esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Zone-Based Secure Multicast Techniques
### Secure Multicast Technique  
The secure multicast technique consists of two main steps to increase the reliability of the transmitted data and provide integrity. In the first step, the member node that wants to transmit data in the multicast group is encrypted using a member key issued by the group management node, and then the encrypted data are transmitted to the group management node. Encryption of the transmitted data is performed by Equation (1).  
\[\mathrm{E(M_{i})}=\sum_{i=1}^{n}(C_{-}P(k,M_{i})) \tag{1}\]  
Here, \(C_{-}P(k)\) is represented the member key, and \(M\) means to be transmitted by the member node. In the second step, the group management node performs block encryption of encrypted data received from the member node by using the group key \(v_{p}\) issued by the authentication node. The process which authentication management node issues the group key is as follows.  
Step 1: Group management node \(R1\) generates an encryption key pair \(\big{(}(G_{R_{1}},P_{R_{1}})P\big{)}\) by the authentication management node and requests to issue a group key.  
Step 2: The authentication management node receives the group key request confirms the group management node and generates the group key.  
Step 3: Member key \(\big{(}K_{R_{i}N_{i}}\big{)}\) is issued to the member nodes using the received group key \(\big{(}GK_{R_{1}},H(k,M)\big{)}\).  
\(E(M_{i})\) that the member node encrypts using the member key in half to the left and right as shown in the following Equation (2).  
\[E(M_{i})=(L_{0},R_{0}) \tag{2}\]  
Figure 4: Management process for moving member nodes.  
In each ith rotation, the new left and right sides to the center are calculated by Equations (3) and (4).  
\[\mathrm{L_{i}}=\mathrm{R_{i-1}} \tag{3}\]  
\[\mathrm{R_{i}}=\mathrm{L_{i-1}}\cdot\mathrm{F(R_{i-1},\,u_{pi})} \tag{4}\]  
Here, \(\mathrm{u_{pi}}\) is an auxiliary key used for the ith rotation and becomes a unique key issued by the authentication node. The final cipher text obtained through this process is shown in Equation (5).  
\[\mathrm{C(E(M)=(L_{n},R_{n})} \tag{5}\]  
When the cipher text generated through the above process is transmitted to the multicast group, each group management node receiving it performs decryption using a group key issued by the authentication node. Then, the group management nodes transmit packets to the member nodes they manage, and the member nodes decrypt the data using their own member keys. Since the data are transmitted after this encryption process, secure multicast is provided by providing a strong block from data forgery attacks by malicious nodes and eavesdropping attacks. Figure 5 shows the transmission process of secure multicast.
# GNSS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III
## 1 Introduction
Hence, the transmission channel characteristics of the navigation payload are calculated through the \"received signal\", which can not only support the signal quality monitoring and evaluation of GNSSs but also provide a reference for differential positioning users. Moreover, it is a basis for the adjustment of signal pre-distortion at the satellite. Thus, in-depth research must be conducted on the characteristics of the navigation payload transmission channel according to the signal quality assessment. There have been some studies on the modeling of signal distortion in the transmission channel. A class of signal distortion models proposed by the International Civil Aviation Organization (ICAO) is widely used in signal deformation. These models include digital distortion (Threat Model A, TMA), analog distortion (Threat Model B, TMB), and hybrid distortion (Threat Model C, TMC). The signal distortion caused by digital equipment is usually modeled with TMA, that caused by analog equipment is usually modeled with TMB, and that caused by the interaction of the two is usually modeled with TMC [12]. However, related studies have shown that the second-order model in TMB is obviously not accurate enough for modeling the analog distortion of wideband modulated signals [10]. Thus, some scholars used high-order finite impulse response (FIR) filters to replace the TMB model. [PERSON] modeled the navigation load transmission channel as a linear FIR filter through simulation and estimated the transmission channel characteristics using the least square method [13]. [PERSON] modeled the transmission channel as an FIR filter and obtained the impulse response of the FIR filter through the cumulative inversion of the correlation domain [11]. [PERSON] modeled the BDS-2 transmission channel as a TMB model and used the least square method to obtain the transmission channel characteristics [14]. The signal distortion in the transmission channel has previously been modeled. However, only analog devices were considered, and digital devices were neglected. In addition, the applicability of wideband modulated signals remains to be studied. Therefore, there is still potential for research accurately estimating the transmission channel characteristics from the received signal. In the continuous monitoring of the GPS signal, we found that the GPS III signal was significantly improved compared with the GPS II signal, especially in the L5 frequency band. This study conducts a detailed analysis based on the L5 measured signal. The remainder of this paper is briefly structured as follows: Section 2 details the navigation payload transmission channel model and joint estimation method. Section 3 verifies the model and algorithm of this paper according to the measured data of the GPS-L5 signal, as well as analyzes and discusses the GPS signal evaluation results. The paper is summarized in Section 5.
# Structural Augmentation in Seismic Data for Fault Prediction
## 3 Seismic Fault Detection Based on U-Net
### Network Training  
As shown in Figure 9, to further compare and validate the effectiveness of the structural data augmentation framework, our workflow is compared with three recently popular machine learning approaches (manually interpreted data learning, synthetic data learning, and transfer learning).
## 5 Questionnaire and Quantification Method Approach
### Structure and Scoring Logic  
The questionnaire consists of 60 closed-form questions, each mapped to one of the 18 (plus one) technical debt types and assigned to a corresponding stakeholder (Organizer or Participant). All questions follow a binary response format:  
* YES--Best practice applied (reduces debt).
* NO--Best practice not applied (adds debt).
* I Don't Know/I Don't Answer--Indicates uncertainty (adds debt).
* Not Applicable--Neutral, scored as zero.  
Each item is internally assigned a severity weight from 1 to 5, based on its perceived importance in mitigating the relevant technical debt, as derived from our literature synthesis and thematic analysis (see Section 4). While respondents are not informed of the exact weights, the scoring logic operates as follows:  
* YES: reduces the total debt score, yields a negative value equal to the weight (\(-\)w).
* NO or I Don't Know/I Don't Answer: increases the score, yields a positive value equal to the weight (\(+\)w).
* Not Applicable: scored as zero (neutral).  
The cumulative score for each debt category is calculated by summing the individual item scores. This aggregated value reflects the perceived severity of the technical debt:  
* A total score \(\leq\)0 indicates low or no observable technical debt.
* A score between 1 and 10 suggests moderate technical debt.
* A score above 10 indicates significant technical debt accumulation, signaling a need for focused mitigation.  
A positive total score reflects divergence from best practices, while a negative or near-zero score suggests alignment. This scoring system enables comparative analysis across stakeholder groups and debt types and supports longitudinal tracking of improvements over time.  
For certain categories, such as Accessibility Debt, responses are currently solicited only from organizers, given their direct control over platform design and usability. In future implementations, feedback from participants may be integrated to provide a more comprehensive assessment.  
These category-level severity scores, as summarized in Figure 4, were derived by aggregating question-level weights and triangulating them with insights from the literature. While these scores are not intended to be absolute measures, they serve as relative indicators to support stakeholder prioritization, benchmarking, and longitudinal tracking. The scoring logic is designed to be adaptable and may be refined in future iterations based on empirical feedback or expanded debt taxonomies.
# Arginine-Mediated Self-Assembly of Porphyrin on Graphene: A Photocatalyst for Degradation of Dyes
###### Abstract  
Porphyrin nanostructures with well-controlled size, shape and functionality can be used for visible-light photocatalysis. In this work, a graphene@porphyrin nanofibre composite was successfully fabricated via arginine-mediated self-assembly of tetrakis (4-carboxyphenyl) porphyrin (TCPP) on graphene nanoplates (GNPs). The formation and crystallisation of the graphene@porphyrin nanofibre composite was fully characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), fourier transform infrared (FTIR), ultraviolet-visible (UV-vis) and fluorescence spectroscopy. The assembled TCPP nanofibers were 50-200 nm in diameter with length in micrometers long, which were densely and uniformly distributed on the surface of graphene. The GNPs@TCPP nanofibers showed enhanced visible-light photocatalytic activity in comparison with free-standing TCPP nanorods for the degradation of Rhodamine B (RhB) and methyl orange (MO). The possible photodegradation mechanism of these dyes by the GNPs@TCPP nanofiber photocatalyst was proposed.  
Keywords:tetrakis (4-carboxyphenyl) porphyrin; self-assembly; photocatalyst; dyes degradation; pollutant treatment; graphene nanoplates +
Footnote †: journal: Applied Physics Letters
## 3 Soil Features as a Consequence of Reduced Fertilization
### Soil Biology  
The biological processes within soil are closely linked to N availability, and reduced N fertilization has a direct impact on soil biological activity. Lower N inputs slow down the activity of N-dependent microbes, particularly those involved in nitrification and denitrification [32; 33]. However, while there may be a decrease in overall microbial activity, a reduced N regime encourages a shift in the composition of microbial communities. Instead of favoring fast-growing, N-hungry microbes, reduced fertilization supports the proliferation of microorganisms that are better adapted to low-N environments, such as N-fixing bacteria like _Rhizobium_, _Azotobacter_, _Bradyrhizobium_, and _Frankia_; beneficial fungi like arbuscular mycorrhizal fungi (_Glomeromycota_); and decomposers like _Pencilillium_ and _Trichoderma_[74; 75; 76]. These organisms play a crucial role in maintaining soil health by compensating for the lower external N inputs through natural N fixation and OM decomposition [76]. In particular, symbiotic N-fixing bacteria and fungi improve nutrient cycling, enhancing the sustainability of the soil's biological system and supporting long-term fertility [31; 76].
# Seismic Reflection Analysis of AETA Electromagnetic Signals
## 4 Results
### Seismic Case Analysis
#### 4.1.1 Analysis of Jizhaigou M. 7.0 Earthquake  
When the Jizhaigou M. 7.0 earthquake occurred, there were six effective AETA monitoring stations within 200 km of the epicenter. Table 1 shows the location and installation date of stations within 200 km of the earthquakes. Figure 8 shows the fitting effect of the LAE on the electromagnetic data of JZG and SP stations half a month before and one week after the earthquake. Figure 9 shows the curve fitted by the model on a specific day with or without anomalies, which can show the fitting ability of the model more clearly.  
outliers exceeding 0.7, SP showed the most with two points. There were only six abnormal points in QCX with no value exceeding 0.7. In summary, before the Jiuzhaigou earthquake, the electromagnetic data of all the AETA stations showed abnormal disturbances, and that the number of anomalous points was irrelevant to the distance from the station to the epicenter. The reason may be that the electromagnetic waves received by different stations contain different seismic information that depends on geological structure when the electromagnetic data are propagated underground.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Station** & **Installation** & **Location** & **Epicenter Distance** \\ \hline — & Epicenter & — & 33.20\({}^{\circ}\) N, 103.82\({}^{\circ}\) E & 0 \\
1 & JZG & 10 June 2017 & 33.25\({}^{\circ}\) N, 104.24\({}^{\circ}\) E & 40 km \\
2 & SP & 12 June 2017 & 32.65\({}^{\circ}\) N, 103.60\({}^{\circ}\) E & 64 km \\
3 & PW & 8 June 2017 & 32.41\({}^{\circ}\) N, 104.55\({}^{\circ}\) E & 111 km \\
4 & QC & 7 June 2017 & 32.59\({}^{\circ}\) N, 105.23\({}^{\circ}\) E & 147 km \\
5 & MX & 13 June 2017 & 31.69\({}^{\circ}\) N, 103.85\({}^{\circ}\) E & 167 km \\
6 & WC & 13 June 2017 & 31.48\({}^{\circ}\) N, 103.59\({}^{\circ}\) E & 192 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: AETA Station within 200 km of the Jiuzhaigou epicenter.  
Figure 7: The epicenter of two major earthquakes and nearby stations. (**a**) Jiuzhaigou M. 7.0; (**b**) Yibin M. 6.0.
## 4 Results on ML/DL-Based Adaptive Modulation
### Comparative Analysis of Adaptive Modulation Techniques  
Tables 14 and 15 present the evaluation of systems implementing adaptive modulation, categorized into SC-UWA and MC-UWA systems, respectively. The corresponding attributes have already been explained in Section 3.3. In UWA communication, throughput reflects the average number of successfully received bits per unit time, factoring in protocol overhead and errors. Accurate channel classification ensures optimal modulation decisions, boosting data rates and reducing retransmissions. Low precision, however, leads to errors and underutilized bandwidth. Thus, the throughput values in Tables 14 and 15 denote the accuracy of selecting the most suitable modulation scheme.  
In single-carrier systems, training loss values are often not provided, with some studies using mean squared error or threshold-based approaches. Complexity is generally unspecified, but throughput improvements range from 3.6% to over 99% accuracy. The BER gains vary, with some studies reporting substantial improvements while others providespecific values. In multi-carrier systems, training loss values include actor and critic loss, CE, and mean squared error, though some studies do not specify a loss function. Complexity varies, with some models having higher processing and memory demands. Throughput improvements range from near ideal performance to specific percentage increases, such as four percent higher accuracy. The BER gains are generally better than threshold values, with some studies maintaining a stable BER of 0.001 or reporting improvements of up to 32 percent.  
Overall, SC-UWA systems use ML techniques like SVM, KNN, LDA, MLP, CNN, and RL. They rely on measured data, simulated data, or no dataset in the case of RL. MC-UWA systems use adaptive modulation with A-kNN, CNN, and RL. Training sources vary, and Bellhop is commonly used. RL-based approaches often focus on theoretical learning. SC-UWA focuses on real data and classical models. MC-UWA emphasizes reinforcement learning and simulation. This shows a shift toward smarter, flexible methods in UWA communication.
## 4 Command and Control  
With reference to the architecture of Figure 2, the C&C is a distributed software component whose responsibilities are: data fusion for situational awareness, implemented by the Data fusion subsystem, smart alarm management, implemented by the Security subsystem, and communication management, implemented by the Message broker.  
Data fusion. This is the core functionality of the Data fusion component, intended as a combination of data from multiple sources to improve the interpretation performances of the source data [27]. This component collects and processes data from the video and sensor-based systems to timely generate triggers and to deliver information related to suspect persons.  
Monitoring and persistent storage of data. The data fusion is extended with functions for run time data visualization and storage. The former is accomplished by a Monitoring component providing data for Grafana dashboards [28]. These display events of interest of system performance at run time, captured by the Prometheus temporal database server [29]. Persistent storage of all the messages received and sent by the C&C is accomplished by the Instead DB implemented in MySQL [30]. Recorded data includes partial and complete sensor and video results, useful for system assessment and forensic activity.  
Smart alarm management. This function, implemented by the Security subsystem component, concerns dispatching all the results of interest, including alerts, to several security sites, such as the agent smart glasses and a security room. The Security subsystem component includes a web application (_GUI_) devoted to displaying system results to the various clients that may be registered to this aim and an Alarm management component for dispatching alarms to the smart glasses subsystem and for receiving confirmation messages by the end users.  
Communication management. This function is implemented by the Message broker that follows a publish-subscribe protocol to dispatch messages to all the technological components of the architecture.  
A more detailed description of the implementation of the C&C components follows.
# A Transfer Learning Method for Pneumonia Classification and Visualization
## 5 Results
### Experimental Framework  
After applying the proposed preprocessing technique and solve the problem of imbalanced sets we used a total of 2698 CXRAY images. Upon the final images, a partition was performed with an 80-20 hold-out as a validation method. It is important to note that the partition used to obtain both training and validation sets were performed only once. Therefore, conducted experiments used always the same sets and a fixed seed was established in order to replicate the results. Finally, the test set was not modified by RUS, conserving all 624 instances for testing purposes.  
The Xception CNN model used in this research, and the models used for comparison were implemented in the Deep Learning framework Keras [16] that includes the tool for data augmentation. The pre-processing technique and RUS were both implemented in Python 3.6 using OpenCV [49] as the main image processor library. All experiments were conducted in the Google Collaboratory platform that in our experiments provides a Linux platform with 12 GB of RAM and a GPU Nvidia Tesla K80 with 12 GDDR VRAM. The average training time was 53 s per epoch, 0.779 s per batch. Therefore, each example was processed in approximately 0.0244 s.  
Experimental framework and proposed methodology summaries as follow:  
1. RUS is performed over the original training data of the Pneumonia dataset.
2. Hold-out 80-20 was performed in order to obtain the training and validation sets.
3. Pre-processing technique was applied to training, validation and original test sets.
4. Training and validation sets were fed into the network.
5. The test set was fed into the trained network to obtain the classification results.
6. Grad-CAM was used in order to generate a heatmap of the possible localization of pneumonia manifestations on test images.  
Figure 9 shows the general diagram of the proposed methodology.  
Figure 9: General workflow of the proposed methodology.
# FLINT: Flows for the Internet of Things
## 5 Evaluation
### Mapper Forwarding Rate  
FLINT provides flexibility in connecting non-interoperable IoT networks. Due to its modularity, it is possible to interconnect various IoT components on top of which the _Mapper_ adapter can queue packets based on the interface type of the device. These advantages, however, come at a cost. This section analyzes the drawbacks introduced by the platform.  
The above section used six _direct_ adapters. This type of adapter forwards the data directly to another endpoint in the platform. However, the _Mapper_ adapter can be used to forward data between several components. As shown in Figure 11, the average latency increases and the goodput decreases, since the message bus must process twice as many messages.  
In order to measure the impact of another hop, a _dummy_ adapter was added to the system. This is a processing adapter that forwards the packet after a _No Operation_ (NOP). Messages will now flow from the MQTT adapter to the _Mapper_ adapter, to the _dummy_ adapter and finally to the UDP adapter. Using this configuration, the message bus must process three times the amount of messages compared to the second configuration.  
An overview of the measurements is given in Table 2. It can be seen that the message bus and the _Mapper_ adapter form a bottleneck for the system. In the future, this can be solved in several ways. A first solution could use MQTT v5 Shared Subscriptions. This feature distributes the load across all subscribers on the same topic and is sometimes referred to as client load balancing. This allows setting up multiple Mapper adapter instances without having duplicates. To avoid saturation of the MQTT broker, a cluster of MQTT brokers can be deployed. Managing a cluster of MQTT brokers, however, requires non-standard broker discovery, subscriber takeover and routing management. Therefore, other messages busses, such as Apache Kafka or Pulsar [32] can be used. This would require a few changes to the system. First, the sink must be replaced in order to communicate with the improved message bus. In Apache Kafka, multiple partitions can then be used to form consumer  
Figure 11: The latency (in seconds) and goodput for six MQTT adapters measured for different configurations in FLINT.  
groups. These groups avoid duplication of data over multiple members subscribed to the same topic. As such, multiple mapper instances can be deployed, without the need to redesign the Mapper adapter completely. However, all Mapper adapters should access the same Thing Descriptions, and therefore require access to a shared Thing Directory.