esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Measurement Campaigns  
### Sensor Node Consumption  
First, the energy consumption of the different blocks of the node for two different sampling frequencies of the IMU data (400 samples per second (SPS) and 1600 SPS) have been characterized. Detailed energy requirements from the node components to operate during the active mode cycle are presented in Table 1.  
Figure 7: Scheme of the power management section of the wireless sensor node.  
For an active mode cycle, the node energy requirement is between 1.45 and 1.65 mJ, depending on the accelerometer's configuration.  
In sleep mode, the current drawn from CBUFFER by the RTC is less than 50 nA. Less than 100 nA is similarly drawn from CSTORE by the UVLO circuit and to the leakage current on the PMOS switches.  
The average efficiency of the LTC3106 has been estimated between 75 and 85%, depending on the input voltage, leading to an overall energy budget of 2.2 mJ. As the maximum energy available on the capacitors is 4.8 mJ, it is large enough to allow two full active cycles.  
To characterize the efficiency with which the storage element gets charged, we connected the input of the bq25570 to a 2450 source meter (Keithley Instruments, Solon, OH, USA). The voltage source was set to the halved open-circuit voltage (ranging from 0.3 to 0.5 V), while the compliance current was set between 100 and 400 \(\upmu\)A. For the different configurations, we measured the time required for CSTORE capacitance: (i) to start charging, that is exiting the cold-start state and CBUFFER reaching 3.6 V; (ii) to reach 2 V (first power-on); (iii) to reach 3.3 V (fully charged). These measurements are shown in Figure 8. The efficiency is computed as the ratio between the overall energy stored on CBUFFER and CSTORE and the power supplied at the input.  
As expected, the energy efficiency in charging a capacitor directly connected to the boost converter output (Figure 8a) is very low, between 11 and 22%. However, due to the choice of charging directly only a small CBUFFER and connecting the large storage element CSTORE to a second DC/DC converter, the efficiency in fully charging the storage element is improved to values ranging from 52 to 87%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Energy** \\ \hline MCU (400 SPS) & 380 \(\upmu\)J \\ MCU (1600 SPS) & 510 \(\upmu\)J \\ IMU (400 SPS) & 265 \(\upmu\)J \\ IMU (1600 SPS) & 275 \(\upmu\)J \\ LoRa Transceiver & 405 \(\upmu\)J \\ Real Time Clock & 13.2 \(\upmu\)J \\ Decoupling Caps (estimate) & 70 \(\upmu\)J \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of required energy for the main components of the node.
## 2 Related Research
### IoT Security Threats  
#### 2.2.1 Threats from the Device Perspective  
Security threats from devices include information leakage from a hacked device, data forgery/alteration, device access by unauthenticated users, and device loss or theft.  
First, unauthorized access to a person's device means that an unauthorized person can access it. When using a wireless network, if an ID, password, etc., are not in a router or in the IoT device itself, anyone will be able to access it. If an attacker with a malicious purpose accesses it, the damage is increased. An attacker could inject malicious code over a wireless network, turning a smart device into an infected device. Distributed denial of service (DDoS) attacks can be generated by gathering infected IoT devices in this way.  
Second, information leaks through hacked devices can allow communication with smart devices over wireless networks to use corporate IoT services. At this time, if a device becomes vulnerable, an attacker can access the smart device via a wireless network. For example, as mentioned above, the user may not have set up an ID and password required to use IoT device services. An attacker can access the device without any information. It may lead to secondary damage such as leaking information stored inside the device or requesting a ransom by taking the data  
Third, the security threat of data forgery and alteration has one prerequisite: the attacker must be able to access smart devices via a wireless network. After that, administrator authority to use the device can be obtained, and data can be forged or altered. These types of forgery and alteration of data may pose threats such as sending malicious data for communication and preventing a service from performing normally. Accidents causedby sensor malfunctions and erroneous information transmission caused by modulation of documents are possible. When using a smart sensor in a company, it is possible to detect the movement of people inside the building, change the temperature in the office, add humidity, smoke, carbon dioxide, etc., and analyze where electricity is used a lot. Modulating these values causes a variety of damages, such as cutting off power due to overload detection, paralyzing the business by activating the fire alarm due to a malfunction in a smoke sensor, and triggering the sprinkler system by raising the temperature in a sensor.  
Fourth, device loss or theft is a security threat that can occur when a user with a smartphone, etc., loses the device or if anyone steals the device. By losing a smart device, allowing other users to acquire it, allowing them to access it, or sending and receiving sensitive information that should not be accessed, confidentiality may be compromised. Information such as IDs and passwords recorded in a smart device could lead to network infringement that allows access to routers and IoT devices that can be connected to by obtaining access rights.
## 6 Conclusions  
An effective transmission strategy based on a fuzzy control system was proposed in this work, which was called a fuzzy control routing-forwarding algorithm. This algorithm comprehensively considered nodes' characteristics. Consequently, during the working time, the metrics were the Degree of Intimacy (DI) and Separating Time (ST). During the non-working time, the metrics were the Sensitivity of Interest (IS) and the Sensitivity of Age (AS). Unlike other routing algorithms based on a single scenario, this algorithm synthetically considered the scenarios of working time and non-working time. When selecting a suitable next hop node from the surrounding neighbors, this algorithm exploited fuzzy control decision-making methods to calculate the forwarding preferences of nodes. Supposing that the computing capacity and cache spaces of mobile devices in opportunistic social networks will be further enhanced, the MSFC algorithm can be applied to the transmission environment of 5G and big data networks. In future work, we will further improve the performance of the algorithm, enhance its portability, make it suitable for multiple scenarios, and further improve its transmission efficiency.  
[PERSON]. ([PERSON]), [PERSON], [PERSON], and [PERSON] conceived of the idea of the paper. [PERSON]. ([PERSON]), [PERSON] ([PERSON]), [PERSON], [PERSON], and [PERSON] designed and performed the experiments; [PERSON]. ([PERSON]) and [PERSON] ([PERSON]) analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON]. ([PERSON]) wrote and revised the paper. All authors read and agreed to the published version of the manuscript.  
This work is partially supported by National Natural Science Foundation of China (Grant Nos. 61462079, 61562086, 61562078, 61862060) and the Science and Technology Support Project of Ministry of National Science and Technology of China (Grant No. 2015 BAH02F01).  
The authors declare that they have no competing interest.  
Figure 12: Energy surplus with various cache spaces.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 2 Literature Review and Applications in Disaster Management
### Applications  
Applications of UAVs in disaster management vary according to their role during various phases of disaster management and can be broadly classified as shown in Figure 1.  
Figure 1: Classification of applications of UAVs-enabled IoT platform in disaster management.
## 5 Results and Discussion
### Comparison of Optimization Methods  
The practical method (PM) [8] is widely used in the design of bridges in China, aiming at reducing the bending energy of structures. Further, the practical method was used to optimize the cable pre-tension forces. Compared with the optimization results with the weight vector (2,8), the pre-tension forces distribution of the two methods was shown in Figure 14. It is noted that the distribution of the cable pre-tension force obtained by the practical method is not uniform, and the cable pre-tension force changes abruptly at the auxiliary pier. The B-spline curve is used to represent the pre-tension force distribution, which can ensure the uniform distribution of cable force.  
Figure 15 shows the vertical displacement of the deck under dead and live load. It is noted that the deck displacement of the proposed method under dead load is much smaller than that of the practical method. The maximum deflection of the deck under live load is 1.575 m and 1.609 m respectively. The traditional optimization method does not consider the effect of the pre-tension force of the crossing cable on its constraint stiffness, so the proposed method can obtain a higher structural stiffness.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Length from the** & & & \\
**Mid Tower (m)** & & & \\ \hline
367.5 & (0.58:0.42) & (0.58:0.42) & (0.63:0.37) \\
382.5 & (0.62:0.38) & (0.63:0.37) & (0.64:0.36) \\
397.5 & (0.67:0.33) & (0.68:0.32) & (0.68:0.32) \\
412.5 & (0.73:0.27) & (0.75:0.25) & (0.73:0.27) \\
427.5 & (0.81:0.19) & (0.82:0.18) & (0.81:0.19) \\ \hline \end{tabular} Note: Vertical component of crossing cable force of side tower: vertical component of crossing cable force of mid tower.  
\end{table}
Table 4: Allocation of dead load between crossing cables.  
Figure 14: Cable pre-tension forces.  
The bending moment of the deck under dead load is shown in Figure 16. It is noted that the two methods have larger bending moments at the side span, but the moment distribution of the practical method at the mid span is better than that of the proposed method. According to the optimization results, it can be seen that the cable pre-tension force optimization process should not only consider the internal force of the tower and the deck, but also consider the influence on the constraint stiffness of the crossing cable.
## 7 Conclusions  
Many lessons have been learned about the role of SWC in Indonesia so far, which can set an example at the global level. Changes in various SWC efforts were adjusted to various regulatory changes and the dynamics in the existing communities. The role of community participation, consistent implementation of SWC regulations, and the government's political will in SWC implementation are critical to support sustainable development goalsin Indonesia. The Indonesian government had many related programmes to achieve the country's sustainable development goals in terms of protecting and promoting the sustainable use of terrestrial ecosystems on soil, water, and vegetation. Harmonizing SWC with community cultures can increase the success of its implementation and application. The inclusion of multiple generations in the SWC implementations and campaigns might also benefit the programme even more. Furthermore, when both engineering (terracing, slope hydraulics project, and gully control techniques) and biological (afforestation, hedgerow, and enclosure) techniques are applied together, soil and water loss can be reduced more successfully.  
So far, many long-term benefits have been achieved through the SWC implementation in Indonesia, including biodiversity, hydrology, and microclimate benefits. SWC increased the adaptation of native plants to local rainfall and soil conditions, which impacted biodiversity. SWC increased infiltration and improved soil hydrological characteristics that had an impact on soil and hydrology sustainability. The selection of SWC techniques related to water harvesting techniques and water use efficiency can be adjusted according to local conditions. SWC through vegetation techniques also played a role in lowering temperatures, increasing humidity, and reducing intensity levels that had an impact on the good quality of the microclimate. The applicability of SWC in this review can be implemented in the region condition with a tropical wet, humid climate, and most of the areas are dominated by sloping areas with three major terrains: steeply dissected, hilly, and mountainous land.  
I.W.S.D., P., C.A.S., B.H.N., N.K.E.U., B.S.S., A.S., M.D.E.W., I.K.A. and N.S. had an equal role as main contributors who equally discussed the conceptual ideas and the outline, conducted the systematic literature reviews, prepared the initial draft, provided critical feedback on each section, and revised and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the anonymous reviewers for their valuable comments which helped improve this manuscript.  
The authors declare no conflict of interest.
# The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures
## 4 Discussion
### Limitations  
The sample sets used to train/test the canines and computational model both skewed toward the same demographic categories. In terms of race, the sample sets were predominantly white or Caucasian, and in terms of gender, they were predominantly female. Additionally, the overarching dataset of total collected T-shirt samples lacked contributions from non-binary, genderqueer, and non-cis-gendered individuals.  
While conducting a crowdsourcing campaign for sample donations allowed for a large number of samples to be collected from a geographically diverse set of donors, the procedure required some sacrifice of oversight into how the samples were handled pre-processing. The T-shirt samples used in this study were handled by multiple individuals from sample kit preparation to its final analysis, either by canine or HS-SPME-GC-MS analysis. At each point of interaction, the opening of containment vessels creates an opportunity for VOCs of interest to escape and interferents to settle onto the samples. In addition, the samples were stored in permeable membranes (Ziploc(tm) bags) from the time of collection until either (a) they were transferred to a glass mason jar and kept for storage by PVWDC or (b) they were transferred to FIU. Samples that were allocated for FIU remained in the permeable containment at room temperature until arriving at the facility. The storage of samples at room temperature or in permeable containers allows for the continuous emission of highly and nominally volatile compounds from the sample. The long-term storage of samples under these conditions creates opportunities for sample depletion and may have impacted the performance of both experiments by lowering the abundance of total analytes in the samples.  
It is important to note that the canine and HS-SPME-GC-MS experiments were not informed by the same sample sets. While all samples were collected by PVWDC and processed in the same manner, not all of the specific samples used to train or test detection dogs were used to inform the LDA model. Of the 291 samples used in canine training and 148 used to inform the computational models, 79 samples were used to inform both models. The increased number of samples used in training the canines may have beneficially impacted their ability to generalize the COVID-19 odor signature despite the presence of pet interferers.  
Samples processed at FIU were subjected to UVC light exposure at 254 nm for 10-min prior to analysis. [PERSON] et al. (2009) noted that prolonged exposure to UVA/UVB light resulted in a distortion of human scent profiles, with some VOCs reducing in abundance and other compounds appearing as UV light exposure increased [53]. However, the [PERSON] study looked at direct, continuous exposure to the light source for 0, 1, 3, 5, or 7 weeks [53]. This study utilized a shorter exposure time of 10 min which has been previously reported to not adversely impact the VOC profiles being investigated [18].
## 2 Materials and Methods
### Consumption Calculations and Statistical Analysis  
Energy measurements were converted from watts to kilowatts (kW) for standardization and analyzed to compare energy use between standard and optimized MRI protocols, focusing on total and per-sequence consumption. Sequence duration adjustments allowed for accurate per-sequence energy calculations. Descriptive statistics in Excel were used for quantitative analysis. Data visualization was conducted in PyCharm IDE (version 2024.1) using Python libraries: NumPy, Pandas, and Matplotlib.  
Figure 1 summarizes the tests conducted.  
Determining the most realistic standard sequence:  
Figure 2 shows an example of the daily electricity consumption profile of our MRI unit along with the daily hourly price variations observed during the winter season.  
Figure 1: Analysis of energy consumption and winter electricity prices by time period flowchart.  
Based on an 8 a.m. to 8 p.m. schedule, with peak periods from 9-11 a.m. and 6-8 p.m., several models were developed. The first optimization scheduled the least energy-intensive protocols during peak hours and more energy-intensive ones outside these times.  
The second optimization involved implementing these optimized protocols within the same operational window. Key constraints included mandatory 7 min, 1.5 kW breaks between protocols. Further constraints aimed to reduce antenna adjustments and implement body region-specific workflows to streamline technician operations. To optimize staff availability, protocols not involving contrast injection were scheduled at the beginning and end of the day.
###### Abstract  
This paper assessed the evolution of the performance ratio (PR) of a utility-scale photovoltaic (PV) installation that operates at subtropical climate conditions. The period of study encompassed 8 years, and the PR was calculated according to the ICE 61724 standard with a monthly resolution. A linear mixed effects model (LME) is a suitable tool for analyzing longitudinal data. Three LME models were assessed to provide the degradation rate. The \"null model\" evaluates the general relationship between PR and time with a monthly declination rate (\(\Delta\)PR%) of 0.0391%/month. The \"typology model\" considered the relationship between PR and, as covariates, time, _Manufacturer_, _Technology_, and _NominalP_. Only the \(\Delta\)PR% related to NominalP was found to be significant, so that, when the nominal power of a type of PV module used for a PV production unit is increased by one unit, the \(\Delta\)PR% of the corresponding unit increases by 0.000897%/month. Finally, the \"location model\" took into account the relationship between PR and, as covariates, time, _Edge_, and _LengthSt_. These last two factors were significant, resulting in an increase of 0.0132%/month for a PV unit located at the edge of the facility and 0.00117%/month and per PV production unit when considering the length of a street, respectively.  
solar photovoltaic; performance ratio; PV module degradation; mixed-effect linear modeling +
Footnote †: journal: Applied Sciences
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 6 Discussion of the Obtained Results
### Speed Comparison of EB-REP and Existing FRR Mechanisms
#### 6.1.1 Theoretical Comparison  
The FRR process begins with fast failure detections, especially using the BFD protocol and its hello message system. The link is declared as broken if one side of the BFD session does not receive three subsequent BFD hello messages. It usually takes around 30 ms using a 10 ms BFD hello message interval. Upon detection of a failure, an FRR mechanism must change routing information for the use of a new backup path.  
The switchover to the new backup path is in the order of milliseconds because the path is already precalculated by the EB-REP mechanism. From the theoretical view, we can state that the recovery rate provided by the EB-REP mechanism is comparable to other proactive FRR solutions.
# A Self-Supervised Learning Model for Unknown Internet Traffic Identification Based on Surge Period
## 4 Results and Discussions
### Experiment Performance
Accordingly, the \(P\), \(R\), and \(F_{1}\) score, are calculated as follows:  
\[P=\frac{TP}{TP+FP} \tag{9}\]  
\[R=\frac{TP}{TP+FN} \tag{10}\]  
\[F_{1}=\frac{2 PR}{P+R} \tag{11}\]  
As shown in Table 3, the \(P\) of the proposed self-supervised learning-based identification model could reach above 74% when identifying unknown Internet traffic, with the best performance of 86% under scenario A. Although the highest precision does not exceed 90%, it is still an outstanding achievement for the identification of unknown Internet protocols without labels. The data in bold in the table represent the best performance results under the listed conditions.The performance results demonstrate the proposed model could extract the flow-based characteristics and further realize the identification of unknown Internet traffic. We can also find that with the increase in the number of clusters, the performance indicators such as \(P\), \(R\), and \(F_{1}\) score are generally on the rise, while the rising speed is gradually slowing down. It shows that when the k_clusters reach a certain value, the evaluation indicators of our model tend to converge. Figure 6: The loss function of the JigClu-based feature pre-training model. When comparing the performance among different scenarios, it can be seen that the identification performance of our model would slightly decrease with the increasing types of unknown Internet protocols, but the overall performance is still good. To further illustrate the effectiveness of our proposed method, we have carried out a detailed analysis of the identification performance of each Internet protocol under different scenarios. Figure 7 shows the results. To be noted, all results are obtained under the optimal configuration state. As can be seen in Figure 7, the model shows different identification performances for different Internet protocols. It has a relatively good identification performance for most protocols, and some have reached a precision of over 90%. However, the performance is not so satisfactory for protocols such as the sftp in scenario C.  
We further analyze the identification results of sftp in scenario C. The final identification results are shown as follows: sftp (58%), scp (38%), and skype_file (4%). We find that nearly 40% of sftp protocols are regarded mistakenly as scp protocols. This is mainly because both sftp and scp are encrypted file transfer protocols. Compared with the scp protocol, the sftp protocol has less data, which also affects the identification performance. It implies that our model could effectively distinguish different types of unknown network protocols while the performance in identifying different Internet protocols of the same type needs to be strengthened.