esa-sceva/satcom-chunk-collection huggingface dataset:
## 12 Conclusions  
A detailed overview of the design and features of a laser beacon integrated into the NEMO tower for the KM3 NeT project has been presented, together with the results of the qualification. The pivotal constituents of the laser beacon have been presented, including the batteries, diffuser, electronic attenuator, antibiofouling system, and laser head. This article delves into the mechanical aspects of the laser beacon, showcasing a titanium container designed to withstand marine corrosion. The use of batteries enhances the portability of the laser beacon, and the results of this study show that Mi-MH batteries achieve the best performance for the operation of the laser beacon. A study of the antibiofouling system has also been presented. The rod design allows for the emission of light that will not be disturbed by biofouling, while the minimum and maximum emissions angles have been optimized to maximize the light range. \(\theta_{min}=0^{\circ}\) guarantees that the light will reach the optical modules of the tower where the laser beacon is installed, whereas \(\theta_{max}=75.9^{\circ}\) guarantees that the light of the laser beacon will reach the lower stories of nearby towers. The qualification has proved that the laser beacon container can sustain a pressure of 350 bars. The internal PD has a jitter of less than 0.5 ns, providing precise timing. Finally, the qualification has shown that the laser heads emit pulses of about 4 uJ in a stable manner. These insights contribute significantly to the advancement of optical calibration systems for underwater neutrino telescopes.  
The individual contribution of each author is as follows: 'Conceptualization, D.R. and A.S.L.; methodology, D.R.; software, A.D.; validation, D.R., A.S.L. and D.C.; formal analysis, F.S.G.; investigation, D.R.; resources, A.S.L.; data curation, F.S.G.; writing--original draft preparation, D.R.; writing--review and editing, D.R.; visualization, A.D.; supervision, D.R.; project administration, A.S.L.; funding acquisition, A.S.L. and F.S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministerio de Ciencia e Innovacion: Programa Estatal para Impulsar la Investigacion Cientifico-Tecnica y su Transferencia (refs. PID2021-124591 NB-B-C41) (MCIU/FEDER); the Programa de Planes Complementarios I+D+I (refs. ASFAE/202/023); Generalitat Valenciana: Prometeo (PROMETEO/2020/019); the Grisolia (ref. GRISOLIAP/2021/192) and GenT (refs. CIDEGENT/2018/034, /2020/049, /2021/23) programs; and the EU: MSC program (ref. 101025085), Spain.  
Figure 10: Pressure tests performed on the laser beacon container.
# MIMO Self-Heterodyne OFDM Using Band Selection Technique
## 5 Conclusions  
In this paper, two new techniques; BS-STBC and BS-FSTBC are proposed for MIMO self-het OFDM system. The two proposed techniques are based on using BS approach. The use of BS helps to select the optimum sub band for data transmission. The used of the optimum sub band with higher gain for transmission reduces the probability of error and consequently improve the BER performance. The FSTBC achieves the full data rate compared to the STBC technique which achieves only the half data rate. The proposed techniques (BS-STBC), and (BS-FSTBC) are compared to the conventional techniques (STBC), and (FSTBC). The simulation results showed that proposed techniques outperform the conventional techniques performance in terms of BER. The proposed BS-FSTBC technique has superiority over the proposed BS-STBC technique in term of BER performance. The FSTBC MIMO self-het OFDM system can withstand the required same high BER performance level with a small number of subcarriers providing the lower complexity.  
Conceptualization, A.I.Z., M.A., S.E.E.-K. and W.K.B.; Formal analysis, A.I.Z., S.E.-K. and W.K.B.; Investigation, M.A. and W.K.B.; Methodology, A.I.Z., M.A., S.E.E.-K. and W.K.B.; Resources, A.I.Z. and M.A.; Supervision, A.I.Z., S.E.-K. and W.K.B.; Validation, W.K.B.; Writing--original draft, M.A.; Writing--review & editing, A.I.Z. and W.K.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 4 Methods
### Improved Matrix Divide-and-Conquer-Based FFT Algorithm (MV-2 FFT)
#### 5.1.3 Algorithm and Configuration  
To conduct a comprehensive comparison, we benchmarked three implementations of our algorithms running on NPUs against the Cooley-Tukey algorithm running on other platforms for execution speed. One baseline we used was the Cooley-Tukey algorithm based on OpenCL running on the NVIDIA Tegra X2 built-in GPU, referred to as FFT_CL. NVIDIA Tegra X2 is specifically designed for edge applications, and its level of compute power is representative. Another baseline was the Cooley-Tukey algorithm running on an embedded CPU within the NPU, referred to as FFT_CPU.  
Note that the FFT_CL was excluded from the energy efficiency evaluation as the different hardware designs interfered with the experiment's results. An additional reason was that we focused more on the impact of the NPU algorithm and internal units on the overall energy efficiency of the board. For the same reason, the FFT_CPU was included in the analysis of CPU energy efficiency as an internal unit of the NPU. That is because CPU units are usually integrated into NPU architectures to support the execution of algorithms that cannot be effectively processed through matrix and vector computations.  
For the algorithm configuration, we used the GCC O2 level optimization for the FFT_CPU and set the minimum block matrix size differently for MM-2 FFT. These configurations were obtained based on preliminary experimental results to maximize performance. Specifically, the GCC O2 level optimization was used because it accelerates the FFT_CPU algorithm up to three times faster than the non-optimized one, and it outperforms all other optimization levels. Regarding matrices division, we partitioned the input matrix into block matrices whose order was half of the original matrix when the order of input matrices was lower than 2048. Specifically, for an input matrix of order 2048, we set the minimum  
Figure 3: Software flow chart of the edge filter.  
block matrix size to \(512\times 512\). We selected those configurations as they are shown to be the optimal ones according to the preliminary experiment. It is worth noting the experiment result shows that fine-grained division of the input matrix may slow down the algorithm execution on the NPU platform. This is because the fine-grained division strategy incurs high scheduling costs. For the configuration items not mentioned above, we used the default settings.
## 4 Protein Quality Evaluation Methods
### In Vivo Models
#### 4.1.1 Protein Digestibility-Corrected Amino Acid Score (PDCAAS)  
For over three decades, the PDCAAS has been the primary system for evaluating the quality of proteins in human foods. The PDCAAS calculates a digestibility value using the overall digestibility of crude protein (CP) measured in rats. This value is then multiplied by the concentration of the primary limiting amino acid (AA), determined by contrasting the AA profile of a protein sample with the estimated AA requirements of young children. However, limitations of PDCAAS have been identified, as the total tract digestibility of CP may not accurately reflect AA digestibility, and PDCAAS assumes equal digestibility for all AAs [42; 43].  
Therefore, there is a growing need for more accurate methods for evaluating protein quality, which has led to the development of the Digestible Indispensable Amino Acid Score (DIAAS) method. The DIAAS method is recommended by the Food and Agriculture Organization of the United Nations (FAO) as an alternative to PDCAAS, and it overcomes the limitations of the PDCAAS method by considering the true ileal digestibility of AA, rather than total tract digestibility of CP, and using a more accurate estimation of the AA requirements for humans [46; 47].  
Although PDCAAS was previously considered the \"gold standard\" for protein quality determination, the DIAAS method has emerged as a more reliable alternative for evaluating protein quality in human foods.
## 2 A Literature Review
### Adoption of TAM and Its Extensions  
Technology is commonly created in a specific social context and can be influenced by human behaviors and institutional background [70]. At this point, human activities can be regarded as a part of technology and, thus, affect the outcomes of technology. To explain the determinants of technology acceptance from the perspective of attitude and behavior, TAM is universally adopted [71]. TAM is an essential theoretical framework underpinning the investigation into the determinants of Industry 4.0 technology adoption. Specifically, this model elucidates how PU and PEOU substantially influence an organization's decision to integrate advanced technological systems, thereby acting as the conceptual backbone of this meta-analysis. TAM postulates that the intention to adopt the technology is highly influenced by the PEOU and its usefulness [72]. The intention to adopt will work as an intermediary to the actual use of the technology, and the PU will be influenced by the PEOU in TAM [72]. The evolution of the original TAM, TAM2, and TAM3 is a widely used extension. TAM2 considers the determinants of PU [73], and TAM3 includes the determinants of PEOU [74]. The UTAUT model was proposed as a systematic model based on TAM and other relevant theories. The framework of the UTAUT model contains facilitating conditions that can affect behavioral intention [75]. Additionally, it adopts a unique set of terms (i.e., performance expectancy, effort expectancy, and SI) to represent the variables in TAM.  
Thus far, several studies have highlighted the benefits of these models' applications to different technologies and contexts, including healthcare technologies [76], digital technology [77], online mapping technology [78], mobile library applications [79], automated vehicles [80; 81], and virtual reality technology [82; 83]. TAM and its extensions comprise various factors (e.g., SI) from other models for a better fit in different technologies and contexts [73; 84; 85]. Notably, the systematic literature review can compare the concepts and results of the prevailing literature and present a holistic picture of the field, but it fails to obtain reliable outcomes due to the different contexts. Therefore, meta-analysis has been widely adopted as a quantitative method. TAM and its extensions were also employed in many meta-analyses in numerous domains, such as e-health application acceptance [86], health information technologies acceptance [87], and e-business acceptance [88]. The above literature indicates that meta-analysis effectively unveils the underlying mechanism of acceptance of Industry 4.0 technologies in various sectors.
## 4 Results and Discussion
### Forest Ecosystems Characterization
#### 4.1.3 Characterization of Deforestation and Forest Degradation between 1991 and 2019  
Results concerning the forest perturbation (e.g., deforestation, degradation, or reforestation) over the study period can be found in Table 4 and Figures 11 and 12 below.  
\begin{table}
\begin{tabular}{c c} \hline
**Epoch** & **Coeff. Correlation R2 between Reference and Product** \\ \hline
1991 & 0.7559 \\
1995 & 0.6988 \\
2000 & 0.6991 \\
2002 & 0.7864 \\
2005 & 0.7903 \\
2010 & 0.7988 \\
2015 & 0.8336 \\
2019 & 0.8901 \\ \hline \end{tabular}
\end{table}
Table 3: Correlation between Forest Density value of reference data and map product.  
Figure 10: Forest Type year 2019.  
Figure 11: Evolution of Forest Perturbation in hectares, over the 1991 to 2019 period.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **1991–1995** & **1995–2000** & **2000–2002** & **2002–2005** & **2005–2010** & **2010–2015** & **2015–2019** \\ \hline Forest regeneration (ha) & 26 & 25 & 24 & 47 & 178 & 44 & 104 \\ Forest loss (ha) & 253 & 47 & 41 & 0 & 149 & 160 & 45 \\ Forest degradation (anthropogenic) (ha) & 384 & 363 & 12 & 22 & 72 & 46 & 53 \\ Forest degradation (natural) (ha) & 0 & 0 & 0 & 0 & 1 & 5 & 0 \\ \hline \end{tabular}
\end{table}
Table 4: Forest perturbation on Dilijian National Park over the 1991–2019 period.  
Figure 12: Forest disturbance map over the period 1991 to 2019.  
With the creation of the National Park in 2002, the forest degradation drastically dropped, and the forest regeneration increased, following the set-up of forestry management policies.  
The forest degradation, which is stable over the 2002-2019 period, has been attributed to anthropogenic origin unless ancillary data can prove that the cause is natural. Therefore, it is very likely that the anthropogenic forest degradation is strongly overestimated: forest wildfire monitoring service (NASA FIRMS) only monitors wildfires since year 2000, and no field data regarding other natural causes (storm, disease, etc.) were available.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 4 Results
### IPv6 Global Connectivity Test  
A test of connectivity and application usage was performed for the implementation across the global Internet with IPv6 as described in Section 3.3.2. With reference to the key evaluation requirements discussed in Section 3, this connectivity test further demonstrated _Implementation in IPv6_, and also use of eNIDs for both _Communication across IPv6 networks_ on wide-area connectivity, and _Use in existing IPv6 applications_.  
The program binaries were unmodified--they were installed directly from the FreeBSD repositories. By adding entries with names and I-LV values in /etc/hosts, the mechanism described in Section 3.4 ensured that all flows were ILNP flows at the network level. As the flows were generated at the client, the packets were captured at the server using tcpdump to show that ILNP packets had indeed traversed the global Internet.  
The applications operated correctly at the user level. From our results, we observe the following features:  
* All packets generated were ILNP packets (rsync,ping6, ssh).
* All packets were seen as IPv6 packets in the network and forwarded correctly.
* TCP flows were set-up and terminated correctly, with correct operation of TCP algorithms during data transfer (rsync, ssh).
* ICMPv6 packets were handled correctly (ping6).
* The server system did not use eNIDs to demonstrate that eNIDs can be used at the client independently of the server.
* The tests with rsync and ssh ran over secure connections showing further compatibility of eNIDs for use in secure communication for applications.
* _NID values are different for each flow._  
The last two features are easiest to observe in the ping6 packet capture. This test consisted of 3 runs of ping6 in succession, each of 10 packets. Each run of ping created a new socket and so each has a new eNID value.
## 4 Panel Data Analysis-Based Surface Texture Prediction Model
### Examination of Panel Data Analysis-Based Surface Texture Prediction Model
It is clearly seen that the cumulative axle load (CSAL), monthly average humidity (MAH), monthly rainfall (MR), CA and FAF ratios are negatively correlated with the surface texture of asphalt pavement, while the monthly average temperature (MAT) and FAC ratio are positively correlated with the surface texture of asphalt pavement. A fixed-effects type panel data analysis model with respect to skid number variation has been developed in Ref. [28]. The data were taken from the skid number results measured from 255 road sections for 15 years, and it was reported that the annual average rainfall was negatively correlated with skid number, whereas the traffic volume and the annual average humidity were positively correlated with skid number. That is, Ref. [28] concluded a positive correlation between the annual average humidity and the skid number (SN) of asphalt pavement, which is contrary to the panel data analysis model developed in our study. The conclusions in Ref. [28] are contrary to the engineering practice. Practice shows that the higher the humidity is, the lower the skid resistance is. However, the absolute value of the partial coefficient of MAT is very small, and MAT is insensitive to surface texture. \[ \begin{split}\text{SMTD}_{t}=3.8721-0.0588\log(\text{CSAL}_{it}) +0.0008\text{MAT}_{it}-0.0021\text{MAH}_{it}\\ -0.0007\text{MAR}_{it}-4.6375\text{CA}_{it}+1.0111\text{FAC}_{it}-2. 6346\text{FAF}_{it}+u_{i}\end{split}  \tag{12}\]
## 3 Results and Discussion  
Values such as vehicle delays, intensity, and queue lengths are critical parameters in evaluating the capacity and service levels of intersections. Among these parameters, the most frequently used vehicle delay value can be obtained in several ways depending on the infrastructure of the intersection or environmental factors. For example, in a four-leg junction, there is a possibility that vehicles coming from different points can cross in all four directions. The total elapsed time for a vehicle entering the intersection is the time spent from the moment a vehicle, coming from any current, enters the intersection arm until it leaves the intersection. In an intersection equipped with sensors, it is possible to obtain information about when the vehicle enters and leaves the intersection, how much time the vehicle spends at the intersection or how many vehicles are in the queue. As mentioned above, the queuing theory is used to make these calculations. Before the intersection intensities and delays are calculated by the queuing theory, it is necessary to examine the distribution of the number of vehicles during the day. Since the intersections within the scope of the study were equipped with sensors, the number of vehicles was examined in detail first. Therefore, as can be seen in the graphic below, the change in the number of vehicles during the day was analyzed through graphs. Figure 10 below shows the number of vehicles coming from three different directions of the intersection, the sketch of which is given in Figure 8.  
Similarly, these graphs were drawn for all the examined intersections to investigate the status of the intersections. The important issue in queue models is to determine the distribution characteristics of arriving vehicles and vehicles passing through the service point. The notation and calculations of the queue model change according to these distribution characteristics [52]. Since the vehicle queuing models at the selected intersections were examined in detail in the previous study, the outputs obtained in this study were mentioned [46]. According to the formulas results are given in Table 10 below. In the same analysis performed at the Kartal-Sahilyolu intersection in Istanbul, the performance of the phases, rather than the currents, was examined. Table 11 shows the results obtained within the scope of the evaluation made in 10 days. Examining the phases shows that the number of vehicles in the system and the mean time spent in some periods of the day are relatively high. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Phase** & \multirow{2}{*}{**Minute**} & **Cycle** & \multirow{2}{*}{**Green**} & **Red and** & \multirow{2}{*}{**Direction (Number**} & **Total** \\  & **Diff.** & **Time** & & **Time(sec)** & & **Yes(sec)** & **of Vehicles)** & **Vehicle** \\  & **(sec)** & & **(sec)** & & **Time(sec)** & & **Count** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 9: Data example of the phase coming from Adalhan Junction Alaaddin direction (sec).
## 5 Testbed and Simulation Results
### Performance Measurements
#### 5.3.2 Performance Measurements for 30 Node Simulation Setup  
Before rainfall we used the standard AOMDV protocol and after the rainfall we used our modified AOMDV protocol to compare its performance with the standard AOMDV. In this simulation scenario  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type/Protocol and Time** & \
\begin{tabular}{c} **Standard AOMDV** \\ **3 h before Rain** \\ \end{tabular} &
\begin{tabular}{c} **Modified AOMDV** \\ **3 h after Rain** \\ \end{tabular} \\ \hline Total Sent Size from cbr2 & 5000 kbit & 3749 kbit \\ Total Sent Size from cbr0 & 4375 kbit & 6874.7 kbit \\ End-to-End Delay (cbr2) & 256,910 ms & 349,410 ms \\ End-to-End Delay (cbr0) & 16,210 ms & 5.67 ms \\ Throughput (cbr2) & 2.65 kbps & 3.70 kbps \\ Throughput (cbr0) & 4.20 kbps & 6.20 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance Measurement before and during the time when node 0 is affected by rain.  
Figure 7: 30-Nodes Simulation Setup.  
we divided the simulation period depending on raining level, for instance with no rain the period from 0 to 900 s, light rain period from 900 to 2000 s and heavy rain from 2000 to 2500 s.  
An AWK script was built to calculate the average of Throughput and End to End Delay on node 0 and node 1 before and after node 1 is affected by rainfall. The highest priority data stream will be granted to cbr1 coming from node 1 because it is affected by rain and it has important data that should be send to the destination node (GW). We reroute cbr0 coming from node 0 traffic through node 10 which has data with low priority. In this way we reduce forwarding load on node 1, which means that the throughput and end to end delay will be better on cbr1 after node 1 affected by rain.  
Node 1 is affected by two levels of rain light rain and heavy rain, so we compared the Throughput and End to End Delay in two levels.  
The results listed in Table 5 clearly show improvement in Throughput and End to End Delay on cbr1 coming from node 1 which validates our assumptions and it shows that the event-driven approach does improve overall network efficiency.  
Figure 8 shows the throughput of Node 1, separately being affected by rain. The reason behind this increase in throughput is that at heavy and very heavy rain, we use the modified AOMDV protocol.