esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Use Cases for Calculating Periodicity Intensity
#### 2.2.3 Periodicity Intensity in Calf Movements  
The regularity of the sleep-wake cycle is important not just for humans but for other living animals. We investigated the strength of the circadian periodicity of new-born calves on a commercial farm and how that strength of periodicity changes throughout their first 6-8 weeks after birth [31]. Many animals, including cattle, are behaviourally synchronised with herds, coordinating reactions to external zeitgebers [32], so our interest in this use case is in the periodicity intensity of the circadian rhythm for the herd as a whole as well as for individual calves.  
We gathered data from an Axivity AX3 wearable accelerometer [33] affixed to a collar worn on the necks of each of the 24 dairy calves from just after birth. The calves were fed twice a day, were bedded daily and had human contact during these activities. Feeding times were at similar times to milking as they were fed whole milk. Thus their day-to-day activities would have been very structured and regular. Individual calf movements, as detected by the accelerometers, would have varied among calves as they play and interact and explore their surroundings, so despite the regular structure to feeding and sleep, there is scope for a lot of individual variability among them.  
Data from the accelerometers was sampled at 12.5 Hz and gathered for up to 8 weeks for each calf. It was processed in the way described in [34] by pre-processing into signal vector magnitude (SVM), a time series independent of sensor orientation and thus invariant to the movement of the collar around the neck. A Butterworth fourth-order band-pass filter [35] with frequency in the range 0.5 to 20 Hz was then applied to remove any white noise, and negative values were converted into absolute values. Movement values used for analysing periodicity were the aggregated mean of SVM calculated over non-overlapping 60-s windows.
# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 3 Presenting the Yolo V5 Network Architecture Model and Introducing Other Detection Models
### Yolo Introduction to Yolo Network Architecture Model  
3.3.1 Introduction to Yolo (Yolo V5)  
Yolo (You Only Look Once) is a famous neural network architecture in the field of fast and accurate object detection in images and videos. Yolo V5 is the fifth (05) generation version of Yolo, developed by Ultralytics. Yolo V5 has a number of outstanding features such as:  
Single shot detection: Yolo V5 is a one-stage object detection model, capable of detecting and classifying objects in a single run over the entire image without the need to create region pre-proposals like two-stage models.  
Backbone architecture: Yolo V5 uses a convolutional neural network (CNN) architecture as the \"backbone\" for feature extraction. The Yolo V5 instance uses EfficientNet architecture as the backbone, making the model capable of learning complex features and achieving good performance.  
Scale variants: Yolo V5 is designed with different versions (s, m, l, x), corresponding to small, medium, large, and extra-large model sizes. The availability of multiple versions allows the user to customize the model size to meet the requirements of speed and accuracy.  
Figure 5: Architectural process of Residual Network (ResNet).  
Figure 6: Architectural process of Inception ResNet.  
Multi-scale inference: Yolo V5 supports applying different scaling levels to the input image during the prediction process. This makes the model capable of detecting objects at different size ratios and achieving better performance on small or large objects.  
Platform support: Yolo V5 supports multiple platforms, including PyTorch and ONNX, making it easy for users to deploy and integrate into their projects.  
Yolo V5 has achieved high performance and fast speed in object detection in images and videos, and it has been widely used in practical applications such as autonomous vehicles, security monitoring, and detection of technical infrastructure objects in construction.
## 1 Introduction  
Reduction of surface recombination is very important for crystalline silicon (c-Si)-based electronic devices, and especially for high-efficiency c-Si solar cells. Because of the increasing need for lower-cost silicon solar cells, as Si material has a rather high cost, thinner Si substrates are required. Conventional surface passivation for Si involves the formation of a thin silicon dioxide (SiO\({}_{2}\)) layer. However, this process requires a long period at high temperatures, leading to an increased thermal budget. Due to these process-related issues, low-temperature surface passivation methods for both heavily doped and moderately doped c-Si surfaces have been widely studied. Several materials such as SiC, a-Si:H, and Si\({}_{3}\)N\({}_{4}\) have been considered for surface passivation [1]. Recently, Al\({}_{2}\)O\({}_{3}\) films grown via atomic layer deposition (ALD) have been reported to have good surface passivation on p-type c-Si [2, 3]. ALD provides a very precise control over the properties of the material, especially the uniformity and thickness of dielectric layers. However, an Al\({}_{2}\)O\({}_{3}\) passivation layer has negative fixed charges, which result in an inversion layer on the n-type wafer surface, causing short-circuit current loss due to the parasitic shunting between this inversion layer and metal contact [4]. HfO\({}_{2}\) films have positive fixed charges and this makes it very suitable for passivation of n-type silicon wafers. There is currently limited research focusing on the passivation of HfO\({}_{2}\) on n-type silicon wafers for silicon solar cells [5; 6].  
In this work, the surface passivation properties of the HfO\({}_{2}\) films deposited by a remote plasma atomic layer deposition system (RP-ALD) on n-type c-Si with different post-annealing temperatures are investigated. The structural changes and the electrical properties of the thin films induced by annealing temperature are characterized. Finally, PC1D (Version 5.9, University of New South Wales, Sydney, NSW, Australia, 2003) simulation for HfO\({}_{2}\)-passivated n-type silicon solar cells is presented.
# Polarization Transmission of Visible Light in Inhomogeneous Sea Fog Particle Environment
## 3 Indoor Simulation Experiment under Inhomogeneous Sea Fog Particle Environment
### Experiment Procedure  
In this experiment, we would set eight groups of fog filling time, namely, eight salt fog concentrations. As well, we use optical thickness to characterize the salt fog concentration. According to the Beer-Lambert law, the incident light with a light intensity value of \(I_{0}\) passes through the transmission medium and exits, and the outgoing light with a light intensity value of \(I\) is obtained. \(I\) and \(I_{0}\) have the following relationship:  
\[I=I_{0}\exp(-\mu_{e}L) \tag{19}\]  
The calculation formula of the known optical thickness is \(\tau=\mu_{e}L\), and \(L\) is the thickness of the medium. The transmittance has the following calculation formula: \(T=I/I_{0}\). The relationship between the transmittance and optical thickness is as follows:  
\[T=\frac{I}{I_{0}}=\exp(-\tau) \tag{20}\]  
The relationship between fog filling time and optical thickness is shown in Table 4.  
The test experiment is divided into two stages. In the first stage, the polarization transmission experiment is conducted with three wavelengths (450, 532, and 671 nm) as variables. In the second stage, the polarization transmission experiment is conducted with these five relative humidity conditions (45%, 55%, 65%, 75%, and 85%) as variables. Turning on the laser, and the experiment starts under eight different fog-filling time conditions. The incident polarized light is scattered, absorbed in the salt fog, and then exits the salt fog environment. The outgoing polarized light is divided into  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Fog filling time/min** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ \hline
**Optical thickness** & 0.16 & 1.12 & 2.45 & 3.12 & 4.08 & 4.32 & 4.67 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relationship between fog filling time and optical thickness.  
Figure 5: Diagram of the indoor simulation experiment setup under an inhomogeneous sea fog particle environment.  
Figure 6: Physical map of the device: (**a**) transmitting ends; (**b**) box; (**c**) receiving ends.  
One way is received by the optical power meter to obtain the light intensity value of the outgoing light, which is used to calculate the optical thickness. The other way is received by the polarization state measuring instrument to obtain the DOP of the outgoing light.
## 3 Results
### Dry Matter Yields  
Alfalfa dry matter measurements indicated that the first five cuttings were the most productive cuttings across the experimental sites (Figure 7). Early season DM yields varied from 3.29 (cut 1) to 4.21 (cut 4) Mg ha\({}^{-1}\) over the first five cuttings. However, early-season yields were more variable across the sites and years (Figure 7). The standard deviation value of DM yield was 1.24, 0.81, 0.80 Mg ha\({}^{-1}\) for the first through third cuttings, respectively. The high SD value of the first cutting is due to a greater alfalfa yield in the first harvest after establishing alfalfa fields. Alfalfa fields often have a higher yield at the first harvest after planting, that typically occurs 5-6 months after planting. This is not something that happens during the following seasons and consequently a greater standard deviation was observed for the first cutting.  
Lower mean DM values were obtained for the last four cuttings, varying between 2.87 (cut 6) and 1.48 Mg ha\({}^{-1}\) (cut 9). The maximum and minimum DM yield observed per cut was 5.43 and 1.24 Mg ha\({}^{-1}\) across the sites and the seasons, respectively.  
Seasonal alfalfa DM yields varied across the experimental sites. The mean seasonal DM value varied from 23.01 Mg ha\({}^{-1}\) at site E (the 2020 season) to 29.90 Mg ha\({}^{-1}\) at site  
Figure 6: 30 min Normalized Difference Vegetation Index (NDVI) values at site D over five harvest cycles of May through September in the 2019 season. Red arrows show the cutting dates during the period.  
Figure 7: Dry matter yields per each cutting for the different experimental sites (site A through site I) and crop seasons. The experimental sites had 8–9 cuttings over the study period. SD standard deviation.  
B (the 2019 season) (Figure 8). Overall, alfalfa yields were greater in the first year after the establishment than the following years, and generally decline over the subsequent years (Figure 8). Plant stand loss over the time is likely one of the main reasons of yield reduction. An average of 26.25 Mg ha\({}^{-1}\) was determined as the seasonal alfalfa yield of the experimental sites.  
While we expected greater crop water use at the fields under SDI due to better water distribution uniformity over time and space, the findings of this study and an earlier study [1] clearly revealed that this expectation depended upon how irrigation water was managed. For instance, the soil moisture and applied water data verified that sites C and G in the 2019 season likely experienced moderate water stress even though farmers had no plan for deficit irrigation at these sites. The soil moisture tension data clearly demonstrated that site C could have benefited from one extra irrigation event between mid-March and late-April 2019 (Figure 9a). The cumulative ETa was 163 mm at this site for that harvest cycle while the total water applied was 148 mm, indicating deficit irrigation practices at that site.
# Synthesis and Characterization of Unsaturated Succin Acid Biobased Polyester Resins
## 3 Results and Discussion
### Characterisation of Unsaturated Resins
#### 3.2.4 Viscosity Measurements  
The viscosity of unsaturated polyester resins is a key parameter that determines the possible applications of these materials. It is a function of the resins' molecular weight [35], functional groups such as terminal -OH groups, or intermolecular -H bonds [11], and can  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **M\({}_{\text{w}}\) (g/mol)** & **M\({}_{\text{n}}\) (g/mol)** & **D** \\ \hline PESM 50-50 & 4700 & 1600 & 2.8 \\ PESM 40-60 & 5100 & 1700 & 2.9 \\ PESM 25-75 & 4600 & 2050 & 2.2 \\ PPEGSM 50-50 & 5600 & 2100 & 2.7 \\ PPEGSM 40-60 & 6600 & 2700 & 2.4 \\ PPEGSM 25-75 & 9700 & 3600 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Size-exclusion chromatography (SEC) results for the produced resins.  
Figure 3: FTIR collected spectra of the resins before curing.  
be modified with the addition of reactive diluents [36; 37]. In our work, the viscosity of the obtained resins was measured at five different temperatures, applying a constant shear rate of 100 s\({}^{-1}\). The results are shown in Figure 4. For each group of resins, containing PEG200 and EG, respectively, viscosity increased with an increasing amount of MA incorporated in the polyester backbone. This was a direct consequence of the increased molecular weight of the materials, in accordance with the SEC measurements. In all, the resins exhibited high viscosities at ambient temperatures, drastically limiting their potential applications. Therefore, the use of reactive diluents is mandatory to facilitate processing or to increase the applied temperature to above 40 \({}^{\circ}\)C.
## 3 Results  
### Experimental Setup  
Figure 10 shows an experimental setup of the proposed system, which is simply composed of a pair of the CMA and beam projector. As a projector, the LG beam projector (Model: LG PF85K) whose resolution, brightness and size were \(1920\times 1080\) pixels, \(1000\) ANSI-lumens and \(275\times 219\times 45\) mm, respectively, was used. The total diverging angle (\(2\theta_{p}\)) and depth-of-field (DoF) of this projector were estimated to be \(39^{\circ}\), and ranged from 62 cm to 372 cm, respectively. In addition, for the experiments, a \(22^{\prime\prime}\)CMA whose pitch and curvature radius were \(7.47\) mm and \(15.50\) mm, respectively, was fabricated  
Figure 9: Optical configuration of the integrated points of 3-D object images in the left/right viewing zones.  
and located at the distance of 800 mm from the beam projector. Here, the focal length of an elemental CM of the CMA was calculated to be 7.32 mm.  
As mentioned above, to test the feasibility of the proposed system in the practical application, a 22\({}^{\prime\prime}\)CMA was specially designed and fabricated. That is, the CMA was manufactured by coating the aluminum particles being spread from the thermal evaporator on the surface of a base convex lens array to make a high-reflector aluminum layer. Here, aluminum was used as a coating material because the reflectance for the visible light becomes much more than 90%. Thus, the convex lens array with a thin aluminum layer acted as a convex mirror array. Each convex mirror had the same curvature and pitch as the base convex lens, but its focal length became much shorter than that of the base convex lens. In the experiment, pitches and focal lengths of the base convex lens and convex mirror were 7.47 mm, 29.88 mm, and 7.47 mm, 7.32 mm, respectively. That is, the focal length of the convex mirror became 4.08-fold shorter than that of the corresponding convex lens. In fact, as the focal length of the convex mirror got shorter, its \(f\)-number (#) also decreased, which then resulted in an increase of the viewing angle of the corresponding CMA by a factor of \(2\times\arctan(1/2/\#)) [24]. Table 1 shows the detailed specification of the pickup and display setups.
## 4 Simulation and Evaluation
### Performance Evaluation
#### 4.2.4 Impact of Window \(K\)  
The simulation experiments above have been done with \(k\) equal to 9. However, as the value of \(k\) reflects how much attention is paid to the historical link quality, different values of k result in different simulation result. Figure 7 shows the success rate of LQER and energy consumption of LQER compared to MHFR with the values of \(k\) from 3 to 13, where the node number is 500. It is not that the larger \(k\) is, the better the protocol performs. If \(k\) is too large, it requires for more storage. From Figure 7, we can see that success rate keeps increasing while \(k\) is no larger than 9. After 9, the increase is not so obvious and seems stable. Similar is the energy consumption. It tends not to decline when \(k\) is larger than 9. Thus \(k\)=9 may be a best choice for link quality estimation.
## 4 Discussion  
From the results presented in the previous two sections, it is noted that the proposed CPM-GFDM always performs better than conventional GFDM for several values of modulation index \(h\) when using a matched filter receiver. In the case of a zero-forcing receiver, only when \(J=8\) are there modulation indices that perform better than conventional GFDM if the channel is AWGN. When the channel is frequency-selective, the number of \(h\) values that outperform conventional GFDM are much less than those for the AWGN channel. However, unlike for the AWGN channel, for \(J=4\) there are values of \(h\) that perform better than conventional GFDM. In the case of the AWGN channel, the best performing modulation indices are \(1/2\), \(3/10\) (appearing twice), \(5/16\) (appearing twice), and \(7/16\), as is evident from Table 4, while in the case of frequency-selective channels, the best performing modulation indices are \(3/10\) (appearing thrice), \(5/16\) and \(7/16\) (appearing twice), as has been shown in Table 4. Finally, it can be concluded that the proposed CPM-GFDM configuration outperforms conventional GFDM.
# Generation of Data-Driven Expected Energy Models for Photovoltaic Systems
## 3 Results and Discussion
### Limitations and Future Work  
The methodological approach of this analysis was strongly guided by available data. However, future work could extend these methods to consider: (1) energy generation at finer resolutions, (2) additional co-variates, and (3) alternate model formulations. For example, scaling could be used to consider alternate frequencies (beyond the hourly intervals considered in this study) post-evaluation. The methods used in this analysis explicitly omitted variables not included in current standard models (e.g., [15]). However, future assessments could more explicitly incorporate co-variates such as temperature, wind speed, and even age of the site. The latter would especially enable active consideration of degradation influence, which can influence long-term energy generation of PV sites [35]. Additional co-variates (such as type of inverters and modules) could also be included in subsequent iterations to capture more subtle impacts associated with differing site designs. Finally, future work could consider alternate model formulations (e.g., splines) to improve model continuity and better capture energy generation for smaller system sizes.
## 4 Key Enabling Techniques
### Ultra-Dense Network (UDense Net)  
Cell densification has become the major contributor to enhance capacity in previous generations of wireless communications, about 4-5 macro-cell base stations (MBSs) / km\({}^{2}\) in 3G, around 8-10 micro-cell BSs/km\({}^{2}\) in the 4G and expected increase of 40-50 small-cell BSs (SBSs)/km\({}^{2}\) in 5G [94]. The principal goal of cell densification is to address the coverage problem by spatial frequency-reusing and offloading the data traffic to the SCs. High splitting gain by dense small cells was a major challenge. Hence, an ultra-dense network (UDN or UDense Net), with one macro-cell BS that serves a huge variety of small-cell BS's, is widely considered a key player in achieving capacity. UDense Net is known as a promising technology to enhance SE, capacity, and significant system performance by utilizing spectrum opportunities locally for every single hertz. It also possesses the advantages of low transmission power, effective expansion of coverage, the throughput of the network, and flexible deployment [95, 96]. However, several challenges including security, interference, mobility support, flexible back-haul connectivity, and energy consumption also exist. Similarly, In [59], two architectures: self-back-hauled small cells for UDense Net and direct access were investigated and results showed a 30% rate improvement. In [69], increased handover rates by using a trace methodical approach to secure the network bycontinuous monitoring. [PERSON] et al. [97] suggested a network architecture for UDense Net by two effective localized mobility management techniques which have performance evaluation results of the average handover signaling costs, average packet delivery cost, average latency, higher handover latency and average signaling load to the core network. [PERSON] et al. [98] addressed the problem of anti-jamming communication in an unknown environment for UDense Net by a deep reinforcement learning base anti-jamming algorithm. This algorithm is represented in an actor-critic framework. For the selection of anti-jamming action, a convolution neural network (CNN) is used as an actor, and a deep neural network (DNN) as the critic for the value estimation.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.2 Zero-Knowledge Proof  
Most location-based services (LBSs) require proof of location (PoL) to prove that the user satisfies the service requirement, which exposes the user's privacy. [PERSON] et al. [154] propose a zero-knowledge proof of location protocol to better protect the user's privacy. With this protocol, the user can choose necessary information to expose to the server, so that hierarchical privacy protection can be achieved.  
Another application case is authentication, where an entity proves that it knows a password, without revealing it to the verifier. Furthermore, it can be applied to show the steps in a protocol or process have been done correctly (honest behavior). This approach has been used in smart meter and electronic toll pricing [149].
# A New Design Method of Shield Tunnel Based on the Concept of Minimum Bending Moment
###### Abstract  
As the soil-resistance coefficient in a soft soil area is small, overlarge bending moment may cause exceeding transverse deformation for the shield tunnel and cause structural diseases and waterproof failure at the longitudinal segment joints. Hence, a new idea of cross-section design for a minimum bending moment shield tunnel was proposed. This article has first put forward the concept of a zero bending moment shield tunnel. Then, based on rational and feasible hypotheses, a structural mechanical model and an analytical expression of axis for the cross-section of the zero bending moment shield tunnel was obtained, and computational formulas of internal force and key geometry parameters were given. Based on the case of the metro shield tunnel constructed in the Shanghai soft soil area, the zero bending moment shield tunnel was designed, and its characteristics were analyzed. Considering only one cross-section of shield tunnel can be adopted in one metro line, the design method and procedure of the minimum bending moment shield tunnel were put forward. Finally, taking one of the Shanghai metro lines as an example, a cross-section of a minimum bending moment shield tunnel was designed, and its bending moment was compared with the bending moment of a circular section shield tunnel, which had the same horizontal diameter. The comparison revealed that the cross-section of the minimum bending moment can significantly reduce the bending moment of shield tunnel.  
minimum bending moment; cross-section; shield tunnel; soft soil area; design method +
Footnote †: journal: Applied sciences