esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation
### Code Synchronization  
The simulation of overall code synchronization combines code acquisition and code tracking, demodulation of the modulated baseband signal through code synchronization, and restoration of the baseband signal. The simulation result of overall code synchronization is shown in Figure 23, which shows that the demodulated baseband signal is chaotic when the signal is not synchronized and correctly restored when the signal is synchronized. Hence, we see that the DSSS receiver restores the signals transmitted by the transmitter accurately with regard to both time and frequency, based on our proposed serial code acquisition.  
Figure 22: Simulation result of code tracking.
# Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration
## 3 Results
### Evaluation
#### 3.2.4 Number of Clusters  
After selecting the clustering factors and regional factors in the previous section, we test the impact of the number of clusters on the calibration performance. As shown in Figure 12, it is found that the RMSE of the three groups of data decreases with an increase of the number of K-means clusters. However, when the number of clusters increases to a certain range, the reduction of RMSE is limited.  
Therefore, we can choose a moderate number of clusters, such as 6, as this can control the amount of computation in a small range to obtain sufficient calibration performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Combination**} & **Clustering** & **Regression** & \multirow{2}{*}{**Group 1**} & **Group 2** & **Group 3** \\  & **Factors** & **Factors** & & & \\ \hline
1 & P+T & P+T & 4.63 & 9.69 & 11.33 \\
2 & P+T & P+T+H & 4.45 & 7.53 & 9.96 \\
3 & P+H & P+H & 4.48 & 7.56 & 9.7 \\
4 & P+H & P+H+T & 4.43 & 7.33 & 9.42 \\
5 & P+H+T & P+H+T & 4.46 & 7.68 & 9.56 \\ \hline \end{tabular}
\end{table}
Table 1: Use RMSE as an indicator to evaluate the performance of different models for PM\({}_{2.5}\) calibration, where P represents PM concentration, T represents temperature, H represents relative humidity. Division basis for K-means clustering/Parameters used in regression equations in the legend as: P+T/P+T means select P and T as clustering basis, select P and T as training parameters of regression equation.
## 2 Literature Review
### Agriculture  
The integration of IoT technologies into agriculture has revolutionized farming practices by providing real-time data on crop health, soil moisture, and weather conditions, enabling farmers to make informed decisions and optimize operations [54; 55]. However, traditional agricultural systems often face challenges such as data tampering, lack of transparency, and inefficiencies in record-keeping, which can lead to issues like theft, unavailability, and damage to records [31]. DLTs can significantly enhance agricultural product traceability from farm to market, ensuring all stakeholders have access to accurate, tamper-proof information while improving product safety and quality throughout the SC. For example, blockchain has been employed in [56] to develop a framework that continuously monitors crop, soil, and environmental conditions, supporting sustainable and eco-friendly farming practices. The system used blockchain's inherent immutability and data provenance to ensure the authenticity and traceability of agricultural produce, while also enabling transparent decision-making and reliable food certification across the agri-chain. However, traditional blockchain implementations often encounter significant limitations in Food and Agriculture Infrastructure due to increased latency, high resource consumption, and elevated communication overhead, as discussed in [57]. These challenges hinder the system's ability to meet large-scale, real-time identity authentication demands and contribute to substantial electricity consumption, ultimately affecting the scalability and practicality of blockchain deployment in real-world agricultural scenarios.  
Recently, Holochain has emerged as a promising alternative in agriculture. For example, a Holochain-based, IoT-enabled agri-food traceability system was proposed for the dairy industry, demonstrating greater sustainability and efficiency compared to traditional blockchain models [30]. Beyond traceability, Holochain enables secure and private data sharing among SC stakeholders, including farmers, suppliers, certifying authorities, and consumers, ensuring the authenticity of health certificates and compliance with food safety regulations. Unlike blockchain platforms that rely on smart contracts, Holochain enforces data integrity and rules of interaction through custom validation logic and P2P protocols defined within its distributed applications. These mechanisms ensure that only certified products are admitted to the decentralized market, preventing the sale of food without proper documentation and establishing decentralized control based on verifiable credentials and cryptographic proof. It also enables the creation of decentralized marketplaces, allowing farmers to directly connect with buyers, reducing intermediaries and ensuring fair pricing. Holochain can also optimize the use of resources such as water, fertilizers, and pesticides through real-time monitoring and data analysis and protect land ownership records against unauthorized access and corruption by maintaining them on a secure, decentralized ledger.
## 3 Message Format  
The message format is defined by the structural rules of forming the messages and their contents in a service's interaction protocol or interface description. Every protocol may define several types of messages with each having its particular format. Understanding the protocol's message format is crucial to mimic each service's behaviour. Some of the researchers extract the key parts of messages and their offsets, which indicate their location from the start of the message, while others extract the full structure of messages. As Figure 2 shows, each message consists of a header and payload. Each header and payload can later be divided into their relevant bytes of information and tagged separately. This section starts with the research dedicated to discovering partial message format and then continues reviewing work focused on inferring the whole message format.
## 4 Methodology
### Sequential Optimization Problems
#### 4.2.2 Fair Technology Distribution Problem  
The solution to the problem of Section 4.2.1 provides the \(\kappa\) technologies, out of the \(N\) available, that will be used in the design. Most of the time, the number of these available technologies is less than the number of nodes in the network, meaning that several nodes will be using the same technology. The number of required devices, per selected technology, to minimize the vulnerability of the entire network is calculated by fairly balancing the total number of SRNG events among the network devices. This design step depends on the number of nodes in the analyzed topology and, in practice, is constrained by a fixed CAPEX. The problem is mathematically stated as:  
\[\mathbf{n}^{*}=\underset{\mathbf{n}\in\mathcal{N}}{\operatorname{argmin}}\ \sum_{k=1}^{\kappa}\Big{(}\alpha_{k}n_{k}-\alpha_{c}\frac{n}{\kappa}\Big{)}^{ 2}, \tag{6}\]subject to:  
\[\sum_{k=1}^{\kappa}n_{k}Q_{k}\leq B,\] (the CAPEX constraint), (7) \[\sum_{k=1}^{\kappa}n_{k}=n,\] (the number of nodes constraint), (8)  
where \(\mathbf{n}=(n_{1}\dots,n_{k},\dots,n_{\kappa})\) is a vector of \(\kappa\) elements that specifies the number of nodes of each technology, \(\mathcal{N}=V^{\kappa}\) represents the search space in the optimization problem and corresponds to the collection of every possible combination of number of nodes per technologies that can be selected, \(Q_{k}\) is the cost, in some predefined currency, of each node belonging to the \(k\)th technology, \(B\) is the total CAPEX available to purchase network nodes, and \(\alpha_{c}=n^{-1}\sum_{k=1}^{\kappa}\alpha_{k}n_{k}\). (We refer to [13] for the details on how to obtain the value of \(\alpha_{c}\)).  
The term \(\alpha_{k}\) is a key parameter termed as the risk index associated to the \(k\)th technology. We introduced this parameter for the first time in [13], and in this paper, we redefine it formally in a more practical manner using the formula:  
\[\alpha_{k}\triangleq\sum_{r=1}^{M}X_{kr}P_{r}, \tag{9}\]  
which means that the risk index of each technology is given by the failure probability of each SRNG event, disclosed in the market, that affecting it. Note that we have exploited the assumption about the SRNG events being mutually exclusive.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using JAMOVI(r) v.2.3.24 software. For all variables, the data were expressed as mean and standard deviation (SD). The Shapiro-Wilk test was used to determine the normality of the variables. In addition, within-session reliability of test measures was analysed using a two-way random intraclass correlation coefficient (ICC) with an absolute agreement (95% confidence intervals). Intraclass correlation coefficient (ICC) values were interpreted according to [PERSON] and [PERSON] [29] considering \(>\)0.9 = excellent, 0.75-0.9 = good, 0.5-0.75 = moderate, and \(<\)0.5 = poor, respectively. Additionally, the coefficient of variation (CV) was calculated, and a value of \(<\)10% was considered acceptable [30].  
The differences in shooting speed according to the kind of technique used (static drive shot, static slap shot, dynamic drive shot, and static drive shot), were tested using a one-way analysis of variance (ANOVA). To observe the pairwise differences between groups, Bonferroni post-hoc tests were used. The significance level was set at \(p<0.05\) for all statistical analyses. Additionally, effect sizes were reported as partial eta-squared (\(\eta_{\text{p}}\)\({}^{2}\)), with cut-off values of 0.01-0.05, 0.06-0.13, and \(>\)0.14 for small, medium, and large effects, respectively [31]. For pairwise comparison, [PERSON]'s \(d\) effect size (ES) with 95% confidence intervals was calculated [31], and the magnitude of the ES was interpreted as \(<\)0.2 = trivial; 0.2-0.6 = small; 0.6-1.2 = moderate; 1.2-2.0 = large; \(>\)2.0 = very large [32].  
[PERSON]'s correlations were used to examine the relationship between bench press variables (1 RM, mean power, and peak power) and shooting velocities (static drive shot, static slap shot, dynamic drive shot, and static drive shot). Statistical significance was established at \(p\leq 0.05\). Correlations magnitudes were evaluated using the Hopkins scale and interpreted as follows: trivial (0.00-0.09), small (0.10-0.29), moderate (0.30-0.49), large (0.50-0.69), very large (0.70-0.89), nearly perfect (0.90-0.99), and perfect (1.00) [32].
## 3 Results
### Improvement of Soil Enzymes Activity by the Interaction of Biochar with Microbes  
The activity of phosphatase and urease was compared in different soil treatments. As shown in Figure 5A, biochar could significantly increase the acid phosphatase activity in the soil. When microbes were applied together with biochar in the soil, the acid phosphatase activity was increased compared to that when only biochar or microbes were applied to the soil. When the microbes were applied in soil supplemented with 2% biochar, the acid phosphatase activity was highest and 61.2% higher than that of control in the sterilised soil, as well as 39.3% higher than that in soil with 2% biochar.  
As shown in Figure 5B, significant differences in urease activity were not detected in soil with increasing amounts of biochar, whereas urease activity was significantly increased when the microbes were applied to the soil. The maximal increment in urease activity was showed in the soil added with both 2% biochar and soil microbes, which was 49.4% higher than that in the soil without the addition of biochar and microbes. There was a significant reduction of urease activity, when the biochar mass reached 5%.
## 2 Materials and Methods
### Monitoring the Influence Of Factors On The Apparent Sound Reduction Index--DOE Method  
At the beginning of the planning, the objective of the experiment, input factors, and monitored outputs of the process were determined. In our case, a complete factor plan with three factors was chosen: frequency (factor A), excitation interval (factor B), and the type of excitation noise (factor C). Each of the input factors acquired two levels.  
The objective of the experiment was to determine which of the considered factors or their interactions significantly affect the apparent sound reduction index of the partition structure (dB). Input factors and their levels are shown in Table 1.  
The effects of all main factors (A, B, and C) are given in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Designation**} & **Frequency (Hz)** & **Excitation Interval (dB)** & **Type of Noise** \\ \cline{2-4}  & **A** & **B** & **C** \\ \hline Low (\(-\)) & F1 & 76 & White \\ High (\(+\)) & F2 & 100 & Pink \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input factors and their levels in the experiment with three factors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency Interval (Hz)**} & \multicolumn{4}{c}{**Factors**} \\ \cline{2-5}  & **A** & **B** & **C** \\ \hline
**I** & **effect** & 18.190 & 8.675 & 5.075 \\  & **_p-value_** & 0.003 * & 0.009 * & 0.049 * \\ \hline
**II** & **effect** & 8.023 & 1.243 & \(-\)0.262 \\  & **_p-value_** & 0.0001 * & 0.040 * & 0.583 \\ \hline
**III** & **effect** & 4.258 & 0.382 & \(-\)0.117 \\  & **_p-value_** & 0.0001 * & 0.146 & 0.610 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of main factors.  
Figure 7: Difference between apparent sound reduction index for pink or white noise.  
The significance of individual effects of factors is tested by means of a t-test and determination of the p-value. The evaluation of the performed/conducted experiment shows that in the frequency band Frequency I, the current frequency (18.190; factor A) has the greatest effect on the observed response. The excitation interval (8.675; factor B) and the type of noise (5.075; factor C) have a significantly lower effect. In this band, all three main factors have a significant effect on the apparent sound reduction index (see Table 2).  
Factors A and B have a statistically significant effect on the apparent sound reduction index also in the frequency Interval II. Compared to frequency Interval I, the effect of these factors is significantly lower (8.023; factor A, 1.243; factor B). The effect of factor C (noise type) was not confirmed in this interval (see Table 2).  
The effect of factor A on the apparent sound reduction index is also in the third Interval III. In this interval, the effect of the other two factors B and C on apparent sound reduction index was not confirmed (see Table 2).
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 1 Introduction
The SDM consortium has been engaged in various research activities, such as digital recording media, managing related metadata, and exploring media expression and production methods using such data in virtual spaces or by combining virtual and real spaces. In 2016, the SDM consortium launched a new project for use on platforms for managing three-dimensional (3D) video and audio media called the SDM Ontology [17] referred to as the SDM Ontology Version 1. Digital media production can be divided into four stages: recording, editing/processing, distribution, and playback, as shown in Figure 1. In the recording stage of digital media, media data containing video and audio are generated, and at the same time, various metadata about the recording environment and content are generated. SDM Ontology Version 1 defines a vocabulary focused on metadata descriptions during the recording stage. In this study, we propose SDM Ontology Version 2, which can comprehensively describe metadata from the recording, editing/processing and playback phases by organizing and improving the structure of SDM Ontology Version 1. Figure 1 shows the difference in scope between SDM Ontology version 1 and version 2 (Hereafter, when we refer to SDM Ontology, we refer to SDM Ontology Version 2). However, there is no established method of expression or management for the various metadata generated in the recording and editing process, and each digital media creator must manage their metadata. These metadata are generally described in a form unique to each producer or in a format specific to the equipment or software used, making it difficult for third parties to use them. Consequently, most of the digital media that can be distributed in general are limited to data in the form of audio and visual media data after editing has been completed. We developed the SDM Ontology to structurally describe the metadata necessary for a series of digital media production processes using a precisely semantically defined vocabulary. As a result, it can be expected that independently recorded and edited digital media can be referenced and reused, promoting the division of labor in digital media production. New digital content emerges through the participation of various content creators. The remainder of this paper is organized as follows. Section 2 introduces related research, including existing ontologies that provide music-related Resource Description Framework (RDF) vocabularies, 3D audio technology, and existing interactive applications that handle 3D video and audio. Section 3 presents the design policy and structural outline classes of the SDM Ontology proposed herein. Section 4 describes the dataset used in this study and its recording environment. Section 5 describes the design and implementation of Web3602, an interactive application based on the SDM Ontology. Sections 6 and 7 describe the evaluation of the SDM Ontology and Web3602, respectively. Finally, in Section 8, we discuss and conclude the study.
# Collective Attestation for Manageable IoT Environments
## 3 Collective Attestation for Maintenance
### CAMIE Overview  
**Phases.** We define three phases: maintenance, operation, and attestation phases as shown in Figure 1.  
* **Maintenance phase:** The _maintenance phase_ is an offline phase to install/update software and replace devices. Upon initializing a swarm network, the network operator injects a distinguishable tag into every component binary code. It builds a Bloom filter for each component and then joins them into a device tag.
* **Operation phase:** This is the phase for normal operation. In the operation phase, the swarm attestation is prepared in advance. When a new device joins to a swarm, the network operator assigns the device tag to a device and then the device registers its device tag to neighbors. During the operation phase, any authorized verifier requests the swarm attestation.
* **Attestation phase:** Upon the request, the swarm attestation is performed in the _attestation phase_ and reports the attestation results to the verifier. If an erratic (or compromised) device is detected, then the device is marked as a target and its registered tag is merged into the region summary. The region anchor then sends the aggregated tags of targets. CAMIE can be used for target search by adding a target pattern in the joined form of device tags. If a device tag is matched to the target pattern, it is also merged to the region summary. Based on the attestation result, the network operator can decide whether the maintenance phase starts or not. If the maintenance job is required only for specific regions only, then the maintenance can be limited to the part of a swarm while the remaining part keeps working in the operation phase.  
**Component tags and region anchors.** In the swarm attestation, program code itself is protected in the minimal trust anchor of the hybrid attestation, if the binary-embedded _component tag_ can keep the secure binding between a tag and a device, then it be used as a primitive indicator of attestation. First, since a tag is generated from a random number, every tagged component is distinguishable even if it has the same binary code with other components. Second, the device tag is composed by merging (i.e., OR'ing) the Bloom filter of the component binary code. Therefore, in CAMIE, the device tag is the measurement value of the device as well as the fingerprints of the installed software components. The device tag can be used for finding a installed component in the searching process. A _region anchor_ separately attaches the merged device tag of compromised or targeted device up to the point to the attestation result, which is called \"region summary.\" Hence, the region summary can show the regional information to the verifier and suppress the loss of details in the state merging.
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
## 4 Results and Discussion
### Laboratory Test Results
#### 4.2.4 Freeze-Thaw Cycle Test  
Table 12 gives the results of the freeze-thaw cycle test, where \(R_{f}\) represents the UCS after the test, and \(BDR\) represents the antifreeze index. As can be seen, under the same number of freeze-thaw cycles, SNS had a certain degree of improvement in the antifreeze performance for silt compared with CS. With the increase in the number of freeze-thaw  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Stabilizer** & **Time/d** & \(R_{w}\)/MPa & \(R_{c}\)/MPa & \(D_{w}\)/\% & \(K_{w}\) \\ \hline \multirow{4}{*}{SNS} & 1 & & 3.78 & 15.73\% & 0.84 \\  & 3 & 4.45 & 3.65 & 17.98\% & 0.82 \\  & 7 & & 3.86 & 13.26\% & 0.87 \\  & 1 & & 2.17 & 23.59\% & 0.77 \\ \multirow{2}{*}{CS} & 3 & 2.84 & 2.04 & 28.17\% & 0.72 \\  & 7 & & 2.07 & 27.11\% & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Water stability test results.  
Figure 3: Change of split strength with time.  
cycles, the \(BDR\) of SNS-S decreased faster than the \(BDR\) of CS-S, but the \(BDR\) of SNS-S under different number of freeze-thaw cycles was greater than the \(BDR\) of CS-S.  
Combined with the analysis results of freeze-thaw cycle of stabilized soil by [PERSON] [34], [PERSON] [35], and [PERSON] [36], it can be concluded that due to temperature changes, soil particles will be broken in the process of freeze-thaw cycle, the molecular attraction between soil particles will decrease, and the spacing between soil particles will become larger. The water phase in the soil will also change alternately, and the ice crystal structure will increase continuously, resulting in cracks on the soil surface. The C-S-H gel and C-A-S-H gel generated by hydration reaction and pozzolanic reaction in stabilized soil are destroyed with the increase in freeze-thaw times, which ultimately reflects the decrease of the macroscopic mechanical strength of stabilized soil.  
As can be seen from Figure 4a, a few cracks appeared on the surface of SNS-S, and soil particles began to peel off on the surface. As can be seen from Figure 4b, with the increase in the number of freeze-thaw cycles, cracks on the surface of SNS-S increased, and a large number of soil blocks fell off or even formed gaps. This can also better explain the reason why the compressive strength of SNS-S significantly decreased when the number of freeze-thaw cycles of stabilized soil increased from a macro perspective. Due to the continuous freeze-thaw cycles of free water in the gaps, the gaps between the soil were also expanding and decreasing, leading to the formation of penetrating cracks between the gaps, thus leading to the phenomenon of peeling and falling blocks on the surface of the soil.