detection performance of the virtual sensor. The accuracy, precision, recall, and F-1 score values were 1.000. The virtual sensor showed excellent fault-detection performance for fixed offset (Cases B-1 to B-4) with all evaluation metrics ranging from 0.9930 to 1.000. The distribution of the performance metrics ranged from 0.9846 to 0.9993 for the random offset (Case C). However, it should be noted that the virtual sensor had difficulty detecting faults with small offset values among the random offsets. The distribution of all performance metrics ranged from 0.8851 to 1.000 in the drift offset (Cases D-1 and D-2). This indicated that the virtual sensor had difficulty detecting small offset values during the initial non-operating period of the chiller.  
Overall, the proposed virtual sensor demonstrated excellent fault-detection performance in all cases. However, it exhibited difficulty in detecting faults with small offset values in Cases C, D-1, and D-2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{8}{c}{**Simulation Case**} \\ \cline{2-10}  & **A-1** & **A-2** & **B-1** & **B-2** & **B-3** & **B-4** & **C** & **D-1** & **D-2** \\ \hline Accuracy & 1.0000 & 1.0000 & 0.9995 & 0.9998 & 0.9978 & 0.9980 & 0.9902 & 0.9510 & 0.8851 \\ Precision & 1.0000 & 1.0000 & 0.9985 & 0.9994 & 1.0000 & 1.0000 & 0.9993 & 0.9479 & 0.8778 \\ Recall & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 0.9930 & 0.9937 & 0.9703 & 1.0000 & 1.0000 \\ F-1 score & 1.0000 & 1.0000 & 0.9992 & 0.9997 & 0.9965 & 0.9968 & 0.9846 & 0.9733 & 0.9350 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of fault-detection performance of the virtual sensor.  
Figure 8: _Cont._
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis  
The efficiency and competence of the network are primarily determined by the speed of the technology and the network response time. If the technology is strongly secured but the algorithms require a significant processing time, the technology will fail in real-life scenarios, especially in networking. In today's era of rapid Internet services, computation and communication must be fast enough to respond in real time. In this section, we consider three major factors used to verify the efficiency of our protocol: the computational cost, communication cost, and signaling overhead. Figure 2 shows an algorithm in which the GS is not used, but the client shares part of the secret key with other clients (as explained above). The number of messages exchanged in that algorithm is significantly greater than in ours.
## 3 Experimental Procedures
### Experimental Results
#### 3.3.4 Main Crack Propagation Behavior  
Figure 9 presents the velocity of blast-induced main crack versus with time. The VM and OM represent the blast-induced main crack in the specimen with a vertical and oblique unfilled joint, respectively. After the time of 40 us, the velocity of the main crack VM decreased significantly during the passage of reflected dilatational wave, and then remained stable with approximately 417 m/s when the shear wave impinged onto the propagating crack. Similar to the main crack VM, the velocity of the main crack OM also decreased from 515 to 428 m/s during the incidence of reflected dilatational wave, and then dropped gradually from 428 to 398 m/s. This indicates that the reflected dilatational wave could significantly decrease the opposite propagating crack velocity when it obliquely impinged onto the main crack, whereas the reflected shear wave can slow down the decline rate of the main crack velocity. After the time of 80 us, the velocity of the main crack for both VM and OM dropped rapidly when the main crack propagated adjacent to the unfilled joint.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Arrival Time/ μs**} & \multicolumn{2}{c}{**Medium with Vertical Joint**} & \multicolumn{2}{c}{**Medium with Obliquely Joint with 45\({}^{\circ}\)**} \\ \cline{2-5}  & **Theoretical** & **Experimental** & **Theoretical** & **Experimental** \\ \hline t\({}_{\text{p}}\) & 37.5 & 40 & 30 & 30 \\ t\({}_{\text{s}}\) & 69.0 & 70 & 51.4 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the arrival time for the reflected waves encountered the blast-induced crack between the theoretical analysis and experimental study.  
Figure 10 illustrates the variation of the dynamic stress intensity factors of the main crack. Compared with the crack velocity, the dynamic stress intensity factor \(K_{1}^{d}\) of crack VM dropped a little during the passage of reflected dilatational wave. However, during the incidence of the reflected shear wave, \(K_{1}^{d}\) decreased and \(K_{\rm II}^{d}\) increased. This indicates that a mixed mode I-II fracturing is produced under the influence of the reflected shear wave. In addition, when the reflected wave obliquely impinged onto the main crack, the value of \(K_{1}^{d}\) decreased, and \(K_{\rm II}^{d}\) increased. This implies that the propagation of the main crack changed from mode I type to mixed mode I-II type, making an increase in the consumption of fracture energy when the main crack obliquely encountered the reflected wave, and decreased the crack velocity.
# Roboethics in Remote Human Interactions and Rehabilitative Therapeutics
## 5 iManus for Remote Rehabilitation and Learning
### iManus Platform
On the other hand, the therapist can connect to the system using the desktop interface, monitor the performance and progress of the patient, and prescribe new tasks. The desktop interface is shown in Figure 3. Thus, the therapist can judge the patient's performance both qualitatively and quantitatively. As such, traditional treatment based on visually observing the patient's performance and precise evaluation based on the recorded KPIs and their analysis is possible using the iManus platform. In addition, online and offline communication between the patient and the therapist enables the therapist to give immediate feedback on the patient's practice, helping him/her increase the efficiency and effectiveness of each practice. The mobile and web applications are connected using a cloud-based mobile platform as a service (mPaaS). The cloud database is used to securely synchronize the data between the patient and therapist in real-time. Cloud storage is also used to store videos and the gathered sensory data. It is worthwhile mentioning that when the therapist receives the data on the cloud system, he/she can also provide a pre-recorded task for the continuation of the patient's training. This will also enable the therapist to manage remote patients in an organized, traceable, and standardized manner. Figure 1: Overview of iManus platform. Figure 3: Screenshot of the desktop app at the clinician site. A clinician may add/remove patients, record a new exercise and assign it to the patient, and score the performance of the patient using the audiovisual data and sensory feedback (KPIs) received from the patient site. Figure 2: Screenshots of the mobile app at the patient site. A patient has access to exercises prescribed by the therapist and can communicate with the therapist, as well as monitor their performance.
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 5 Simulation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Items** & & **REQUEST** & **QUEST** & **OP-PL** \\ \hline \multirow{3}{*}{Pitch (deg)} & \multirow{3}{*}{1-150 s} & MEAN & \(-\)14.7921 & \(-\)1.7156 & \(-\)2.3169 \\  & & RMSE & 16.4321 & 3.5995 & 4.1962 \\ \cline{2-6}  & \multirow{3}{*}{150–300 s} & MEAN & \(-\)36.1971 & 0.1662 & \(-\)0.0760 \\  & & RMSE & 37.5654 & 0.1715 & 0.0824 \\ \hline \multirow{3}{*}{Roll (deg)} & \multirow{3}{*}{1-150 s} & MEAN & \(-\)11.6315 & 1.8145 & \(-\)0.3420 \\  & & RMSE & 14.3065 & 12.6798 & 1.5843 \\ \cline{2-6}  & \multirow{3}{*}{150–300 s} & MEAN & \(-\)29.8454 & \(-\)0.2526 & 0.1159 \\  & & RMSE & 31.4920 & 0.2619 & 0.1210 \\ \hline \multirow{3}{*}{Yaw (deg)} & \multirow{3}{*}{1-150 s} & MEAN & 2.0683 & 5.7750 & 10.8685 \\  & & RMSE & 2.6878 & 63.6506 & 57.2915 \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{150–300 s} & MEAN & 8.9417 & 3.5183 & \(-\)0.9181 \\ \cline{1-1}  & & RMSE & 9.4477 & 3.7871 & 1.4000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error statistics of three algorithms in simulation test. Figure 11: Box plot of angle errors.
## 4 Examples of Combining ML Tools to Interpret the RVA Profile
Furthermore, the interpretation of both the PCA and PLS loadings provided critical information that was hidden in the RVA profile that was used to better explain which starch pasting properties of the sample contributed to explaining the differences between the different regions [16; 17; 26]. A study by [PERSON] et al. (2013) [27] evaluated the physicochemical properties and RVA profiles of taro (_Colocasia esculent_ L.) using Pearson correlation and PCA. The results from this analysis demonstrated differences between taro flour and other flour samples from different crops. The taro samples had high carbohydrate contents and water absorption levels and lower protein contents, foaming capacities, and STBs [27]. The PV of taro flour was lower in comparison to potato flour and higher than soya and corn flours. The visualization of the scores derived from the PCA indicated that taro and potato flours had negative scores, whereas the soybean and corn flour samples had positive scores along the first principal component [27]. Recently, a study by [PERSON] et al. (2018) [28] demonstrated the ability of RVA profiles, combined with ML tools, to identify viscosity fingerprints that can serve as profile markers for the classification of rice from different regions and varieties. The full RVA profiles obtained from 152 rice samples were analyzed using PCA and PLS-DA [28]. The results showed that different rice subspecies or group types could be distinguished by interpreting the RVA profile in combination with PCA. The ability of RVA profiles combined with ML tools to obtain useful information for the traceability of ingredients in alternative protein-based formulations has been reported [35]. RVA was used to evaluate the addition of cricket powder to chickpea and flaxseed flours via PCA and PLS regression [35]. The utilization of PCA and PLS regression enabled both qualitative and quantitative identification of the addition level of cricket powder to chicken pea and flaxseed flour samples. Differences in the PLS loadings associated with the RVA profile due to the addition of CKP was reported by the authors, and the cross-validation statistics for the predicted level of cricket flour were as follows: R\({}^{2}\)CV = 0.90-0.95 and SECV = 5.7-7.7. This study demonstrated that integrating RVA profiles with machine learning tools provides a reliable, accurate method for ensuring proper ingredient formulation, which is crucial for both quality control and meeting the traceability demands in alternative protein-based food products [35].
## 1 Introduction
Based on the overall simulation results, we derive a distinctive parameter that indicates the variability in the AUV-BS distance caused by BS motion. * Considering the analysis, we propose several handover considerations, including the influence of BS motion on the AUV-BS distance, as well as how to adaptively respond to the variability in the location of a BS. In addition, we recommend strategies for setting the handover triggering time and configuring the network radius to effectively respond to BS motion. Figure 1: BS motion responses and varying overlapping region due to BS motion. This paper stands out as a pioneering analysis of the impact of BS motion on handover decisions in B-UWANs; it also introduces an indicative parameter examining the extent of the impact that BS motion has on handover decision errors. Furthermore, the proposed handover considerations in response to BS motion can aid in designing handover protocols for B-UWANs. The remainder of this paper is structured as follows: Section 2 details the system model considering underwater handover for B-UWANs; Section 3 describes the numerical modeling of a buoy-structured BS; in Section 4, we explain the conditions and assumptions for the simulations, as well as providing an analysis and summary of the simulation results; finally, we conclude this study in Section 5.
## 4 Results and Discussion
### Application of the RIRA Method
Precipitation is the main recharge source for soil moisture in the hill and gully region of the Loess Plateau [50]. According to the survey, 40% of the catastrophic landslides are trigged by rainfall on the Loess Plateau of China [26]. It is not easy to infiltrate after precipitation, however, once the effective strength of the loess is reduced by rainfall, the landslide is triggered. There is a close correspondence between landslide occurrence and the pattern of precipitation, and 84.6% of landslides have happened in the monsoon in the Baota district since 1985 [51]. Therefore, rainfall is the fourth important factor in the study area. Elevation data reflected the location of the landslide, which means the volume of landslides has higher relative sensitivity on the variable of elevation in the study area. The slope is an important performance of the slope geometry, which determines the state and distribution of stresses in the slope mass and controls the stability and mode of the instability [51]. However, the slope has relatively little impact on the occurrence and scale of landslides in the study area. The increased vegetation coverage has a certain inhibitory effect on the amount of landslides, but considering the comprehensive effect of other factors, the influence of plants is not obvious. In addition, landslides with different scales occurred in different lithology. Thus, the influence of lithology on landslides is also relatively rare in the study area. The impact of environmental and human factors on the surface, such as construction activities, road cutting, and natural resources exploitation, may lead to landslides [34]. However, during the 9 th Five-Year Plan period (1996-2000), Yangou watershed was selected as a demonstration region for integrated research for the development of ecological agriculture in the Loess Plateau, and the land use structure changed rapidly [41]. Nowadays, the land cover tends to be stable, so the absolute sensitivity of landslides to land use is lowest.
# Dynamic Controllability of Processes without Surprises
## 4 Sudden Termination and Sudden Start Problems, and Semi-Dynamic Controllability
### Sudden Start Problem  
In which cases is an activity required to suddenly start? Similar to the sudden termination, a required sudden start is due to the existence of some temporal constraint, which binds a start event to some other uncontrollable (contingent) event. Thus, it is not possible to schedule the start event in advance, but it is required to react to the observation of the occurrence of a contingent event.  
In an SSC, for all possible start times of \(C\), _there exist_ certain start times for \(S\) and some advance notice \(w\) at least equal to \(k(S)\), such that some (uncontrollable) durations of \(C\) exist, which lead to the violation of some temporal constraint in the process. Instead in an SSP, for all possible start times of \(S\) and _for all_ possible advance notice \(w\) at least equal to \(k(S)\), for all start times for \(C\), some (uncontrollable) durations of \(C\) exist, which lead to the violation of some temporal constraint in the process.  
**Definition 5** (Sudden Start Constellation and Pattern): _Let \(P\) be a dynamically controllable process. Let \(S\) and \(C\) be activities in \(P\), with \(C\) having duration constraint \(d(C,C.d_{min},C.d_{max},c)\). Let \(\Phi\) be the set of constraints in \(P\)._  
\(S\) _and_ \(C\) _are in a_ _sudden start constellation (SSC)_ _iff_  
\(\forall C.s\ \exists S.s\ \exists w\in\mathbb{N}:S.s-w\geq k(S)\ \exists C.d_{min}\leq C.d\leq C.d_{max}:\ \
eg\Phi\)__  
\(S\) _and_ \(C\) _are in a_ _sudden start pattern (SSP)_ _iff_  
\(\forall S.s,w\in\mathbb{N}:S.s-w\geq k(S)\)_,_ \(\forall C.s\ \exists C.d_{min}\leq C.d\leq C.d_{max}:\ \
eg\Phi\)__  
_We use the notation \(SSC_{S,C}\) to indicate that \(S\) and \(C\) are in a sudden start constellation, and \(SSP_{S,C}\) for a sudden start pattern._  
Similar to the STP, if there is an SSP in a process, sudden start cannot be avoided, without changing the process. If there is an SSC but not an SSP, the execution of a process can be dynamically scheduled in a way to both observe all constraints and avoid sudden start.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
###### Abstract  
The software defined networking (SDN) paradigm with enhanced features of IPv6 offers flexible network management and better network visibility for enhancing overall network performance, network manageability, and security. Thus, along with the IPv6 network deployment worldwide, SDN migration has emerged worldwide, but network service providers suffer from different issues when migrating their existing legacy network into operable SDN and IPv6 enabled networks. In this paper, we investigate the affordability of broadband network services for the rural communities in the context of information and communication technology (ICT) infrastructure deployment throughout Nepal. During the phase of network transformation, it will be more challenging for the service providers of Nepal to have a proper choice of technologies to expand the network while considering the proper policy formulation, affordability, need of skilled human resources, deployment cost, and many other aspects. We also present the service provider affordability via energy optimization in software defined IPv6 network (SoDIP6) implementation that contributes to a reduction in organizational operational expenditure (OpEX). We perform an experimental analysis over an SoDIP6 network testbed and present a comparison of the annual energy and OpEX savings for network service providers. Our empirical analysis shows that an energy saving of 31.50% on switches and 55.44% on links can be achieved with an SoDIP6 network compared to a network with legacy devices and network management. Optimization on service provider network operational cost leads to sustainability and affordable services to both customers and service providers  
affordable broadband; rural communities; sustainable societies; SDN; IPv6; SoDIP6; energy efficiency