esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Error Analysis and Human Evaluation  
Human evaluation was conducted on 200 translations (67 per target language) using the optimal configuration. Three qualified native Tarifit speakers evaluated translations using 5-point Likert scales (Table 7).  
Inter-annotator agreement measured by Krippendorff's alpha was \(\alpha=0.69\) for adequacy and \(\alpha=0.65\) for fluency, indicating substantial agreement. The correlation between automatic metrics and human judgments was strongest for chrF (r = 0.84), while BLEU showed weaker correlation (r = 0.72) (Figure 5). Representative examples of human evaluation outcomes across different translation quality levels are provided in Appendix A.3, illustrating the range of translation challenges encountered and the evaluation criteria applied.  
Systematic error analysis reveals distinct patterns across target languages (Figure 6): **Morphological Errors:** 42% of errors involved incorrect handling of Tarifit's agglutinative morphology, most frequent in English translations (48%).  
**Lexical Errors:** 28% of errors involved lexical choices. Arabic translations showed fewer lexical errors (19%) compared to French (31%) and English (39%).  
**Cultural Errors:** 18% of errors involved mistranslation of culture-specific terms, relatively consistent across languages (15-22%).  
**Code-Switching:** 12% of errors occurred in code-switched segments. Arabic translations handled these most effectively (28% success rate vs. 8% for French and 6% for English).  
The error analysis reveals that while current LLMs demonstrate promising capabilities for Tarifit translation, systematic challenges remain in morphological processing and cross-linguistic transfer, particularly for topologically distant target languages. The increased dataset size of 1000 sentences provides more robust error pattern identification  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Language** & **Adequacy** & **Fluency** & **BLEU** \\ \hline Arabic & \(3.4\pm 0.7\) & \(3.6\pm 0.6\) & 20.2 \\ French & \(2.8\pm 0.8\) & \(3.0\pm 0.7\) & 14.8 \\ English & \(2.5\pm 0.9\) & \(2.7\pm 0.8\) & 10.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Human evaluation results.  
Figure 5: Automatic vs. human evaluation correlation.  
and demonstrates the persistent challenges in automated processing of morphologically complex low-resource languages.
## 5 Conclusions  
In this paper, an enhanced clustering method (TIGSO-EDS) was designed and implemented. Optimal CH selection was carried out using the hybrid VIKOR-GSO method, and data aggregation was performed on the selected CH. Next, the aggregated data were passed to the BS through the best possible path, selected using the Cuckoo Search algorithm. Here, the security was guaranteed by the encrypted process between the BS and the CH. The simulation results showed that the proposed TIGSO-EDS method had enhanced performance than the existing EEHC [17], EECT [32], and S-EECP [33] methods regarding the network energy consumption, throughput, aggregation latency, and delay  
Figure 8: Energy consumption of different methods (base station located at 50 \(\times\) 50).  
Figure 9: Energy consumption of different methods (base station located at 100 \(\times\) 250).  
parameters. It uses the hybrid VIKOR method combined with GSO, which is applied to select the cluster head in the network. Then, for cluster formation, a GA was used, and data aggregation was performed. To transfer the data securely, they were encrypted using AES. The Cuckoo Search algorithm (CSA) was followed to find the optimum path, and then the data were communicated to the BS for further processing. The simulation results show that TIGSO-EDS performed better compared with the other methods.
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 3 Multiple Target Tracking
### Measurement-to-Track Association  
The measurement-to-track association is the process of assigning the measurements to the established tracks. The NN association rule assigns the \(\hat{n}^{t}_{j}(k)\)-th measurement to track \(t\), which is obtained as:  
\[\hat{n}^{t}_{j}(k)=argmin_{m=1,\ldots,N_{M}(k)}\|\mathsf{v}^{t}_{mj}(k)^{T} \Big{[}S^{t}_{j}(k)\Big{]}^{-1}\mathsf{v}^{t}_{mj}(k)\|, \tag{15}\]  
\[\mathsf{v}^{t}_{mj}(k)=z_{m}(k)-H\hat{\mathbf{x}}^{t}_{j}(k|k-1), \tag{16}\]  
where \(N_{M}(k)\) is the number of measurements at frame \(k\), and \(z_{m}(k)\) is the \(m\)-th measurement vector at frame \(k\). The measurement gating is performed by the chi-square hypothesis testing assuming Gaussian measurement residuals as [18]:  
\[\mathsf{v}^{t}_{\hat{n}^{t}_{j}(k)j}(k)^{T}\Big{[}S^{t}_{j}(k)\Big{]}^{-1} \mathsf{v}^{t}_{\hat{n}^{t}_{j}(k)j}(k)\leq\gamma_{f}, \tag{17}\]  
where \(\gamma_{f}\) is the gate threshold for the measurement association. The maximum speed gating is also applied to the nearest measurement as:  
\[\|z_{\hat{n}^{t}_{j}(k)}(k)-H\hat{\mathbf{x}}^{t}_{j}(k-1|k-1)\|\leq\Delta\cdot S _{max}, \tag{18}\]  
where \(S_{max}\) is the maximum speed of the target. The measurements that fail to associate with the track go to the initialization step in Section 3.2.
## 3 Materials and Methods
### Analysis of Phenolic Compounds
#### 3.3.1 Folin-Ciocalteu Assay  
The total phenolic content was determined using the Folin-Ciocalteu method [34] slightly modified [35]. In a 15-mL plastic flask, 0.5 mL of deionized water and 125 \(\mu\)L of the Folin-Ciocalteu reagent were added to 125 \(\mu\)L of the suitably diluted sample extract. The mixture was kept for 6 min and then 1.25 mL of a 7% aqueous Na\({}_{2}\)CO\({}_{3}\) solution were added. The final volume was adjusted to 3 mL with water. After 90 min, the absorption was measured at 760 nm against water as a blank. The amount of total phenolic compounds was expressed as gallic acid equivalents (GAE, mg gallic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RT (min)** &
\begin{tabular}{c} **UV Absorption** \\ **(nm)** \\ \end{tabular} & **Onion Flakes** & **Onion Rings** \\ \hline  & & & **(mg g\({}^{-1}\))** & **(mg g\({}^{-1}\))** \\ \hline
**flavonoid** & & & & \\ Quercetin-7,4\({}^{\prime}\)-diglucoside & 22.3 & 252–366 & 0.05 & 0.04 \\ Quercetin-3,4\({}^{\prime}\)-diglucoside & 25.2 & 265–344 & 1.76 & 0.94 \\ Isorhamretin-3,4\({}^{\prime}\)-diglucoside & 27.3 & 266–344 & 0.11 & 0.01 \\ Quercetin-3-glucose & 33.7 & 256–350 & 0.05 & 0.02 \\ Quercetin-4\({}^{\prime}\)-glucose & 36.9 & 253–365 & 1.52 & 0.87 \\ Isorhamteinna-4\({}^{\prime}\)-glucose & 38.0 & 252–366 & 0.03 & 0.13 \\ Quercetin & 42.9 & 255–370 & 0.04 & 0.04 \\
**Total flavonoid content** & & & **3.56 b** & **2.04 a** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quali-quantitative flavonoids content, expressed as mg\({}_{\text{rutin}}\) mg g\({}^{-1}\). Data are the mean of three independent determination (Standard Deviation \(<5\%\)). For the total flavonoid content, different superscript letters (a, b) indicate significant difference at \(p<0.05\).  
acid/100 g sample) through the calibration curve of gallic acid. The calibration curve ranged from 20 to 500 \(\upmu\)g mL\({}^{-1}\) (R\({}^{2}\) = 0.9969).
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 3 Subjective Listening Tests Using the MUSHRA Methodology  
### Method  
To demonstrate the impact of spatial audio compression on perceived audio quality, a set of subjective listening tests were carried out using a double-blind multi-stimulus test method with a hidden reference and hidden anchor (MUSHRA) following the ITU-R BS.1534-3 Recommendation. During the listening tests, subjects were presented with a labeled reference and several unlabeled test samples. The subjects were asked to rate different samples for a set of encoding conditions such as Ambisonic order and encoding bit rates. They were asked to assign quality ratings from 0 to 100 to the unlabeled test samples using a numerical continuous scale in five intervals: bad (0-20), poor (20-40), fair (40-60), good (60-80), and excellent (80-100).
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 2 Application Scenarios
### Sensor Nodes into Soldier Uniforms  
Integration of sensors into soldier uniforms reduces greatly the weight burden placed on them and provides more opportunities to successfully execute missions. Sensors can measure not only geolocation data of soldiers but also health and body conditions, such as heart rate, breathing or blood pressure. Rapid access to this information could help soldiers and officers in charge of missions survive otherwise lethal enemy attacks.  
Since this information is sensitive, it should be transmitted ciphered. However, ciphering may not be sufficient. Fake sensors should not be used in this scenario.  
Using the proposed sensors an enemy cannot impersonate a soldier because his/her biometric features do not match with the template of the legitimate soldier. The base station can track the messages sent by the soldiers wearing the sensors and detect if he/she is executing correctly the mission or there is something wrong (non-repudiation is achieved).
## 4 Case Study
### Subsequent On-Site Validation  
As a further test of the accuracy of Equation (25), the subsequent construction section K91 + 700 to K91 + 950 of the tunnel was selected. The total length of this tunnel section was 250 m, with a total of 42 days of construction excavation. It was influenced by the tensor fault F2-6, which was given in Figure 10.  
The average thickness of the aquifer \(H_{n}\) in the water-rich fault fracture zone was approximately 587 m. The surface of the mountain was a weathered layer of andesite with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**The Average Duration of Water Gushing (d)**} \\  & **150** & **200** & **250** & **300** & **350** \\ \hline Method of rainfall infiltration & 7701 & 10,268 & 12,836 & 15,403 & 17,456 \\ Method of the runoff module & 5854 & 7805 & 9757 & 11,708 & 13,269 \\ number of the groundwater & 36,510 & 36,020 & 35,274 & 34,800 & 34,085 \\ Dynamic monitoring data & 38,198 & 36,174 & 35,231 & 34,736 & 33,578 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of water gushing.  
Figure 10: The relationship between the fault F2-5 and the tunnel section.  
a precipitation infiltration coefficient of 0.25 to 0.3. The 250 m long section was divided into six successive sections from A to F with a difference gradient of 0.0005 based on the difference in rock feed. During construction, the timing of water gushing was recorded for each section. The effects of the amount of water seepage from adjacent tunnel sections were ignored. The specific parameters were detailed in Figure 11.  
The parameters in Figure 11 were substituted into Equation (24) for the calculation. The value of the variation of \(R_{n}\) with \(T\) for this construction section was obtained. The calculations of \(R_{n}\) at each time point were substituted into Equation (25), and the calculated value of water gushing from the section K91 + 700 to K91 + 950 was obtained. The calculated results were compared with the dynamic monitoring values, which were shown in Figure 12.  
As can be seen in Figure 12, the trend between the calculated value curve and the dynamic monitoring data curve was generally consistent. Both of them decreased when the time increased. The calculated value of water gushing within one to six days was slightly greater than the actual monitored value, with an average error of 7%. Some fluctuations in the results of dynamic monitoring occurred between days 7 and 42. Overall the calculated values were relatively close to the recorded values. The maximum error was less than 5%.
## 4 Discussion and Conclusions
### Algorithm Discussion
#### 4.1.1 Path Planning for Restricted Network  
The traditional heuristic function can effectively solve the shortest path planning problem but falls short when dealing with path planning in a restricted networks where the focus of the traveler is shifted from the shortest path to the least number of transfers, the shortest time, and the lowest cost. The multivariate heuristic function solves the above problem by taking the traveler's concern as a constraint function.
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 3 Results
### 50 kW Case Study
#### 3.2.2 50 kW Case Study Sensibility Analysis  
For the 50 kW case study a sensibility analysis has been performed considering a range of annual yields from 1100 to 1500 kWh/kWp with a step of 100 kWh/kWp in the range. Using the methodology applied in Section 3.1.1, it is possible to calculate the LCOE considering the method  
Figure 10: Analysis of the LCOE against the retail electricity cost at various interest rates (50,000 W inverter, deterministic method).  
Figure 9: Analysis of the LCOE against the regulated tariff at a fixed interest rate of 10% (50,000 W inverter, deterministic method).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**PV Module Cost (\(\xi\)/Wp)**} & \multirow{2}{*}{**System Cost (\(\xi\))**} & \multicolumn{5}{c}{**LCOE (\(\xi\)/kWh)**} \\ \cline{3-8}  & & \multicolumn{1}{c}{**Interest Rate (\%):**} & \multicolumn{1}{c}{**12**} & **10** & **8** & **6** & **4** \\ \hline
1.0 & 118,580 & 0.2465 & 0.2153 & 0.1857 & **0.1579** & **0.1324** \\
0.8 & 104,060 & 0.2184 & 0.1911 & **0.1651** & **0.1407** & **0.1183** \\
0.6 & 89,540 & 0.1904 & **0.1669** & **0.1445** & **0.1235** & **0.1042** \\
0.4 & 75,020 & **0.1623** & **0.1426** & **0.1239** & **0.1063** & **0.0902** \\
0.2 & 60,500 & **0.1343** & **0.1184** & **0.1033** & **0.0891** & **0.0761** \\ \hline \hline \end{tabular}
\end{table}
Table 9: LCOE over a range of interest rates (50,000 W inverter, deterministic method).  
described previously, fixing the regulated tariff at 17.4 c/kWh including taxes and with a fixed interest rate of 10%.  
Table 10 shows the most relevant results for the five cases. Case 1 corresponds to 1100 kWh/kWp and Case 5 corresponds to 1500 kWh/kWp. The positions where the LCOE is lower than the retail cost of energy have been highlighted.  
Five case studies are considered in Table 7, but the cost at 0.6 c/Wp is considered as the current scenario. For that reason, the PDF for the panel cost is log-normal with a mean of 0.6 (\(\epsilon\)/Wp) and a standard deviation of 0.2 (\(\epsilon\)/Wp). The panel cost floor is set at 0.2 (\(\epsilon\)/Wp) so that the  
Introducing the results of the iterations given by the Monte Carlo method for the business model, we obtain the IRR values. The IRR results for the 50 kW business case are shown graphically in Figure 13.  
A success rate parameter (%) is introduced by counting the number of iteration results below the retail cost of the electricity and dividing by the total number of iterations (10,000). Analysing the frequency of the results and the value itself, the success rate for each case can be calculated. In this case of a 50 kW installation, it can be concluded that the success rate would be 99.59%. This means that in 99.59% of cases, grid parity is achieved as the LCOE is lower than the retail cost. As a business case, the mean of the IRR is positive (5.29%). Therefore, it can be concluded that even though the loan input in the model contributes negatively, the investment is successful.
## 4 Discussion
The cause of the higher MBE values on the fixed station can be identified by considering that the ZPD values are strongly dependent on the atmospheric pressure value at the station level. In the model, the pixels over the sea, where the surface height was constant, pressure values were quite homogeneous, all over the area inside the pixel, and were, therefore, directly comparable with a single point. The same does not hold for the ground pixels, where the presence of non-negligible orographic effects occurs within the pixel, influencing the average surface pressure value, which, as a consequence, can not be representative of a single point. Therefore, it can be hypothesised that this bias is mainly attributable to the average elevation of the terrain in the MERRA-2 ground pixels. This effect was particularly accentuated in the LAM1 station located in Sesto Fiorentino near Florence, at a height of a few tens of meters but on the slopes of a mountain with an over 900 m high summit in the central Italian Apennines chain.
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 6 Conclusions  
To further improve the O&M of OWTs and answer some important questions related to them, a new PN model and a new maintenance strategy assessment criterion, namely Cost for achieving turbine availability per day, are developed in this paper. Using the model developed, three new potential O&M measures are studied. From the research reported above, the following conclusions can be made:  
* Periodic maintenance is essential to achieve the economical operation of OWTs. However, only when the periodic maintenance is carried out at optimal frequency, the economical operation of the wind turbine can be achieved. This optimal frequency is every half year for the OWT considered in this paper;
* Measure (2) proposed in the paper provides a new method to more effectively utilise the maintenance resources. The calculation results have shown that the application of this measure does reduce the downtime and maintenance costs of the wind turbine if the \"advanced service\" is conducted infrequently;
* It is essential to equip the wind turbine with a CMS. Thanks to the use of a CMS, the unexpected shutdowns of the turbine due to subsystem failures and the downtime and maintenance cost so caused are significantly reduced. Moreover, the better the quality of the CMS, the more shutdowns, downtime, and maintenance costs will be reduced. However, the CMS should not be very expensive, as the high cost of the CMS may make the operation of the wind turbine no longer economical.  
It should be aware that the PN model developed above is only applicable to the maintenance of a single OWT unit and moreover the influence of extreme environmental events on the reliability and maintenance of the OWT has not been considered in the model. At the next step, the research will be extended to the development of the PN models for simulating the maintenance of an offshore wind farm that consists of multiple OWT units of different sizes. To enable the output of the research be closer to a digital twin of the maintenance of the offshore wind farm, the future wind farm maintenance model will consider the influence of occasional extreme environmental events and the influences of the size, offshore distance, and location of individual OWTs on the model parameters and maintenance costs. In addition, different strategies for inspecting and maintaining multiple OWTs in the same offshore wind farm will also be studied for obtaining an optimal offshore wind farm maintenance strategy.  
Conceptualisation, R.Y. and S.D.; methodology, R.Y.; validation, R.Y. and S.D.; formal analysis, R.Y. and S.D.; investigation, R.Y.; resources, S.D.; data curation, R.Y.; writing--original draft preparation, R.Y.; writing--review and editing, S.D.; supervision, S.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 1 Introduction
Irrespective of the high costs associated with ultra-resolution and very high-resolution satellite images, many researchers have utilized ultra-resolution and very high-resolution satellite images in a number of ecological applications, particularly at local scales, such as coastal or freshwater vegetation mapping [38; 39; 40], tree species detection and classification [41; 42; 43; 44], and detection and mapping of invasive species such as bracken fern [45; 46; 47; 48], to name some examples. As far as the methodology is concerned, both pixel and object-based approaches have been employed for the classification of very high-resolution satellite images using machine learning classifiers [49; 50; 51; 52; 53]. The major objective of this research is to present an ultra-resolution features extraction and classification suite for the differentiation and mapping of land cover and community-level vegetation types at 0.5 m resolution with a limited number of temporal images. The features extraction flow of the ultra-resolution suite involves pan-sharpening of the multi-spectral image, color-transformation of the pan-sharpened image, and the generation of panchromatic textural features. The performance of the ultra-resolution features extraction suite is compared with a very high-resolution features extraction suite at 2 m resolution that involves the calculation of radiometric indices and color transformation of the multi-spectral image. Three machine learning classifiers, Random Forests, XGBoost, and SoftVoting were employed with a ten-fold cross-validation method for quantitatively evaluating the performance of the ultra-resolution suite against the very high-resolution suite for the differentiation and mapping of land cover and community-level vegetation types.
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 2 Related Works
### Motivation  
As mentioned in Section 2.1, two kinds of mobility managements are reported; one is the mobility managements using the concept of follow-me cloud (FMC) [7; 24; 25; 26; 27] and another one is the mobility management using the concept of follow-me edge/cloudlet (FME) [9]. The main motivation of this work is attempted to propose a new mobility management with the integration of the concepts of follow-me cloud (FMC) [7; 24; 25; 26; 27] and the follow-me edge/cloudlet (FME) [9], which is called as follow-me cloud-cloudlet (FMCL) approach in this paper. Existing FMC results do not own the novel property of the VM migration between cloudlets for the purpose of reducing the transmission latency. All existing (FME) results do not keep the advantage of the service migration between data centers (DCs) for reducing the transmission latency. Efforts will be made to propose a follow-me cloud-cloudlet (FMCL) approach to keep the VM migration between cloudlets and service migration between data centers (DCs) to significantly reduce the transmission latency. Thus, this work is to propose a new mobility management using the FMCL approach for the smart cities. One interested capability of fog-computing-based RAN architecture is that F-AP (fog-computing-based access point) of F-RAN contains the local cache functionality. Consequently, the new mobility management with the FMCL approach can inherit the advantage of the service migration from follow-me cloud (FMC) [7; 24; 25; 26; 27] and can explore and additionally keep the data locality property from the local cache functionality from the follow-me edge (FME) [9].  
Consequently, one of the benefits is that the optimal shortest route can be re-calculated and re-constructed from the new service migrated data center to significantly reduce the network transmission latency. Another one is that all received packets can be pre-scheduled to the cache of edge/cloudlet to keep this data locality property of cache to improve the throughput and reduce the packet loss rate.