esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 1 Introduction
(1) We present the design, development and hardware setup of a laboratory-based P2P voltage control testbed; (2) Secondly, we propose the use of a fully distributed dual-decomposition method to design a P2P voltage control system; (3) Thirdly, we propose the use of Long Range Wide-area network (LoRaWAN) technology to design a device-to-device communication system. The device-to-device communication is used to enable P2P data interchange between agents of the proposed voltage control system; (4) Finally, we validate experimentally that the proposed P2P voltage control system can indeed provide satisfactory regulation of the voltage profiles. The testbed presented in this paper provides realistic and pragmatic solution for evaluating P2P smart grid applications. The testbed is used to evaluate the performance of the proposed dual-decomposition-based voltage control system. It can also be used to evaluate other distributed applications for grid management. The testbed allows for re-using of the existing simulator code, while still facilitating accurate integration of power and communication effects on a real hardware platform. The rest of this paper is organized as follows. The laboratory-based P2P voltage control testbed is described in Section 2. Section 3 presents the P2P-based voltage control algorithm. Drive of the inverters is presented in Section 4. The Device-to-Device (D2D) communication modules used to enable P2P communication between the agents are described in Section 5. Section 6 presents the experimental results and the key performance indicators. Finally, the paper is concluded in Section 7 with future work.
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 3 Collaboration between Seaports and Dry Ports
### Original Data Processing  
The indicators in the performance evaluation system of the \"two-port\" cooperation constructed in this study belong to different types. In order to eliminate the interference caused by the order of magnitude, it is necessary to normalize the indicators. The common indicator types include the maximum-type indicator, that is, the larger the indicator value, the better; the minimum-type indicator, that is, the smaller the indicator value, the better; and the interval-type indicator, that is, the indicator value is best within a limited range. The following three mathematical models can be used to normalize the 14 indicators.  
1. For the maximum-type indicator, \[X^{\prime}_{ij}=\frac{X_{ij}-\text{min}X_{j}}{\text{max}X_{j}-\text{min}X_{j}}\] (7)
2. For the minimum-type indicator, \[X^{\prime}_{ij}=\frac{\text{max}X_{j}-X_{ij}}{\text{max}X_{j}-\text{min}X_{j}}\] (8)
3. For the interval-type indicator, \[X^{\prime}_{ij}=\left\{\begin{array}{l}1-\frac{a-X_{ij}}{\text{max}(a-\text {min}X_{j},\text{max}X_{j}-b)},X_{ij}<a\\ 1\qquad,a\leq X_{ij}\leq b\\ 1-\frac{X_{ij}-b}{\text{max}(a-\text{min}X_{j},\text{max}X_{j}-b)},X_{ij}>b \end{array}\right.\] (9)  
where \(X_{ij}\) is the \(j\)-th indicator of the \(i\)-th dry port to be evaluated; \(X^{\prime}_{ij}\) is the normalized value; \(\text{min}X_{j}\) is the minimum value of the \(j\)-th column of indicators; \(\text{max}X_{j}\) is the maximum value of the \(j\)-th column of indicators; and \((a,b)\) is the ideal interval of \(X\).
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 6 Conclusions  
This study examined the integration of quantum computing, classical methods, and deep learning techniques to enhance image processing in dynamic 6G networks. Our findings indicate that quantum methods are effective for rapid edge detection and feature extraction, though they struggle to maintain image quality compared to classical techniques. In contrast, classical methods provide superior image fidelity but often cannot meet the real-time processing requirements crucial for 6G applications. Deep learning approaches, particularly convolutional neural networks (CNNs), show significant potential for complex image analysis tasks, yet they demand considerable computational resources. To address the ethical considerations surrounding AI-generated content, we proposed robust copyright detection mechanisms that employ advanced algorithms to identify potential infringements. This integration not only supports compliance with intellectual property rights but also fosters the responsible use of image processing technologies. We suggest that the future of image processing in 6G networks will rely on hybrid systems that effectively utilize the strengths of each method while incorporating strong copyright detection capabilities. These insights are vital for developing efficient, high-performance image processing systems in next-generation networks, highlighting the promise of combined quantum-classical-classical deep learning architectures within 6G environments.  
Conceptualization, J.S. and H.-Y.K.; methodology, J.K.; software, A.K.; validation, J.K.; formal analysis, J.S.; investigation, A.K.; resources, J.K.; data curation, A.K.; writing--original draft preparation, J.S.; writing--review and editing, J.S.; visualization, A.K.; supervision, H.-Y.K.; project administration, J.S. and H.-Y.K.; funding acquisition, H.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Culture, Sports and Tourism R&D Program through the 860 Korea Creative Content Agency grant funded by the Ministry of Culture, Sports and Tourism in 2024 (Project Name: Global Talent Training Program for Copyright Management Technology in Game Contents, Project Number: RS-2024-00396709, Contribution Rate: 100%).  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Activation of Persulfate for Groundwater Remediation:
## 2 Activation Mechanism of Persulfate for ISCO Application
### Iron Activation
In addition, the addition of metal ions will introduce anions, which will involve in the homogenous reaction between the persulfate and metal ions, and thus affect the activation effect [84]. In contrast, the metal-based heterogeneous reaction system has the advantage of long-lasting activity for persulfate activation, which is associated with the solid form of the activators [38]. Natural minerals including goethite, hematite, pyrite, ilmenite, ferrihydrite, lepidocrocite and magnetite, have been shown to activate persulfate to degrade pollutants [85; 86; 87]. While the iron-contained minerals were proven to significantly activate persulfate, the powder form of those minerals are basically large, thus caused clogging when injected into the subsurface [40; 88]. In addition, the mineral content in some contaminated areas is too low to activate persulfates effectively to degrade contaminants [85]. Engineered nanoparticles are widely studied, such as the nanoparticles of zerovalent iron (nZVI), Fe\({}_{3}\)O\({}_{4}\) (nFe\({}_{3}\)O\({}_{4}\)), and ZVI based bimetal materials [89; 90; 91]. Among these engineered nanoparticles, nZVI seems to be more appropriate in in situ oxidation applications for its low cost and high activation efficiency [77]. [PERSON] et al. (2018) used nZVI particles as the activator for persulfate to achieve a complete removal of phenol [92]. [PERSON] (2021) also applied nZVI-activated persulfate to degrade TBBPA. Laboratory data showed under the set condition (25 mM persulfate, pH 5.5, and 3 g/kg nZVI) that the degradation efficiency of TBBPA (5 mg/kg) was 78.32% after 12 h [93]. The reaction in a persulfate/nZVI system involved two stages (fast reaction stage and slow reaction stage) [92]. In the early step, Fe\({}^{0}\)(s) was quickly consumed, and the aqueous Fe\({}^{2+}\) activated persulfate to generate large amounts of sulfate radicals. When controlled by Fe catalyzed activation in the presence of aqueous Fe\({}^{3+}\) and iron (oxyhydr)oxides in the nZVI shells, the second stage moved significantly more slowly than the first stage [92]. Although there are few studies in the literature reporting the successful application of nZVI in site remediations yet, they have broad application prospects in future site remediation based on their rheological properties and effective persulfate activation characteristics.
## 3 Results and Discussion
### 74 Unconfined Compressive Strength and Splitting Strength  
Unfortunately, we could only get an on-site sample after 7 days curing due to the limitation of the site construction environment. In order to ensure the consistency of the indoor experiment and scene, we had to cure the indoor specimens for the same days. So the 7 days unconfined compressive strength and splitting strength were used as indexes to verify the reliability and accuracy of the two compaction methods. Firstly, the cement stabilized macadam mixture was obtained from the on-site paver at the same time of the basic level paving and rolling. Then indoor vibration compacted (VC) and static pressing (SP) specimens were carried out quickly with a 98% compaction degree according to the compaction test results, and the prepared specimens were transported to the site and were cured together with the base after covering geotextile. After the health period, the specimens were taken out and the on-site sample was drilled nearby, and then cut the on-site sample (OS) into \(\phi 15\) cm \(\times\)\(h15\) cm standard specimens. Finally, the corresponding mechanical strength was tested, and the representative values were calculated under a 95% guarantee rate. The results are shown in Table 9.  
It can be seen that the mechanical strength of indoor static pressure compacted specimens is much higher than that of the on-site sample of base, whose respective ratio is 50.6% and 40.17%. However, the mechanical strength of the vibration compacted specimens and the on-site sample is very close, whose respective ratio is 102.5% and 98.2%. It can verify that the vibration compaction test is much more reliable and accurate in reflecting the actual performance of the field base than static pressure compaction.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 8 Validation
### Long-Range Radio Communication  
Information about the path loss of a communication link is of critical importance as the attenuation in power is deciding if packets can be successfully received or not. The Packet Error Rate (PER) is likewise important as the payload could withhold time-critical information that should not be lost in transmission, or affect the performance of any potential application running above LoRa.  
In the first experiment, we studied the effect of mounting the LoRa radio on a drone. In an anechoic chamber, we measured the signal loss due to the motor vibrations and blocking of the signal by the body frame of the drone. The drone was having the propellers spinning, making the drone almost takeoff. We determined a signal loss due to these impairments of about \(-3.5\) dB [55]. In the second experiment, the setup consisted of two Raspberry Pis, where one acts as a transmitter and the other as a receiver. This experiment does not use a drone as the effect of the drone has already been investigated in the first experiment.  
The steps are as follows: (1) The transmitter and receiver are placed at a fixed distance from each other at the same elevation; (2) The transmitter sends a packet to the receiver; (3) The receiver waits for the packet to arrive and logs the Received Signal Strength Indicator (RSSI) and payload information; (4) Steps 2-3 are repeated \(N\) times for the chosen Spreading Factor (SF) before a new distance is used. Figure 17 summarizes the results for RSSI and PER measurements of LoRa at different distances between the transmitter and the receiver. The distances are measured by a GPS attached to each LoRa radio module. The PER is calculated by counting packets with bit errors in the payload data. RSSI values are shown as box plot symbols. The measurements are based on the statistics of 4000 samples at each distance.  
Figure 16: Remote inter-process communication using either WiFi ad-hoc mode or access point mode.  
Figure 15: ROS and ROS2 remote interprocess communication with default configuration using WiFi infrastructure mode.  
The simplified path loss model is used as linear regression on the data samples. The model is shown below and used for obtaining the path loss exponent \(n\), and the shadowing effect \(X_{0}\):  
\[P_{L}(d)=P_{L0}-10\cdot n\cdot\log\biggl{(}\frac{d}{d_{o}}\biggr{)}+X_{0} \tag{1}\]  
The shadowing effect \(X_{0}\) showed in Equation (1) is a Gaussian distributed random variable with zero mean and the variance \(\sigma^{2}\). Based on our analysis, we find \(d_{0}=11.32\) m, \(P_{L0}=-92.655\) dBm, \(n=1.435\), \(\sigma=4.517\) with a \(R^{2}=0.7774\) coefficient for the regression. The achievable data rate in this setting was 5.5 kbps based on a theoretical calculation. We find that this data rate is sufficient to support the communication of telemetry information between a drone and the GCS.
# Effects of Freeze-Thaw and Wet-Dry Cycles on Tension Stiffening Behavior of Reinforced RAC Elements
## 3 Results and Discussion
### Effects of Freeze-Thaw and Wet-Dry Aging on Main Properties of Reinforced RAC Elements
For example, after 150 freeze-thaw cycles (Table 3) the percentage drop in first-crack values for the three concrete mixtures ranged from 5.0% to 6.5%. After 300 cycles, however, the \(\rm f_{1 st}\) decreased by about 13.0% for C35-NAT, 16.0% for C35-D-C1 and 18.0% for C35-L-C1. Figure 4: (a) Schematic description of the typical behaviors of the reinforced concrete and the isolated matrix contribution in the tension stiffening test with the indication of the main analysis parameters; (b) Load-strain curve for NAT-concrete mixtures; (c) Matrix contribution on tension-stiffening test for NAT-concrete mixtures. The same trend was observed for the samples submitted to wet-dry cycles (see Table 4). The decreases in first crack strength for C35-NAT element increased from 2.1% to 9.6% when the number of cycles was augmented from 25 for 50 cycles. The values observed for the NAT-concrete samples were, however, lower than the results obtained for concretes with RACs: C35-L-C1 registered a 4.7% drop in first crack strength after 25 cycles of wet-dry and 15% for 50 cycles, whereas C35-D-C1 achieved reductions of 3.2% and 11.6% for 25 and 50 cycles, respectively. The first crack strain (\(\epsilon_{\text{1 st}}\)) obtained for the natural mixture was slightly smaller than the values obtained for the RACs. For instance, C35-NAT presented the first crack at 89 \(\upmu\)c, while for C35-D-C1 and C35-L-C1 values were 99 \(\upmu\)c and 96 \(\upmu\)c, respectively. It was observed that, for the highest levels of degradation, all three mixtures showed an increase in this parameter what is in accordance with the discussion made for the elastic modulus of the mixtures. This can be highlighted by looking at REF(FT300) and FT300 values of C35-L-C1 mixture, which ranged from 101 \(\upmu\)eto 106 \(\upmu\)c.  
After the first crack, as the tensile load continued to be applied, more cracks appeared in the concrete (C\({}_{2}\),..., C\({}_{n}\)), until the matrix was divided into several segments. At this moment, the final post-cracking strain (\(\epsilon_{\text{final}}\)) was identified in the composite load-strain curve. The difference between the values of \(\epsilon_{\text{final}}\) and \(\epsilon_{\text{1 st}}\) determines the length of multiple cracking phase. The experimentally obtained values of \(\epsilon_{\text{final}}\) are in the range of 500 \(\upmu\)c for
## 3 Preliminaries
### Evaluation Metrics
As the number of trials increased, the fluctuations in the successful reconfiguration rate (SRR), average recovery time (ART), core reuse rate (CRR), and comprehensive evaluation metric (CEM) decreased, leading to lower error margins and greater accuracy and reliability. Therefore, a larger experimental sample size contributes to the reduction in measurement errors and biases, thereby enhancing the reliability and accuracy of the evaluation metrics. Additionally, it strengthens the overall assessment of topology reconfiguration algorithms, which is essential for advancing research in the fields of embedded systems and network-on-chip (NoC).
## 5 Conclusions  
This paper studies the spatiotemporal distribution and main driving mechanisms of carbon allocation to leaves (\(\Delta\)LAI) in forests in northeast China at five different time scales (GUP, April, May, June, and July), and elucidates the differences in the impact of \(\Delta\)LAI on the GPP. The following conclusions were drawn:  
1. Owing to the differences in physiological attributes, in the GUP, the \(\Delta\)LAI values of DBF and MF are much higher than that of DNF, and all three show an insignificant increasing trend each year. The highest \(\Delta\)LAI in DBF occurred in April and in DNF and MF it occurred in May. The \(\Delta\)LAI of DBF and MF showed a significant year-by-year increasing trend in April, and DNF showed a significant increasing trend in most areas in May;
2. The main factors driving \(\Delta\)LAI in GUP are TEM and SOS. The main driving factors in April and May were SR and SOS. The driving mechanism in June was the most complex, and the difference between different forestlands was the highest. Except for PRE in DBF and MF, all other factors had larger path coefficients. The coefficients of SR and SOS were the highest for DNF;
3. \(\Delta\)LAI in GUP has a significant impact on the GPP. In the MF, the higher \(\Delta\)LAI in May was most conducive to an increase in GPP. In DBF and DNF, the \(\Delta\)LAI in April and May both promote the increase of GPP.  
This study examined the changes in forest carbon allocation to leaves and its driving factors on a large scale, providing an important basis for understanding the combined influence of plant traits and the environment in restricting carbon allocation strategies. It is evident that the development of forest leaves is based on environmental factors and attributes of forestlands. During the critical period, that is April, carbon allocation to leaves effectively improves the carbon sequestration capacity of forestland. This information is of great value for the development and validation of terrestrial ecosystem models.  
Conceptualization, methodology, and writing--original draft, Z.L.; methodology, supervision, and inspection Q.L.; software Z.L. and Q.L.; validation, Y.B.; review and editing, B.S.; investigation and validation, Z.B.; Formal analysis, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Inner Mongolia Autonomous Region Natural Science Foundation (2022 MS04006 and 2022 LHQN04002) and the Introduction of High-Level Talents Scientific Research Start-up Fund Project (2022 JBY030).  
The authors thank the editors and the anonymous reviewers for their crucial comments, which improved the quality of this paper.  
The authors declare no conflict of interest.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 5 Federated Training of Edge Autoencoders
### IID--Partitioned Dataset
#### 5.2.1 Training on Distinct MNIST Subsets  
In this experimental setup, we investigate a more typical IID scenario where each ESP32-CAM device is assigned a distinct subset of the MNIST dataset. The data is partitioned such that each device receives 1000 samples containing all digit classes (0-9) in approximately equal proportions. For example, device 0 receives the first 1000 images, device 1 the next 1000, and so on. This ensures that, although the specific samples differ, the class distribution remains balanced across devices.  
Unlike the extreme non-IID case described earlier, this setup reflects a more realistic federated learning environment in which devices independently collect diverse but representative data. This experiment allows us to evaluate how well a lightweight AE model can learn meaningful global features when trained on individually balanced but disjoint local datasets. Since the data distributions are representative and overlapping, we expect faster convergence and improved generalization compared to non-IID scenarios.
# Security Audit of a Blockchain-Based Industrial Application Platform
## 4 Attacks and Countermeasures
### Attacks
#### 4.1.2 Infrastructure--Software  
Because a variety of software are used to operate the blockchain and related systems, attacks on that software must be considered.  
Malware: Adware, Spyware, Trojan, Virus and Worms
* _Auxiliary DB_: by infecting the Auxiliary DB, the attacker can delete or encrypt data. Data can be manipulated without leaving any external or internal traces (no logs, etc. except the malware itself). By being already inside the trusted network, either data can be sent to external servers controlled by the attacker or other internal targets can be attacked.
* _Condition monitoring_: by having control of the system, an attacker can either disable alarms or create fake alarms, either completely destroying already defective machines or completely shutting down fully functional machines. The attacker can also replace the trained model or gain access to confidential model data.
* _OPC UA server_: a malware infection of the OPC UA server enables the attacker to send forged data to the connected systems (e.g., the blockchain). On the other hand, they are also able to control the machine to the point of damaging it by sending it fake or implausible commands.
* _Sensor_: by infecting the sensor arrays, it is possible to provide fake data that cannot be detected using the other systems. The falsified data can be used to shut down machines or to manipulate them directly into the production process.  
Timejacking
* _Client to peers_: if a client has an incorrect time, the specified transaction period is no longer valid and the transaction is immediately rejected.
* _Peers in blockchain_: if a peer has a different time because it uses a compromised NTP server, the result is similar: the proposed transaction will fail with that particular peer because the valid time frame can no longer be guaranteed. Therefore, depending on the transaction policy, the whole transaction may be rejected.