esa-sceva/satcom-chunk-collection huggingface dataset:
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 5 Pattern Matching-Based Load Anomaly Detector
### Pattern Matching Scheme
#### 5.2.2 Comparison of the Classification Results  
According to the classification results obtained from the improved SVM, there are two cases.  
1. If the prediction result and the real load exhibit the same pattern (i.e., \(P_{prediction}\ =\ P_{real}\)), then the current load data are labeled normal.
2. If \(P_{prediction}\) and \(P_{real}\) exhibit different learned patterns, there are three potential reasons for the difference: (1) the real data are still normal, but it is affected by daily random events, which is unpredictable; (2) a classification error occurs; or (3) the real data are abnormal, and nanotechnology loss exists. Regardless of the reason for the difference, the algorithm uses a cross-entropy loss function to assess the classification result of real load data. Because the pattern-matching-based anomaly detector is designed to identify the normal power usage of the off-grid power plant, only the real value of the load is redetected. If no error is observed, then the classification result is determined to be acceptable.
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## System Design
### Azure Cloud Integration
#### 3.3.2 Azure Stream Analytics  
Azure Stream Analytics [38] seamlessly connect Azure IoT Hub with Power BI and Azure Table Storage to enable rich data visualization and persistent data storage. For data visualization, the Azure IoT Hub is selected as data input and Power BI is adopted as the output to help to transform live data into actionable and insightful visual reports. A SQL-like query language, which is a subset of standard T-SQL, is used for real-time streaming computations and filtering. In this solution, all the data collected from Azure IoT Hub is streamed to the output of Power BI, and the query statement is configured as \"SELECT * INTO [PowerBI] FROM [AzureSphereData]\", in which \"AzureSphereData\" stands for the input alias, while \"PowerBI\" represents the data sink. For data storage, the Azure IoT Hub is configured as data input, while Azure Table Storage is selected as data sink.
## 3 Results
### Fault Detection  
According to the conducted studies and the results presented in the second and third sections, which specifically focus on the performance support system and drive test data, we offer a mechanism to combine this information to complete the architecture of automatic detection of recorded fault.  
All three of our data sets were unavailable at all times, and, due to drive testing limitations and subscriber complaints, the methods used to combine information differed, according to the types of available data for each region.  
There are different ways to combine information from these three categories of data. The simplest of these methods is the use of majority opinion voting. If all three data sources are available, this method identifies a record as a fault when at least two of the three information sources confirm the occurrence of a fault in a specific cell and at a particular time. Another method is to use weighted voting. The basis of this method is the unequal accuracy of different data sources in fault detection. Therefore, assigning more weight to methods with higher precision is necessary. In this way, decisions are made about new data based on each method's ability to correctly identify faults in the network and consider the system's threshold.  
As we mentioned earlier, all data sources may not exist simultaneously in this framework. In addition, according to conversations with field experts, who emphasize the necessity of checking all the reported faults from the performance support system and drive test sources, all the reported faults from these two sources should be checked. If both of these sources report the absence of a problem for a region and at a particular time, and there is a subscriber complaint for this region, this complaint is ignored. Otherwise, the common complaint is used to improve the accuracy of diagnosis of the fault's cause (Table 9).
## 4 Results and Discussion
### Solar Radiation  
#### 4.2.1 The Effect of Different Coatings on Radiant Heat Errors  
After simulation analysis, as shown in Figure 7, when the surface emissivity of the radiation-proof material is smaller, the less it is affected by solar radiation, as demonstrated in Equation (11). Additionally, the thermal conductivity of the coating material also influences the heat exchange rate of the sensor. The radiation errors of the sensor using silver, aluminum, and copper as radiation-proof coatings are 0.14 K, 0.16 K, and 0.18 K at 1 s, respectively, while the error of the uncoated sensor is 0.22 K. Therefore, silver was used as the radiation-proof material for this sensor.  
Figure 6: Effect of different air pressures on temperature changes in Pt thin film.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 2 Imaging System Based on Tandem SAR Satellites
### Working Mode of the First Satellite  
The first satellite, namely, Sat-1, operates in the traditional scanning mode [18], which adopts the burst mode, and assigns the aperture time to multiple subswaths. It can broaden the range swath at the cost of coarsening the azimuth resolution. The observation geometry of Sat-1 is shown in Figure 1. To achieve an imaging system with azimuth resolution \(\delta_{az}\), the necessary burst time \(T_{n}\) of the \(n\)th subswath should satisfy the following:  
\[T_{n}=\frac{0.886\cdot\lambda R_{0,n}}{2V\delta_{az}} \tag{1}\]  
where \(\lambda\) is the wavelength, \(R_{0,n}\) is the mean slant range in the zero Doppler plane of the \(n\)th subswath, and \(V\) is the speed of the SAR platform.  
According to the principle of traditional ScanSAR, we have  
\[B_{D}=(N+1)\cdot B_{n} \tag{2}\]  
where \(B_{n}\) and \(B_{D}\) denote the Doppler bandwidth of the \(n\)th subswath and the 3 dB bandwidth of the azimuth antenna, respectively. According to Equation (2), the coarsening of the azimuth resolution is in the order of \(N+1\), while the range swath compared to a strip-map system is increased by \(N\).  
Figure 1: Pulse assignment diagram of the traditional scanning method, where the antenna beam jumps periodically among different subswaths. \(N\) indicates the number of subswaths.
## 3 GNSS Data and Results
### Cephalonia Island  
Regarding the island of Cephalonia, most GNSS stations were established after the 2014 intense seismic activity. For the period prior to 2014, data were available only from the VLSM station. The time series of this station (Table 3) showed an ENE linear type of motion with small seasonal fluctuations, mainly in the north component, and a subsiding pattern (Figure 9).  
The two strong events of Mw6.1 in late January and Mw5.9 in early February 2014, recorded on the VLSM and KIPO stations, caused measurable static displacement (Appendix B). KIPO station, located on the western part of the Paliki peninsula, closer to the earthquakes' epicenters, revealed the most significant displacement. VLSM station, to the southeast of the epicentral area, situated on the limestone unit of central Aenos Mountain, exhibited smaller co-seismic motion. The co-seismic displacement vectors in these two GNSS sites indicated a right-lateral activated faulting zone [50]. Nevertheless, these events have not caused any displacement to GNSS stations located at the neighboring islands or in western Greece (Appendix B).  
The GNSS velocity vectors for the sites located at Cephalonia Island after the seismic activity of 2014 exhibited a gradual rotation on the horizontal component from ENE in the north to ESE motion to the southern part of the island (SKAL station) (Figure 8). Most stations showed linear type of motion since 2014, with a subsiding vertical component (Table 3). The motional behavior of the VLSM station is, however, also worth noting. The time series  
Figure 9: Time Series of VLSM Cephalonia GNSS station. Red lines mark the dates of the strong earthquakes in the Ionian Islands.  
of VLSM (Figure 9) show irregular motion, in both the north and vertical components, for the period from February 2014 to September 2016. During that period, the station shows a non-linear ESE horizontal motion and uplifting behavior. The station acquired the pre-seismic ENE and subsiding motional character in the subsequent period (from September 2016 to present). This long-lasting irregular behavior--which may be described as a post-seismic relaxation period and also encompassed the 2015 Lefkada earthquake--has not been observed at any other site in the Ionian Islands. Calculating for the post-seismic period (from 2017 to 2020) the baseline changes between the stations in Cephalonia, extension occurred between the northernmost (FISK) and the southernmost (SKAL) stations (V = 4.66 \(\pm\) 0.10 mm/year). Meanwhile, in the central part of the island the distance between the ARGO and VLSM stations remained almost stable (V = \(-\)0.44 \(\pm\) 0.04 mm/year), for a similar period (from September 2016 to October 2021).
## 3 System Architecture and Implementation  
### System Architecture  
The proposed system design and implementation comprises several subsystems, including a backend cloud system, an Edge service, IoT nodes, and specific IOTA subsystems. Our proposed architecture shifts from a cloud-centric IoT system, where the Super nodes simply aggregate and push data to the cloud, to a node-centric system, where each Super node owns the data pushed in a distributed and decentralized database (i.e., the Tangle). The backend serves as a consumer of data and a provider of additional resources, such as administration panel, analytics, data marketplace, etc. Specifically, a narrow down approach was followed commencing from a three-tier reference architecture proposed by [PERSON] et al. [38], as shown in Figure 8, which was modified accordingly to meet our needs.  
Figure 8: The three-tier Reference Architecture.  
Figure 9 illustrates the activities diagram of a typical IoT application that was used as a template for the implementation of our prototype [38]. The diagram was further simplified in order to keep the complexity of the prototype low. The semantic notation submodule was removed as it was deemed unnecessary. The MAM submodule is an addition to the generalized IoT architecture, providing a service which is responsible for receiving the data and based on the metadata, creates the appropriate MAM messages. This submodule is also responsible for connecting to the IOTA Full Node. The final architecture that was adopted is shown in Figures 10 and 11.  
Process wise, data originated from the sensors are decoded in the Edge through specific drivers. Afterwards, they are enriched with time stamp and node denotation metadata and saved. Local storage not only cancels latency but in conjunction with a rule engine, it could enable intelligence. Then the data are transmitted to the Cloud where they traverse through the same subsystems as shown in Figure 10.  
Figure 10: The Edge Super Node Module.  
Figure 9: Subsystems Activities Overview.  
The cloud module also offers a data marketplace based on IOTA as well as a dashboard for the IoT system owner as Figure 11 depicts. Essentially, these are activities that are built on top of the Open API. Various sub-modules were discarded as they increased the complexity of the system without contributing to the implementation's purpose.
## 2 Background
### Bridge Structure & SHM Framework  
The structure under study is a highway viaduct (A32 Torino-Bardonecchia-S.S.335) built with eighteen sections, each one supported by two pairs of concrete pillars situated at their two ends. We focus on a single section instrumented for data analysis before a scheduled maintenance intervention in this work. Maintenance was necessary for the strengthening of the viaduct structure.  
The acquisition framework is described in Figure 1. The system contains five identical sensor nodes. Each one features an STM32F4 microcontroller (MCU) and samples three-axis accelerations and temperature data, and stores them in the cloud through a 4G-connected Raspberry Pi3 gateway.  
The five nodes are connected through CAN-BUS to the gateway. Note that the acquisition system only collects the data without any local edge-side signal processing. In this basic configuration, data analysis is executed on the cloud. The accelerometer samples the data with a frequency of 25.6 kHz to avoid aliasing. Subsequently, the data is subsampled to have a final output frequency (\(f_{s}\)) of 100 Hz.  
The cloud part is composed of a data-ingesion job, which receives and store data from the gateway, and periodically scheduled analysis tasks to monitor the health status of the bridge [2].  
The MCU is an ARM 32-bit Cortex-M4 running at 168 MHz, with 192 kB of SRAM and 1 MB of Flash memory, popular in different edge applications for its low power consumption. Further, the MCU features a floating-point unit and a digital signal processing (DSP) library. The gateway is a standard Raspberry Pi 3 module B [21] (RPi3). It includes a Broadcom BCM2837 SoC, with 64 bit 4-core Cortex-A53 running at 1.2 GHz and 1 GB of DDR2 RAM. The gateway runs an Ubuntu operating system, easing the scheduling of communication tasks through common python interfaces (e.g., an MQTT broker [22]). The cloud system is divided into a storage section and a computing node allocated on the IBM cloud service.  
In this work, we propose to replace cloud processing with a brain-inspired near-sensor anomaly detection algorithm directly computed on the microcontroller (MCU) on the sensor node board. We will show that the proposed algorithm can identify the normal and faulty bridge conditions, avoiding the RAW data transmission to the cloud.
## 3 GPS SDR Design for CRPA Using General-Purpose Parallel Processors
### GPU-Based Parallel Correlator
If we can reduce the number of assigned registers per thread, the occupancy can be increased, but it is generally difficult to reduce the number of register variables in source code while performing the same task. The maximum number of registers per thread can be manually forced using '-maxrregcount' flag at compile time. Although this flag may force to decrease register usage and increase the occupancy, it does not always improve performance for two reasons. First, spilled registers are stored in much slower local memory. Second, higher occupancy above a certain point does not improve performance (Chapter 4 of [43]). We force 24 registers per thread using this flag and it results in the best performance in our case. According to Section 3.2.6 of [43], as many as 24 warps (768 threads) per multiprocessor (_i.e._, 50% occupancy) are required to completely hide the latency of registers' read-after-write dependencies. If occupancy is more than 50%, it may not be necessary to further optimize parameters to obtain even higher occupancy. Note that the GTX 480 GPU has 480 CUDA cores (Table 1), which is theoretically capable of performing 480 16-bit floating-point operations (multiply, add, or multiply-and-add) per clock cycle. With the 83.33% occupancy, one multiprocessor has 1,536 \(\times\) 83.33% = 1,280 active threads, and the GTX 480 GPU with 15 multiprocessors (Table 1) has 1,280 \(\times\) 15 = 19,200 active threads. Thus, it is clear that CUDA cores cannot perform operations on all active threads simultaneously at the same clock cycle. However, it is important to generate large number of active threads to hide memory latency. In this way, CUDA cores would continuously have enough threads to process while other threads wait for memory reading. (Hiding memory latency in GPU will be further discussed in Section 3.4.)
# Optimization of Optical Networks Based on CDC-ROADM Technology
## 4 Conclusions  
An optimization of a DWDM network that takes into account details of an optical node has been performed. The study focuses particularly on the comparison of a DWDM network using a C-ROADM with the one applying CDC-ROADM technology while the main objective of the optimization is to minimize optical node resources. A model based on integer programming is proposed, which includes a detailed description of an optical network node architecture. The model is used to study the DWDM network congestion for selected realistic networks with different topologies and traffic demand sets. The results obtained show that CDC-ROADM-based DWDM network is able to carry 2 to 3 times more traffic than a network based on C-ROADM nodes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **IP Network Cost [Unit Cost]** & **LP Best Bound** & **Computation Time [s]** & **GAP [\%]** \\ \hline
**path1** & 700 & 697.5 & 72,000 & 0.36 \\
**path2** & 702 & 696.7 & 72,000 & 0.47 \\
**path3** & 702 & 696.6 & 72,000 & 0.77 \\
**path4** & 700 & 696.3 & 72,000 & 0.52 \\
**path5** & 708 & 696.4 & 72,000 & 1.08 \\
**path6** & 704 & 695.6 & 72,000 & 1.19 \\
**node-link** & 706 & 689.5 & 72,000 & 2.34 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison for Polish network using CDC-ROADM node model of link-path approach (path#) and node-link approach (node-link), while \(d=70 Gbps,s=32\), and \(p=1\).  
Figure 8: Dependence of network cost on calculation time obtained with IP (best integer) and LP (lower bounds) for: the entire computational time, 20 h (**a**) and for the initial 4 h (**b**).  
A comparison of two approaches, node-link method and link-path method is also presented in the context of the convergence of the algorithm. This comparison shows that link-path approach gives results that are nearer to optimum within a given simulation time. For further improvement of algorithm efficiency both methods should be enhanced by heuristic algorithms.  
Finally, it is noted that the results obtained are helpful to telecommunications network operators providing additional guidance while planning a DWDM network expansion.  
Conceptualization, S.K.; software, S.K.; investigation, S.K., M.Z. and S.S.; writing-review and editing, S.K., M.Z. and S.S.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Numerical Study of SC Walls Perforation
### Results Discussion of SC Walls Perforation  
Seven kinds of thickness combinations of concrete panel and steel plate were used for the SC wall perforation analysis. Figure 10 shows the numerical results for an SC wall with a 250 mm thick concrete panel and an 11 mm thick steel plate. The projectile striking velocity was 600 m/s and the SC wall was perforated as expected. The destructive area of concrete rear surface was larger than its front impact surface due to conical plugging occurring in the back area. In Figure 10b, the von-Mises stress distribution contour exhibits a circular character.  
To examine the penetration responses of SC walls under various striking velocities, a 200-mm thick concrete panel supported by rear steel plates with different thickness was numerically studied. Backing steel plates with different thickness were assumed to have various effects on the penetration resistance. Figure 11a illustrates that projectile residual velocity decreased with increasing thickness of the rear steel plate when the striking velocity was close to the ballistic limit [4]. Figure 11b shows the residual velocities for 150, 200 and 250 mm thick concrete panels subjected to a 600 m/s striking velocity impact. With the residual velocity increasing, the curve of velocity over time shows a slightly oscillating character. Under 600 m/s striking velocity impact, the projectile velocity history during SC wall perforation with a 200-mm thick concrete panel is shown in Figure 11c. The early penetration responses were almost the same, due to the fact that the backing steel plate had no influence on impact resistance of the front concrete target. Concerning the later perforation process, results imply that the rear steel plate has a significant effect on penetration resistance. Figure 11d shows the striking velocities and the residual velocities after perforation of SC walls with 200 mm thick concrete panels. With increasing striking velocity, it seems that the rear steel plate had a less pronounced effect on penetration resistance and thus the residual velocities tend to converge.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part** & **Projectile** & **Concrete** & **Rear Steel Plate** \\ \hline Number of elements & 696 & 376,875 & 50,000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Element numbers for each part.  
Figure 9: Overview of the meshed FE model.  
Figure 11: _Cont_.  
Figure 10: Impact response of an SC wall. (**a**) von-Mises stress distribution in front concrete panel; (**b**) von-Mises stress distribution in rear steel plate.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones  
Shuo Wei  
College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS] (S.W.J.); [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (X.D.)  
Houming Fan  
College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS] (S.W.J.); [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (X.D.)  
Xiaoxue Ren  
College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS] (S.W.J.); [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (X.D.)  
Xiaoxue. Ren  
College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS] (S.W.J.); [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (X.D.)  
Xiaoxue. Ren  
College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (X.D.)  
Xiaoxue. Ren  
College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China; m15614121882@163.  
vehicle was launched by Mercedes-Benz in 2016 and is equipped with fully automated shelving and drones [3]. Due to its potential value in practice, this model has attracted significant attention from scholars. [PERSON] et al. [4] studied a truck-drone solution for rural last-mile delivery with steep roads. [PERSON] et al. [5] studied the vehicle and multi-drone collaborative delivery problem in the context of food distribution. Currently, road traffic congestion is becoming increasingly severe, particularly during peak hours in the morning and evening, leading to slow vehicle speeds. In order to develop more optimal and practical delivery solutions, businesses must account for the time-dependent speeds of delivery vehicles in real-world conditions. Additionally, to ensure safety and efficiency, delivery areas often set vehicle-restricted zones and drone no-fly zones during specific time periods. When a vehicle-restricted zone is in effect, vehicles must detour, and drones are used to service customers within that zone. Similarly, when a no-fly zone is in effect, drones must detour, and vehicles are used to service customers within that zone. Based on this, this paper studies the time-dependent vehicle-drone routing problem under vehicle-restricted zones and no-fly zones from a real-world scenario perspective.  
The following sections will proceed as follows. A literature review is presented in Section 2. The problem description is presented in Section 3. The TDVRPD-VRZ-NFZ mathematical model is introduced in Section 4. The HGAVNS-LM algorithm is presented in Section 5. Section 6 presents numerical experiments. The conclusion and avenues for future research are presented in Section 7.