esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
## 2 Related Work
### Fully Supervised Drivable Road Area Segmentation  
With the development of deep learning, Convolutional Neural Networks (CNNs) have been feasible solutions for semantic segmentation [17; 18; 19; 20; 21]. Some of them have been adapted and applied for drivable road area segmentation. For example, [PERSON] et al. used a deep CNN to segment drivable road areas and the experimental results showed that it outperformed those conventional SVM-classifier-based techniques [22]. Also, a fully convolutional residual network was further implemented for drivable road area segmentation, illustrating that deeper networks can achieve better results. Subsequent researchers made structural improvements based on prototype models. For example, an up-convolutional network was proposed in [23], all-layer and stage-layer modules were designed in [24], a siamesed fully convolutional network (s-FCN-loc) was proposed in [25], and a reverse attention network was designed in [26]. Furthermore, instead of improving the structure of a single model, in [27], a CNN was combined with Long Short-Term Memory (LSTM) for better drivable road area segmentation performance. In addition, for some special tasks such as segmentation with fisheye lens, [PERSON]. [28] used ResNet101 v2 as a feature extraction module to achieve accurate segmentation results for road surfaces. To improve the real-time performance of CNNs, [PERSON]. [29] used an uncertainty-aware symmetric network based on asymmetric dilated convolution and validated it on embedded devices. Yolo-based models [30] also performed well in this field. YoloP [31] and YoloPv2 [32] are capable of achieving segmentation accurately and efficiently, and are able to complete the perception of lanes and traffic objects at the same time. Other methods that allow for the segmentation of the drivable area in multiple-task scenarios include DLT-Net [33], HybridNets [34], and GBIP-Net [35]. ULODNet achieved the segmentation of drivable areas by detecting lanes and obstacles on roads [36]. These studies have demonstrated that CNN-based models can achieve remarkable accuracy in drivable road area segmentation. However, a prerequisite for fully supervised algorithms to achieve good results is that the amount of data needs to be large enough and of a high-enough quality. By presenting the related works in Table 1, it is seen that even if a large number of images can be obtained relatively easily, fine labels must be a time-consuming and expensive task, especially in drivable road area segmentation. Therefore, to overcome this limitation, we propose a semi-supervised drivable road area method, which can achieve satisfactory performance with few annotations.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 6 Time-Domain Characterization
### Transfer Function  
Since the SWB systems use short pulses to transmit and receive signals, it was essential to study the transfer functions for evaluating the proposed antenna's performance and designing the transmitted pulse signals.  
In the transfer function (S21) of the band-notched SWB antenna system, there was a sharp dip at the notch frequencies. From Figure 18, it was observed that the magnitude of S21 in both configurations was flat over the entire SWB spectrum, except at band-notched regions. In the case of the face-to-face orientation, attenuations of 54 dB, 60 dB, and 37 dB were observed at the first notched band centered at 2 GHz, the second notched band at the 6 GHz region, and the third notched band at the 10 GHz region, respectively. In the side-by-side configuration, the attenuation was comparably less than the face-to-face configuration at band-notched regions.
# Antennas for Licensed Shared Access in 5G Communications with LTE Mid- and High-Band Coverage
## 4 Wideband Antenna for LSA Bands and LTE Mid and High Bands  
A wideband antenna (Ant#3) is presented in this section. Unlike the previous dual-band antenna (Ant#2), this is a wideband antenna covering both the mid and high bands. In addition, Ant#3 is smaller than both Ant#2 and Ant#1.
## 3 Results and Considerations
### Maximum Efficiency for Respective Condition and MEPC. 227(64) Regulation Compliance  
At the 64 th session of the Marine Environment Protection Committee (MEPC), International Maritime Organization (IMO), MEPC. 227(64) was adopted, replacing MEPC. 159(55). Consequently, the regulations were strengthened for nitrogen (less than or equal to 20 \(\mathrm{Q_{i}}/\mathrm{Q_{e}}\) mg/L or 70% removal) and phosphorus (less than or equal to 1.0 \(\mathrm{Q_{i}}/\mathrm{Q_{e}}\) mg/L or 80% removal) in advanced shipboard wastewater treatment. In this study, the results according to the optimal conditions and compliance with MEPC. 227(64) were confirmed. Table 3 shows the MEPC. 227(64) and research result comparison.  
Figure 8: Changes in removal efficiency based on changes in mixing: aeration times (T-N).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Remark** & **MEPC. 227(64)** & **Research Result (Maximum Efficiency)** \\ \hline Chemical Oxygen Demand (COD) & Less than or equal to 125 mg/L & 16 mg/L Removal rate: 94.67\% \\ Total Nitrogen (T-N) & Less than or equal to 20 \(\mathrm{Q}\)/\(\mathrm{Q}\)e & 3.984 mg/L Removal rate: 97.34\% \\ Total Phosphorus (T-P) & Less than or equal to 1.0 \(\mathrm{Q}\)/\(\mathrm{Q}\)e & 3.36 mg/L Removal rate: 96.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: MEPC 227(64) and Research Result Comparison.  
Figure 9: Raw water and treated water changes based on changes in mixing: aeration times (T-P).  
The COD concentration suggested in MEPC. 227(64) is less than or equal to 125 mg/L on average, and in this study, it is 16 mg/L, showing a satisfying numerical value. In the case of T-N, the concentration suggested by MEPC. 227(64) is less than or equal to 20 mg/L or at least 70% removal for raw-water and influent water, but in this study, the results are 3.984 mg/L and 97.34% removal, satisfying the regulation. Furthermore, in the case of T-P, the concentration suggested in MEPC. 227(64) is 1.0 or at least 80% removal rate for raw-water and influent water. In this study, the T-P concentration is 3.36 mg/L and the removal rate is 96.27%, i.e., the removal rate is satisfied but the concentration is unsatisfactory. Nevertheless, it is determined that there will be no problem since the removal rate for the raw-water satisfies the MEPC. 227(64) regulation. It is due to the higher concentration of T-P compared to the concentration of raw-water, and in future, specific mechanism and other studies will be necessary for phosphorus treatment such as condensation for its removal.
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
## 2 Engineering Overview and Monitoring Point Layout  
The Nongye Road-Dongfeng Road station interval of the Zhengzhou Metro Line 1 phase 1 features a small radius curve, specifically with a radius of 350 m. The tunnel is buried under a sandy layer. Since its opening on 28 December 2013, the cumulative settlement range in the area has been recorded as 63.83-84.54 mm, which is notably higher compared to the settlement observed in the adjacent straight section. At the same time, the adjacent settlements of the upper and lower tunnels in this interval are not consistent, with a maximum difference of about 27 mm, and there is a trend of further settlement. Therefore, the test site was selected in the shield tunnel interval of the Nongye Road station-Dongfeng Road station interval on the Zhengzhou Metro Line 1. The terrain of the test site is level, with a ground elevation of 87.9-89.8 m.  
Based on the geological investigation report, the terrain of the area is the Yellow River channel and alluvial plain, and there are no special rock and soil types within the site. The exposed strata in Zhengzhou are all Quaternary strata, and the deposition time is from Lower Pleistocene to Holocene. The total thickness of the layer is 50-200 m. From southwest to northeast, the layer thickens from a thin bed and is in unconformable contact with the underlying Tertiary strata. The stratigraphic lithology in this area is mainly composed of Quaternary Holocene (Q4) strata with a depth of 30 m, silty soil (slightly dense to medium dense) and silty clay with a depth of 0-20 m, containing silty sand and fine sand, and medium dense to dense fine sand with a depth of 20-30 m. The strata in the 30-50 m depth range are mainly silty clay (plastic-hard plastic) in the Upper Pleistocene (Q3) of the Quaternary System. Silty sand (dense), mainly yellowish brown, with few calcium nuclei and iron rust spots. The tunnel is mainly located in silty clay, silty sand, medium sand stratum. The buried depth of the site's groundwater level is 9.37-15.4 m, and the groundwater is mainly stored in silty clay, silt, and silty soil layer below 9 m.  
The pore water pressure testing elements were selected from the ATJ-3030 type pressure water level meters produced by the Sichuan Aojitao Company, providing a measurement accuracy of \(\pm\)0.05% F-S. Since Line 1 has been put into operation, a total of 11 monitoring points have been set up along the subway line at the tunnel bottom, all at the same horizontal level. Among these points, K3 and K11 were selected for layering layout, with an additional three vertical monitoring points
# Towards a Maturity Model for IoT Adoption by B2C Companies
## 6 Expert Views
### Expert 1 Interview  
When asked about the most important in the IoT products, the expert built his response around the significance of using data collected by the IoT devices to make better and faster decisions. Even more, this message is traceable throughout the whole interview--IoT is not about smart things, but about data and understanding how it can be used to the benefit of the people. From this, it is quite obvious that _data_ is the central concept in IoT and it definitely should be one of the dimensions of the model. Moreover, since the real value of IoT comes not from merely collecting data, but from effectively using it to improve decision making, a _data management_ sub-dimension should be included in the model as well to define the collection, management, and use of the data. In addition, because of the fact that the major benefit of using IoT is making better decisions, it is possible to track the maturity of IoT adoption in the company through the way IoT is integrated into its decision-making process. Consequently, the _decision-making_ sub-dimension should be added to the model.  
Further on, the expert explained the opposite side of the benefits brought by collecting data--the risk of it being stolen and used in fraudulent interests. The issue of data security needs to be addressed by all parties involved. That is, product developers need to make sure they are employing up-to-date security measures, while the users should have the latest software updates installed and change their passwords regularly. Closely tied to security is the issue of information privacy. However, the expert emphasised that it is more of a legal matter than a technological one. It is the law that defines what privacy is and what companies need to abide by. Having said that, it is possible to identify _data security and privacy_ sub-dimension(s) under the dimension of data. Whether data security and data privacy should be included as separate or as one sub-dimension was put under discussion with the experts in the second series of interviews.  
What is more, organisational _strategy_ sub-dimension under the _organisation_ dimension was identified from the interview. The expert explicitly stated that the integration of IoT in the organisation needs to be fully aligned with its strategy.  
Interesting is the discussion of the expert on the transition of IoT products from being sold as a product to being sold as a service. This is due to the fact that after the product is sold to the client, it does not end there. Data from the IoT devices are being continuously collected and interpreted, and the continuity of this process is what transforms the IoT into a service model. A possible (sub-)dimension that can be identified is therefore _business models_.
# Microwave Photonic Devices Based on Liquid Crystal on Silicon Technology
###### Abstract  
This paper reviews the recent developments in microwave photonic devices based on liquid crystal on silicon (LCOS) technology. The operation principle, functions and important specifications of an LCOS based optical processor are described. Three microwave photonic devices, which are microwave photonic notch filters, phase shifters and couplers, reported in the past five years are focused on in this paper. In addition, a new multi-function signal processing structure based on amplitude and phase control functions in conjunction with a power splitting function in a commercial LCOS based optical processor is presented. It has the ability to realize multiple time -shifting operations and multiple frequency-independent phase shifting operations at the same time and control multiple RF signal amplitudes, in a single unit. The results for the new multi-function microwave photonic signal processor demonstrate multiple tunable true time delay and phase shifting operations with less than 3 dB amplitude variation over a very wide frequency range of 10 to 40 GHz.  
microwave photonics; optical signal processing; filters; phase shifters; true time delays +
Footnote †: journal: Applied Sciences
## 3 Results
### Angstrom-Prescott Equation  
The Angstrom-Prescott (A-P) formulation is the most widely used equation between relative SD (\(\mathrm{S_{d}}/\mathrm{S_{0d}}\)) and solar radiation. The A-P equation is given by:  
\[G_{d}=G_{0d}\times(a+b\times(S_{d}/S_{0d})) \tag{8}\]  
where \(\mathrm{G_{d}}\) and \(\mathrm{G_{0d}}\) are the daily global and extraterrestrial horizontal solar radiation measured in MJ/m\({}^{2}\). \(\mathrm{S_{d}}\) and \(\mathrm{S_{0d}}\) are, respectively, the actual SD and the astronomical daylength in h. \(\alpha\) and b are empirical regression coefficients. An example of the linear regression of the daily clearness index (\(\mathrm{K_{T}}\)) and the relative sunshine duration (\(\sigma\)CS) estimated from the CS sunshine recorder for Larnaca during the period 2009-2012, is given in Figure 12. As shown in the graph, most of the data are within the prediction intervals. The coefficient of determination (R\({}^{2}\)) is 0.901.  
The major drawback of this method is that the empirical coefficients are site specific and thus dependent on local climate conditions. As is indicated from Table 10, \(\alpha\) ranges from 0.227 to 0.298, while b varies from 0.468 to 0.533. The dispersion of the empirical values is small, since all the stations have almost the same climatic conditions. The coefficients of determination are very high, ranging from 0.728 to 0.907. The lowest values are obtained in cases when the relative sunshine was measured from automatic stations equipped with CSD3 recorders. Otherwise, R\({}^{2}\) is around 0.90. It has to be noted that the empirical coefficients for the same location but in different time periods are almost similar. We should emphasize that the CS measurements compared with those of an electronic sensor affect the values of the coefficients. Furthermore, the empirical regression coefficients might vary considerably throughout the year due to seasonal meteorological changes.  
Figure 13 presents the quadratic relationship between the daily direct horizontal irradiation (\(\mathrm{B_{d}}\), MJ/m\({}^{2}\)) and the daily SD obtained by CS and Pr at Athalassa during the period 2012-2015. The coefficient of determination (R\({}^{2}\)) in both graphs is very high (0.924 and 0.933, respectively).
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analysis** & **Sample 1** & **Sample 2** & **Sample 3** & **Average** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline C (\%) & 53.63 & 49.99 & 55.87 & 53.16 & 2.42 \\ H (\%) & 6.62 & 6.51 & 6.18 & 6.44 & 0.19 \\ N (\%) & 0.13 & 0.10 & 0.12 & 0.12 & 0.01 \\ O (\%) & 39.62 & 43.4 & 37.83 & 40.28 & 2.32 \\ FC (\%) & 21.44 & 19.99 & 20.95 & 20.79 & 0.60 \\ Volatiles (\%) & 76.89 & 79.65 & 76.69 & 77.74 & 1.35 \\ Ash (\%) & 1.33 & 2.01 & 1.11 & 1.48 & 0.38 \\ Moisture (\%) & 0.84 & 1.34 & 0.91 & 1.03 & 0.22 \\ HHV (MJ-kg-1) & 21.27 & 19.31 & 21.67 & 20.75 & 1.03 \\ Initial sample weight (g) & 501.24 & 505.59 & 510.67 & 505.83 & 3.85 \\ Weight after thermal & 411.92 & 422.93 & 412.01 & 415.62 & 5.17 \\ Treatment (g) & & & & & \\ Mass loss (\%) & 17.82 & 16.35 & 19.32 & 17.83 & 1.21 \\ EDR & 1.22 & 1.03 & 1.18 & 1.14 & 0.08 \\ MY (\%) & 82.18 & 83.65 & 80.68 & 82.17 & 1.21 \\ EY (\%) & 100.23 & 86.17 & 95.44 & 93.95 & 5.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained in analysis conducted with samples thermally treated at 200 \({}^{\circ}\)C. EDR, energy densification ratio; MY, mass yield; EY, energy yield. with a standard deviation of 1.03. The average weight of samples used in this test at 200 \({}^{\circ}\)C was 505.83 g, with a standard deviation of 3.85, and the average weight of samples after the test was 415.62 g, with a standard deviation of 5.17. This difference indicates an average loss of mass of 17.83%, with a standard deviation of 1.21. The calculation of EDR, MY, and FY presented average values of 1.14%, 82.17%, and 93.95%, with standard deviations of 0.08, 1.21, and 5.84, respectively. Table 6 presents the results obtained for the samples after thermal treatment at 300 \({}^{\circ}\)C, according to the procedure presented in Section 3.2. As can be seen in the results of the elementary analysis, the average values obtained for carbon, hydrogen, nitrogen, and oxygen were 78.61%, 6.54%, 0.43%, and 14.42%, to standard deviations of 1.21, 0.14, 0.04, and 1.15, respectively. Thermogravimetric analysis gave mean values for fixed carbon, volatiles, ash, and moisture of 68.52%, 27.10%, 4.39%, and 3.79%, with standard deviations of 1.56, 1.90, 0.37, and 0.30, respectively. The HHV, calculated using Equation (2), presented an average value of 33.36 MJ\(\cdot\)kg\({}^{-1}\), to a standard deviation of 0.47. The average weight of the samples used in this test at 300 \({}^{\circ}\)C was 507.86 g, with a standard deviation of 4.60, and the average weight after the test was 156.45 g, with a standard deviation of 4.99. This difference indicates an average loss of mass of 69.20%, with a standard deviation of 0.77. The calculation of EDR, MY, and FY presented average values of 1.84%, 30.80%, and 56.69%, with standard deviations of 0.08, 0.77, and 3.80, respectively.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 2 Influential Factors on the Aging and Reliability of EMD Components
### Electromagnetic Stress
#### 2.1.2 Magnetic Field  
The magnetic field contributes to stress when magnetic forces act on cables, motor windings, and conductors carrying electric current. The magnitude of these forces depends on factors such as the magnetic field density, the amount of current flowing through the conductor, and the conductor's orientation within the magnetic field. Stronger currents and higher magnetic field densities result in greater forces, which can vary dynamically with changes in electrical load and operating conditions [23]. These magnetic forces induce mechanical stresses on the materials, potentially leading to the deformation and degradation of insulation over time, particularly in motor windings. The interaction between the magnetic field and the current can also create vibrations and localized heating, further accelerating the degradation of materials [24]. Additionally, a time-varying magnetic field induces unwanted voltages and currents, which can lead to parasitic effects such as EMI, negatively impacting system performance and stability.  
Residual magnetic flux in the core (remanence) can cause additional stress in electric drives. When the core is not fully demagnetized between operating cycles, the remaining flux can result in unwanted magnetic forces and higher core losses, which reduce efficiency and contribute to localized heating [25]. This effect is particularly problematic during high-frequency operation as the hysteresis effects in the core material become more pronounced, further degrading performance. Additionally, high inrush currents may occur when residual magnetic flux is present in the magnetic core. In [26], the authors propose a method and a laboratory setup to determine the residual magnetic flux value in single-phase transformers. Future research could focus on adapting this method to determine the residual magnetic flux in induction motors.  
As stated in [27], to mitigate these effects and ensure long-term reliability, strategies such as effective shielding, grounding, optimal phase-angle switching, and the use of high-quality materials are essential.
## 2 Related Work
### Apache Accumulo  
Apache Accumulo is a distributed key/value storage system to store and manage large data sets across a cluster. It stores data in table, and a table is divided horizontally into tablets. The master of an Apache Accumulo cluster assigns a group of tablets to a tablet server. Figure 1 shows this process. This allows row-level transactions to be processed without the need for distributed locking or complex synchronization methods. When a client inserts or queries and a node is added or removed from the cluster, the master migrates the tablets so that the ingest or query processing load is distributed across the cluster.  
As shown in Figure 2, when a write operation is passed to a proper tablet server, it is first written to the WAL (Write Ahead Log) as a log and inserted into memory called a MemTable. When the MemTable reaches a certain size, the tablet server writes the sorted key-value pairs to HDFS as an RFile. This process is called minor compaction. After that, the MemTable is created again and the compaction is written to the WAL.  
When a tablet server receives a request to read data, the tablet server performs a binary search on the index blocks associated with MemTable and RFile to perform the search. When the client performs  
Figure 1: Data distribution of Apache Accumulo [16].  
Figure 2: Tablet server of Apache Accumulo [16].  
a scan, multiple key-value pairs are returned. If caching is enabled for the table, the index or data block is stored in the block cache for future scans.  
The proposed spatiotemporal method uses the data distribution feature of Apache Accumulo like Geomesa to improve its parallelism for ingestion and query operations. Each spatiotemporal record is mapped to one cell of the 3-dimensional space grid according to its GPS location and timestamp. Then, a number for the cell is assigned by the Hilbert Curve [19] technique. The Hilbert Curve number for the cell is used to determine which tablet server should take care of the record.
# Even Dispersion Design for a Compact Linear Loudspeaker Array with Adaptive Genetic Algorithm
## 5 Experiment
### Experimental Results and Analysis
#### 5.2.2 Optimized SPL Distribution  
With the same experiment setup, the proposed method is applied to optimize the double swept-frequency signal for all the loudspeakers. The time-varying SPL distribution of the proposed method shows much better performance than the non-optimized result as shown in Figure 8c. To show the validity of optimization directly, the SPL distributions in different positions within different times are normalized and averaged. In Figure 9, the non-optimized SPL distributions range in \(\pm 5.27\) dB. With optimization, the SPL distributions are much smaller, which range in \(\pm 1.33\) dB.