esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Verification of Our Simulation Results  
To verify our simulation results, we compare the differences in the average travel times and average departure times when going to work and getting off work between simulation and reality. Tables 4 and 5 show these differences between simulation and reality. The real data on the times at which people board at and depart from metro stops were obtained from the Shenzhen Metro Group. Moreover, we have calculated the average walking times from home to the origin metro stop and from the destination metro stop to the workplace. These times are helpful for calculating the average travel times without including walking time and the average times of boarding at and departing from metro stops in the simulation. Table 6 presents these results. The abbreviations \"HToS\" and \"SToW\" stand for \"home to metro stop\" and \"metro stop to workplace\".  
The experiments reported in Sections 4.1 and 4.3 demonstrate the core heuristics of the replanning strategy, namely that the average departure times of person agents when going to work should be as late as possible with no risk of being late and the average departure times of person agents when getting off work should be as early as possible with no risk of missing metro trips. With this strategy, the average travel time also becomes shorter.  
However, there are some implicit flaws in our optimization strategy; some person agents who are represented as going to work are still caught in congestion when the iteration has ended. In addition, this paper does not consider bus and car trips because of a lack of relevant data. Furthermore, the computational efficiency of the simulation process is relatively low and should be improved by means of parallel computing.
## 3 Results and Discussion
### Quality Attributes
In the present study, the MDA value of CK1 increased gradually from 0.86 nmol/mgprot at day 0 to 3.89 nmol/mgprot at day 16 (Figure 1f). However, the samples treated with edible coating combined with MAP had lower values at the corresponding time, especially the gas composition of 65% CO\({}_{2}\)/35% N\({}_{2}\) or 70% CO\({}_{2}\)/30% N\({}_{2}\). Then the MDA value of all samples had a decrease at the end of storage as a consequence of the interaction between malonaldehyde and protein degradation products [21]. In general, the combined effect of edible coating and MAP (65% CO\({}_{2}\)/35% N\({}_{2}\) or 70% CO\({}_{2}\)/30% N\({}_{2}\)) has positive effects on maintaining quality characteristics of HTS fillets. The activity of Ca\({}^{2+}\)-ATPase is broadly used to assess the actomyosin integrity, reflecting the degradation of myofibrillar protein [14]. In the current study, a decreasing trend was detected no matter what kind of treatment was carried out during refrigerated storage (Figure 1g), which was concordance with the results with Pavap [22]. In comparison with the initial value, the activity of Ca\({}^{2+}\)-ATPase of CK1 group was diminished by 68.97% at the end of storage, while other samples showed more excellent influence on reduction of the activity of Ca\({}^{2+}\)-ATPase, especially the C3 group. This results may be related with conformation and aggregation of myosin globular head [23].
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
## 1 Introduction  
In hyperspectral imaging (HSI), spectral signatures of objects are recorded for each image pixel. The spectral signature of an object is the reflectance variation or function with respect to the wavelength. It is important for characterizing materials and their properties. In HSI, different types of noise appear due to environmental or instrumental influences. The noises include Gaussian noise [1], impulse noise [2], dead pixels or lines [3], and stripes [4]. This has been recently discussed, for instance, in [5] and in the overview articles [6, 7]. HSI combines spatial and spectral information in a hyperspectral data cube, as displayed in Figure 1. Naturally, the amount of generated data is huge, and an efficient and reliable approach to noise reduction takes advantage of the internal dependencies between the wavebands. HSI precision and reliability are essential for many applications, including digitalization and robotization in Earth and space exploration. The applications include agricultural monitoring [8], atmospheric science [9], geology [10], and space exploration [11]. Efficient and reliable noise reduction techniques are essential for image processing in practice regarding real-time decision-making and automation. In [12], various noise reduction techniques have been compared for hyperspectral image data in asteroid imaging, including also low-rank matrix recovery (LRMR).  
LRMR is a low-rank modeling approach [13] and has been discussed among other advanced image processing methods in more detail in [6, 7]. We use here LRMR together with the Go Decomposition (GoDec) numerical optimization algorithm as presented in [14] in the inner iteration steps of the approach for solving the restoration model. Parameter optimization in advanced image processing can provide important indirect information for control and real-time decision-making. The main idea here is to optimize the choice of the algorithm's initial values in order to improve the performance of the noise reduction. We analyze the parameter choices, including applying nonlinear optimization methods as derived in [15]. The open, available hyperspectral data sets Indian Pines [16], Pavia Centre [17], and Pavia University [17] are used for the computational tests.  
This work is part of the coADOVA--ADDing VAlue by Computing in Manufacturing project funded by the Regional Council of Central Finland/Council of Tampere Region and European Regional Development Fund. It supports the project's goals to improve the efficiency of robotics by developing optimal control methods leading to flexible imaging and automation in image processing.  
The article is organized as follows: In Section 2, the methods are discussed, including LRMR and the optimization applied to its initial parameter settings. The used data sets are presented. Section 3 presents the computational results. Section 4 discusses the results according to the existing literature and possible future work. Finally, Section 5 presents the main contributions of this work.
## 4 Simulation Comparison with Existing Schemes
### ZVS Range  
Figure 14 illustrates the ZVS operating ranges of five modulation strategies: SPS, CSO-DPS, OPCB-EPS, CSO-TPS, and the proposed EIOS-TPS. The green region denotes the operating conditions under which all switches achieve ZVS, whereas the other colored regions indicate scenarios where ZVS is not attainable for certain switches. As shown in Figure 14, all four strategies exhibit a large ZVS failure region at low transmitted power levels; therefore, the analysis focuses on the low-power range. In boost mode,  
Figure 13: Curves of normalized current-stress variation with normalized power \(p\) for different voltage conversion ratios. (**a**) \(k\) = 1.5. (**b**) \(k\) = 0.667.  
\begin{table}
\begin{tabular}{c c} \hline
**Circuit Parameters** & **Specifications** \\ \hline \multirow{4}{*}{Power} & Input Voltage \(V_{1}\) = 80 V \\  & Output Voltage \(V_{2}\) = 53.33 V 120 V \\  & Maximum Power \(P_{m}\) = 1176 W \\  & Switching Frequency \(y_{s}\) = 40 kHz \\  & Transformer Turns Ratio \(n\) = 1 \\ \cline{2-3}  & Induction \(L\) = 25.5 \(\mu\)H \\ \hline \multirow{2}{*}{Switch} & \(S_{1}\)-\(S_{4}\), \(S_{5}\)-\(S_{6}\)-; C\({}_{2}\)C\({}_{3}\)C\({}_{2}\)C\({}_{3}\) \\  & \(V_{\text{DSS}}\) = 1200 V \(L_{0}\) = 32 A \(R_{\text{diston}}\) = 40 m\(\Omega\), C\({}_{\text{cons}}\) = 58 pF \\ \hline \multicolumn{2}{c}{SiC MOSFET Drivers} & 1 EDI60N12 AF \\ \hline Microcontroller & TMS320F28335 \\ \hline \multirow{2}{*}{Filter Capacitor (\(C_{1}\), \(C_{2}\))} & Input Capacitor \(C_{1}\) = 450 \(\mu\)F \\  & Output Capacitor \(C_{2}\) = 450 \(\mu\)F \\ \hline \end{tabular}
\end{table}
Table 5: The parametric details of the experimental DAB converter setup.  
switches \(S_{1}\)-\(S_{4}\) cannot achieve ZVS. In buck mode, switches \(Q_{1}\)-\(Q_{4}\) fail to achieve ZVS. This limitation is inherent to the SPS strategy, where all switches can achieve ZVS only when \(k=1\) (i.e., primary and secondary voltages are matched). For CSO-DPS and OPCB-EPS, switches \(Q_{1}\)-\(Q_{2}\) cannot achieve ZVS in boost mode, and switches \(S_{3}\)-\(S_{4}\) cannot achieve ZVS in buck mode. Notably, the ZVS range of CSO-DPS is narrower than that of OPCB-EPS. For CSO-TPS, six switches fail to achieve ZVS in both buck and boost modes, although they operate under ZCS conditions. In contrast, the proposed EIOS-TPS enables ZVS for all switches across the full power range under both buck and boost modes.  
The switching losses of the DAB converter significantly impact overall efficiency, particularly in the low-power operating region. Achieving ZVS for all switches at low power levels significantly enhances converter efficiency. The proposed modulation strategy enables ZVS for all switches across different voltage conversion ratios and the full power range, effectively reducing the DAB converter's switching losses.
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 4 Assisting Drivers around Stop Sign Intersection
### Driving Assistance Protocol at Stop Sign Intersections  
This protocol provides real-time assistance to drivers to allow them to safely and efficiently traverse the stop-sign-controlled road intersections. First, it notifies the drivers early regarding the approaching intersections controlled by the stop signs. Then, drivers can start reducing their speed to stop at the targeted intersection smoothly. The time when the stopping vehicle can move through the signalized intersection is determined based on the arrival time of completed vehicles and the priorities of the driving rules. Finally, drivers must pass through the intersection at a suitable speed. These successive steps are presented in detail in the rest of this section.
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## System Design
### Configure Azure IoT Hub for Azure Sphere  
To use Azure Sphere MT3620 devices with Azure IoT Hub, there are a few tasks that need to be completed. As a prerequisite, an Azure IoT Hub shall be created with a suitable Resource Group and Region on Azure Portal [39]. For a lower latency, the closest location shall be selected. After that, a new instance for the IoT Hub Device Provisioning Service [40] is created and linked with the existing IoT Hub.  
The Azure Sphere device provision process is shown in Figure 2. Firstly, Azure Sphere Developer Command Prompt is used to download the Certificate Authority (CA) for the Azure Sphere tenant. Secondly, the tenant CA certificate shall be uploaded to Azure Device Provisioning Service and then a verification code will be generated to validate the certificate ownership. Thirdly, the validation certificate which proves the ownership shall be downloaded by Azure Sphere Developer Command Prompt with the verification code from the step above. Lastly, the certification process is completed as soon as the signed validation certificate is uploaded to the Azure Device Provisioning Service on Azure Portal. Afterwards, the custom Enrollment Group is created with the certificate that was validated in the previous step to add the Azure Sphere device.  
By the above configuration, not only the use of compromised and untrusted devices is prevented, but also the registration of the authorized devices in the IoT Hub can be ensured.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 4 Case Study
### Step 3: Analyze Threats to Base Energy Security
#### 4.3.3 Step 3.3: Develop Microgrid and Nanogrid Threat Scenarios  
Tables 6 and 7 matrices illustrate the scenarios that are connected to each threat in order to determine nanogrid resilience compared to current microgrid resilience. Each \"X\" in the tables is an indication of a specific piece of equipment being unavailable, offline, or destroyed within the specific threat scenario.  
Figure 6: Operational Concept View of Naval Support Activity Monterey. Baseline microgrid is represented in the top diagram while the proposed microgrid augmented with nanogrids is represented in the bottom diagram.  
\begin{table}
\begin{tabular}{c c} \hline
**Threats** & **Probability of Occurrence/Year** \\ \hline Fire & 0.05 \\ Explosion & 0.04 \\ Insider Threat & 0.10 \\ Fuel Delivery Disruption & 0.02 \\ Loss of Equipment & 0.10 \\ Cyber-Attack & 0.10 \\ Severe Storm & 0.02 \\ Plane Crash & 0.02 \\ \hline \end{tabular}
\end{table}
Table 5: Probability of Occurrence for Postulated Threats. The threats and probabilities are derived from a number of sources including natural disasters (e.g., fire, flood, fire, seismic, high winds, tornado, hurricane, etc.) and human-caused events [52, 53]. It is important to note the limitations on historical data due to climate change. Increasing climate changed has caused considerable changes in weather patterns and poses risk to military installations.  
Figure 7 illustrates the operational concept view for a threat of a fire. This illustrates that a fire can effect more than just the loads compromised and is dependent on the situation. The fire threat scenario assumes that the spread of the fire was able to be stopped on the north-east side of base and did not impact any of the power lines to the rest of the base.
## 2 Materials and Methods
### Isolation of RNA, RNA Electrophoresis, qRT-PCR  
Samples of total RNA were obtained by phenol-chloroform extraction as described previously [25]. Following isolation, RNA extracts were treated with Turbo DNase (Life Technologies, Carlsbad, CA, USA) to eliminate any genomic DNA remnants. The purified RNA was then visualized using a 1% agarose gel in TAE buffer. For cDNA synthesis, 100 ng of the extracted RNA was utilized along with random hexanucleotides and SuperScript III reverse transcriptase (ThermoFisher Scientific, Waltham, MA, USA). To conduct qPCR, the qPCRmix-HS SYBR (Evrogen, Moscow, Russia) was employed, along with specific primers including max_RT_F, max_RT_R, vapC_RT_F, vapC_RT_R, 16 SMSM_RTUp, and 16 SMSM_RTLow (refer to Table 1 for primer sequences). The Applied Biosystems 7300 Real-Time PCR system (ThermoFisher Scientific, USA) was used for qPCR, with the following cycling conditions: 95 \({}^{\circ}\)C for 10 s, 60 \({}^{\circ}\)C for 10 s, and 72 \({}^{\circ}\)C for 30 s, repeated for 40 cycles. LinRegPCR v. 11.0 (the Academic Medical Center, Amsterdam, The Netherlands) was employed to process the PCR data. The abundance of amplicons was calculated relative to the expression level of the 16S rRNA gene.
## 1 Introduction
Therefore, this study aims to determine the measured path loss of the two digital terrestrial UHF television channels in the coastal city of Ikorodu, Lagos and Akure, a city in Nigeria's tropical rainforest zone. Secondly, the path loss predicted by the four selected models will be determined using their respective models. The third objective is to carry out a margin-of-error analysis on the results of the predicted models, comparing them to the measured data, to determine which of the models is best suited for path-loss prediction in the study areas and frequencies. Lastly, the models will be optimized to further enhance their reliability in the study area and similar tropical urban or suburban areas and frequencies. All these objectives have been achieved in this study.
## 3 Data Collection and Observation Quality Assessment  
To validate the performance of the presented smartphone PPP model, two field tests are arranged around the playground of China University of Geosciences (Beijing, China). The first test was the static experiment, which was arranged at 9.00 AM-12:30 PM on 4 June 2025, with a total duration of approximately 3.5 hours. The main equipment used in this experiment was a XIAO MIS smartphone (Xiaomi, Beijing, China) (marked as MI8) and a geodesic receiver called the PANDA GNSS receiver (Pandagnss, Wuhan, China, www.pandagnss.com). The MIS smartphone was placed under a relatively unobstructed sky environment and closely adjacent to the PANDA receiver (as shown in the subfigures of Figures 1 and 2). Due to the positioning accuracy of a smartphone's PPP being at about the sub-meter to meter level, the distance between the MI8 and the GNSS receiver's antenna can be ignored in this work. The purpose of the static experiment is to analyze the convergence and positioning accuracy of a smartphone's PPP, and the purpose of this dynamic test is to show the real-time positioning accuracy of the smartphone PPP model in a real application. To evaluate the positioning accuracy, the PPP solutions based on the PANDA receiver and IGS ultra-rapid precise orbit/clock products (from GFZ) are used as reference positioning results.  
Figure 1: Equipment (MI8, PANDA GNSS receiver, GNSS antenna, and MI8) (**a**) and the sky-plots of GPS and BDS (**b**) in the static experiment.  
The second test was a dynamic experiment, arranged at 10:20 AM on 30 October 2023, and the corresponding experimental equipment and trajectory are shown in Figures 2 and 3. In addition to the MI8 and the two GNSS PANDA receivers, a high-precision inertial navigation device named POS320 (NAVTIMES, Beijing, China) was also used in the dynamic experiment. The reference results of the dynamic experiment are calculated through the tightly coupled integration of RTK/INS using the GINS commercial software (GINS version 3.1.0, [[http://www.whmpst.com/en/imgproduct.php?aid=29](http://www.whmpst.com/en/imgproduct.php?aid=29)]([http://www.whmpst.com/en/imgproduct.php?aid=29](http://www.whmpst.com/en/imgproduct.php?aid=29)), accessed on 21 April 2025).
## 2 The Proposed Fusion Approach
### Laplacian Pyramid (LP)  
The Laplacian pyramid [18; 19; 20] reveals the strong contrast modifications to which the human visual system is most highly sensitive. It can localize in both the spatial and frequency domains. LP is used to extract the most relevant elements of the fused image. LP also sets a premium on elements that have the most effective and are resistant to noise. As a result, the LP minimizes blurring and spatial distortions. The technique for constructing and reconstructing a Laplacian pyramid is shown below. On vector data, the image reduction method is performed by taking the DCT and applying the inverse of the DCT (IDCT) to the first half of the coefficients. The function that reduces IR is used to conduct level-to-level image reduction.  
Reduction Function (Image):  
Image Reduction (IR) using DCT:  
\[n=length(Image) \tag{1}\]  
\[Y=DCT(Image,n) \tag{2}\]  
\[Image.LF=IDCT(Y(1:n/2)) \tag{3}\]  
\[Image.HF=Image-IDCT(Y(1:n/2),n) \tag{4}\]  
Expand Image (IE) using DCT:  
\[n=length(Image.HF) \tag{5}\]  
\[Image=DCT(Image.LF) \tag{6}\]  
\[Image=IDCT(Image,n)+Image.HF \tag{7}\]  
Pyramid Construction:  
\[X=IR(X) \tag{8}\]  
\[lk=x-IE(X) \tag{9}\]  
Each image to be fused is formed into a pyramid using Equations (8) and (9). The constructed stages of the Laplacian pyramid are denoted by \(\Pi\) in the first image and \(\Pi\) in the second image. The following is the image fusion rule:  
\[for\;i=1:J\]  
\[IMAGE1\{i\}=reduce(\Pi) \tag{10}\]  
\[IMAGE2\{i\}=reduce(\Pi) \tag{11}\]  
\[image1=IMAGE1\{i\}.L \tag{12}\]  
\[image2=IMAGE2\{i\}.L \tag{13}\]  
_end_  
_At \(I^{th}\)level,\(IMAGEf.L=0.5*(IMAGE1\{f\}.L+IMAGE2\{f\}.L)\)_  
_for \(J-1\) to \(1\) levels_  
\[D=(abs(IMAGE1\{i\}.H)-abs(IMAGE2\{i\}.H))>=0 \tag{15}\]  
\[IMAGEf.H=D.*IMAGE\{i\}.H+(\sim D).*IMAGE2\{i\}.H \tag{16}\]  
\[IMAGEf.L=expand(IMAGEf) \tag{17}\]  
_end_  
\[FusedImage=convert\_1D\_to\_2D(IMAGEf.L,m,n) \tag{18}\]
## 3 System Overview
### Function Modules
#### 3.2.3 Investigation Module  
The investigation module is the third VR module in the system. The module aims to educate the user about close contact and environment cleaning knowledge. Figure 11 displays the exploring flow of the investigation module. The user moves to the close contact room after the ambulance departure animation. The contact is in the bedroom. The first step is communicating with contact and measuring his temperature. After the investigation, the contact leaves the room and moves to the health facility. The user continues the investigation in the room. The system indicates the user collects the sample in the bedroom and bathroom. After the collection, the user starts the environment cleaning. The user can use the disinfection spray to clean the room under the instruction. When the investigation completed, the animation drives the user to the case submission module.  
The module has three major interactive sections, which are contact investigation, sample collection and environment cleaning. The research refers to VTS and designs simple and attractive interactive methods to improve the user experience in the sections. The module reduces many unnecessary procedure contents and reserves the core and fundamental knowledge to the user.  
Figure 11(a) the communication with close contact. The user can select different questions to ask the contact. The game branch dialogue can enhance the participant for the user. Measure temperature is a small interactive game. The user needs to use the thermometer to aim at the contact's forehead (Figure 3(b)). The thermometer cannot display the correct temperature if the user cannot aim at the correct target. The section educates the user on how to use the thermometer correctly.  
After the contact leaves the room, the user starts collecting the environment sample in the bathroom and bedroom (Figure 11(b)). The system highlights the vital furniture. When the user closes the furniture and uses the tool to collect the sample, the system displays the correct collection notification. The cleaning section is the next step. The system notifies the user to gear the disinfection spray and clean the environment. The research designs the unclean part as the black sphere in Figure 11(c). The user should spray disinfections to the target for a while till the sphere disappear. The section uses the \"capture and destroys\" as the general game flow. It can attract the user to finish the cleaning target. The section assists the user in knowing how to use the disinfection spray and cleaning the environment.  
Figure 11: Investigation Module Exploring Flow.