esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 5 Backward Pitch Predictor
### Backward Pitch Estimation  
The pitch period \(d_{k}\) at time instant \(k\) is estimated from previous reconstructed signal \(e\). The autocorrelation function \(R_{ee}\) of \(e\) is calculated every 240 samples. In addition, when the previous pitch period increases one or decreases one and the shifted coefficients are not stable, the pitch period and pitch coefficients are initialized again by calculating the autocorrelation function \(R_{ee}\). The pitch period is initialized by finding the peak of \(R_{\alpha e}\) between \(d_{min}=20\) and \(d_{max}=125\), and then is recursively updated. The estimate of the autocorrelation function, \(\hat{\rho}(m)\), is obtained from the following recursions [3; 28]:  
\[\hat{\sigma}_{e}^{2}(k)=\delta\hat{\sigma}_{e}^{2}(k-1)+(1-\delta)(e(k))^{2} \tag{10}\]  
\[\hat{\rho}^{(k)}(m)=\delta\hat{\rho}^{(k-1)}(m)+(1-\delta)\frac{e(k)e(k-m)}{ \hat{\sigma}_{e}^{2}(k)}, \tag{11}\]  
where \(\delta=0.95\). The pitch period \(d_{k}\) is updated by:  
\[d_{k+1}=\left\{\begin{array}{ll}d_{k}+1&\text{if }\hat{\rho}^{(k)}(d_{k}+1)= \max\left(\hat{\rho}^{(k)}(d_{k}+1),\hat{\rho}^{(k)}(d_{k}),\hat{\rho}^{(k)}( d_{k}-1),\hat{\rho}_{\min}\right)\\ d_{k}-1&\text{if }\hat{\rho}^{(k)}(d_{k}-1)=\max\left(\hat{\rho}^{(k)}(d_{k}+1),\hat{ \rho}^{(k)}(d_{k}),\hat{\rho}^{(k)}(d_{k}-1),\hat{\rho}_{\min}\right)\\ d_{k}&\text{otherwise}\end{array}\right. \tag{12}\]
# Revisiting the Detection of Lateral Movement through Sysmon
## 6 Methodology
### Credential Exploitation Attacks
#### 6.2.3. Golden/Silver Ticket  
As it concerns the GT attack, Mimikatz supports its execution toward the successful creation of a _\"golden\"_ Kerberos ticket. For the needs of this work, and with reference to Figure 1 and Table 2, the _SYSMON_SET_ domain's _\"chr.smilio\"_ account of Client STA 3 was leveraged and exploited. As mentioned in Sections 5.4 and 5.5, this set of attacks is a combination of the already presented PtH and PtT attacks. For the creation of a GT to be successful, the exploitation of an extracted NTLM _\"Krbtgt\"_ hash is a requirement, along with the domain's _Name_, the _SID_, and the _Username_ of the targeted host. These four elements are combined with Mimikatz to successfully create the _\"golden ticket\"_ (_\"kerberos:golden /user:krbtgt /domain:SYSMON_SET /sid:S-1-5-21-902028059-194221605-2102066478 /krbtgt:d125e4f69c851529045 ec95 ac80 fa37e/ticket: krbtgt.tck /ptt\"_) and allow the adversary to move laterally. All the submitted tickets after the execution of this attack were retrieved with the _\"kerberos:list\"_ command. After the generation of the NTLM Hash for the _\"Krbtgt\"_ account, Kerberos will perceive this TGT as trusted, enhancing its owner with administrative privileges and unlimited access to network facilities related to Kerberos authentication. It should be noted that the same procedure was followed during the creation of a forged TGS ticket and the exploitation of the related service. The only difference is in the use of the _PtT_ parameters implemented during the ticket's production process to indicate the use of the _PtT_ technique to produce the Silver ticket (_\"kerberos:golden /domain:SYSMON_SET /sid:S-1-5-21-902028059-194221605-2102066478 /target:WIN-J23 NIGPIQ6_sysmom_set.lobal /service:cifs /rc4:1d86942 ba7/284a38c97b63025f6&ccb8 /user: Administrator /ptt\"_).
## 7 Experiment and Analysis  
To evaluate the comprehensive performance of the NexoNet, this study utilized a computer equipped with a 12 th Gen Intel(r) Core(tm) i5-12400 2.50 GHz processor, 16 GB DDR4 memory, 1 TB SSD, manufactured by Dell Technologies Inc., located in Round Rock, TX, USA, and running Windows 11 Professional edition. The system was constructed and subjected to simulation experiments using the Golang programming language. The experiments focused on analyzing both the multiple incentive mechanism and the PoAP consensus mechanism.  
Parameters used in the experiments are detailed in Table 2. Within the parameter configuration, the \(PV\) calculation parameter \(\alpha_{gcp}\), representing the maximum \(PV\) attainable from a single content publication, was set at 10.0. The PV calculation parameter \(\alpha_{pp}\) for purchasing content, which is multiplied by the expenditure amount to derive the purchase \(PV\), was set at 1.0. The parameter for reward amounts \(\alpha_{reward}\) was set at 2.0, and the rating calculation coefficient \(\alpha_{rating}\) at 0.5, with the rating user number threshold \(rating_{threshold}\) designated at 40: only ratings from a user count exceeding this threshold are deemed valid. Evaluations of content quality are conducted each period, with the period \(T_{evaluation}\) defined as 30 block heights. Parameters \(\omega_{1}\) and \(\omega_{2}\), related to the content publication interval, were set at 1.0, necessitating a 3 s wait before publishing subsequent content to ensure the \(PV\) remains unaffected, with the same principle applying to purchase activities.  
Within the PoAP consensus mechanism, the probability \(p\) for cryptographic sortition from the total votes was set to 0.2, with the voting period \(T_{election}\) defined as 10 block heights. The parameters for the distribution of block transaction fee rewards, \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\), were established at 0.80, 0.15, and 0.05, respectively. The majority of the block  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\alpha_{gcp},\alpha_{pp}\) & 10.0, 1.0 & \(\omega_{1},\omega_{2}\) & 1.0, 1.0 \\ \(\alpha_{share},\alpha_{rating}\) & 2.0, 0.5 & \(\gamma_{1}\)-\(\gamma_{2},\gamma_{3}\) & 0.80, 0.15, 0.05 \\ \(T_{epoch}\) & 30.0 & \(T_{election}\) & 10.0 \\ \(rating_{threshold}\) & 40.0 & \(p\) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameter setting.  
system incentive, accounting for 0.80 of the total, is enjoyed by users who contribute to the NexoNet. Committee members are allocated 0.15 of the total block rewards based on their weight, and the block proposer are entitled to 0.05 of the overall rewards.
# Experimental Study on Femtosecond Laser Processing Performance of Single-Crystal Silicon Carbide
## 3 Results and Discussions
### Experimental Results
#### 3.2.1 Analysis of Single Factor Experimental Results
When NA = 0.6, as the fluence increases, due to the width of the groove is smaller, the recast material accumulates and blocks in the groove and the groove depth first increases and then remains unchanged. As shown in Figure 8d, an increase in numerical aperture means a decrease in the size of the laser spot and a decrease in the cross-sectional area of the groove, resulting in a decrease in MRR.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 3 Connectivity Models for Vehicular Communications
### Strongly Connected (STC) Route Selection  
The purpose of our proposed connectivity-based routing scheme is to select a strongly connected (also referred as STC) route for data transmission among vehicles. STC is a set of intermediate links that have high probability of connectivity. Multiple routes may exist in a vehicular network to pass data from the source to destination. Suppose a route \(Z\) from the source to the destination \(d\), where the source first transmits data to the cluster head \(CH\) and then \(CH\) transmits the data to the destination through \(k\) hops. If \(\{V_{1},V_{2},\ldots V_{k-1}\}\) denote the vehicles participating in data transmission from \(CH\) to \(d\), then the link of the route can be denoted by \(l_{1}=(s,CH),l_{2}=(CH,V_{1}),\,\ldots,\,l_{k+1}=(V_{k},d)\) with different connectivities. Moreover, the connectivity of a particular link, i.e., \(cl_{i}\), where \(i=\{1,2,\,\ldots,\,k+1\}\), can be calculated using Equation (4). The connectivity of a route \(Z\) can be calculated by the following:  
\[c_{Z_{t}}=\prod_{i=1}^{k+1}cl_{i} \tag{5}\]  
where \(cz\) represents the connectivity of path \(Z\) between source \(s\) and destination \(d\) at time \(t\). In a nutshell, Equation (5) is the product of all intermediate connected links \(l_{c}\) between the source and destination on path \(Z\). If \(\mathcal{Z}=\{Z_{1},Z_{2},\,\ldots,\,Z_{n}\}\) is the set of \(n\) alternate routes from \(s\) to \(d\), STC at time \(t\) can be calculated as below:  
\[Z_{STC_{t}}=\arg\max\{c_{Z_{j}}\},\quad j=1,2,\,\ldots,\,n. \tag{6}\]  
Here, Equation (6) returns the strongest connected route from the source to destination.
## 3 Methodology
### Mitigation Strategies  
The configuration and commissioning of OT networks can incorporate a variety of security strategies to help mitigate failures in specific devices. In this study, we leveraged the built-in security features of the gateway device and introduced additional security components, such as a security gateway that acted as a firewall for both Modbus and DNP3 networks, as well as a software-defined switch to control the physical routing between devices. Each device was evaluated individually to assess its contributions to failure mitigation within the OT network.  
For example, the communication gateway includes several security features aimed at mitigating failures. Specifically, we tested an IP whitelist, SYN scan protection, SYN-flood protection, and Denial-of-Service (DoS) prevention. The security gateway functions as a physical firewall between networks. Since both OT networks utilize TCP-based protocols, we implemented two separate firewall filters: one allowing all TCP traffic and another permitting traffic only within a specified port range for current TCP connections.  
The final security appliance tested was a software-defined switch, which replaces a standard managed or unmanaged switch more commonly found in traditional OT networks. For this, we utilized an OpenFlow-configurable switch designed for OT network configurations, which is capable of enforcing physical routing restrictions. For testing, each switch was configured to allow communication exclusively between the gateway and its respective OT devices on each bus, namely the DNP3 outstation and the PLC.  
Defense-in-depth for OT networks is a strategy that layers multiple security measures to protect from cyberattacks against multiple avenues. The methodology is built around the assumption that no single security feature is foolproof and combines safeguards across different levels. In this paper, we consider on-device security features, external firewalls, intrusion detection systems (IDSs), and software-defined networking for security across multiple layers. We consider and describe these in more detail in the following sections.  
Our mitigation strategy evaluation emphasizes both technical feasibility and deployment practicality. We tested configurations such as static route enforcement using software-defined networking (SDN), real-time packet filtering via external firewalls, and protocol-specific security features like SYN-flood prevention. These were assessed under variable traffic loads to understand their behavior under stress.
# A Generalized Spatial Modulation System Using Massive MIMO Space Time Coding Antenna Grouping
###### Abstract  
Massive multiple input multiple output (MIMO), also known as a very large-scale MIMO, is an emerging technology in wireless communications that increases capacity compared to MIMO systems. The massive MIMO communication technique is currently forming a major part of ongoing research. The main issue for massive MIMO improvements depends on the number of transmitting antennas to increase the data rate and minimize bit error rate (BER). To enhance the data rate and BER, new coding and modulation techniques are required. In this paper, a generalized spatial modulation (GSM) with antenna grouping space time coding technique (STC) is proposed. The proposed GSM-STC technique is based on space time coding of two successive GSM-modulated data symbols on two subgroups of antennas to improve data rate and to minimize BER. Moreover, the proposed GSM-STC system can offer spatial diversity gains and can also increase the reliability of the wireless channel by providing replicas of the received signal. The simulation results show that GSM-STC achieves better performance compared to conventional GSM techniques in terms of data rate and BER, leading to good potential for massive MIMO by using subgroups of antennas.  
space time coding; spatial modulation; generalized spatial modulation; multiple input multiple output +
Footnote †: journal:  
Article
# An Interpretable XGBoost Framework for Predicting Oxide Glass Density
###### Abstract  
Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can create accurate and interpretable models that reveal underlying physical principles. Using a dataset of 76,593 oxide glasses from the SciGlass database, three machine learning (ML) models (ElasticNet, XGBoost, MLP) were trained and evaluated. Four distinct feature sets were constructed with increasing physical complexity, ranging from simple elemental composition to the advanced Magpie descriptors. The best model was further analyzed for interpretability using feature importance and SHapley Additive exPlanations (SHAP) analysis. A clear hierarchical improvement in predictive accuracy was observed with increasing feature sophistication across all models. The XGBoost model combined with the Magpie feature set provided the best performance, achieving a coefficient of determination (R\({}^{2}\)) of 0.97. Interpretability analysis revealed that the model's predictions were overwhelmingly driven by physical attributes, with mean atomic weight being the most influential predictor. The model learns to approximate the fundamental density equation using mean atomic weight as a proxy for molar mass and electronic structure features to estimate molar volume. This demonstrates that a data-driven approach can function as a scientifically valid and interpretable tool, accelerating the discovery of new materials.  
machine learning; glass density; oxide glass; feature engineering; interpretable AI; materials informatics 2025
# Dynamic Network Resource Autonomy Management and Task Scheduling Method
## 3 Dynamic Resource Management Architecture and Task Scheduling Method  
To address the problems of efficient utilization of satellite network resources, diversification of satellite network task demands, and management and scheduling of satellite network resources, we first design the management architecture of dynamic resources to realize dynamic allocation and unified distribution of resources. Then we develop the scheduling algorithm based on this architecture and finally learn the efficient utilization of resources and improve the high timeliness of scheduling.  
As shown in Figure 1, this is the overall framework diagram of our study.  
Figure 1: Overall framework diagram.
## 3 Results and Discussions
### Comparison of the Differently Pretreated Lignocellasic Biomass in the AD Process  
When various lignocellulosicic feedstocks are subjected to different pretreatments, they react uniquely. In addition, the inherited chemical composition and properties of the biomasses further affect the AD process. Table 4 compares the semi-continuous AD of different types of pretreated lignocellulosic feedstocks [26; 27; 34; 38; 39; 58; 65; 66]. During the alkaline pretreatment, it was observed that the methane production significantly increased because of the delignification properties of the alkaline pretreatment [34; 58]. Nevertheless, alkaline pretreatment has a higher cost and a negative environmental impact due to the chemical usage. However, as mentioned in this study, SE pretreatment is cost-effective and environmentally friendly. When the AD of the steam-exploded olive mill (Lignin = 20.3%TS) was performed (200 \({}^{\circ}\)C for 5 min) [27], a nominal methane yield was observed. However, it was concluded that increasing the OLR to 2 g-VS L\({}^{-1}\)d\({}^{-1}\) for days 175-275 led to a meagre methane yield (85 mL g-VS\({}^{-1}\)). When using _L grandiflora_ in this study (Lignin = 25.22 \(\pm\) 4.6%TS), the average methane yield was 133.2 mL g-VS\({}^{-1}\). Batch methane potential tests with _L. grandiflora_ on the same SE condition were performed [7], and the methane yield was approximately 180 mL g-VS\({}^{-1}\). Despite the higher lignin and woody structure of _L. grandiflora_, the methane yield was enhanced by the SE pretreatment (180 \({}^{\circ}\)C, 30 min). For the lignocellulosic waste, such as aquatic weed, SE seemed to be an effective and efficient pretreatment when applied to semi-continuous AD.
## 2 Methods
### Data Analysis  
All statistical analyses were conducted using IBM Statistical Package for the Social Sciences, version 25.0 (IBM-SPSS Inc., Chicago, IL, USA). A descriptive analysis was performed, stratified by weight categories, for each variable under study. Frequencies and percentages were used for qualitative variables, while measures of central tendency (mean and standard deviation) were employed for quantitative variables. Normality tests were conducted first to determine the appropriate statistical analysis for each variable. Specifically, the normality of the sample was verified using the Kolmogorov-Smirnov test (with the Lilliefors correction) for variables with more than 50 cases, and the Shapiro-Wilk test for variables with 50 or fewer cases. The mean values of the sequential, temporal, technical, and tactical parameters of boxing matches were compared across the five weight categories and among the different rounds of the match. An ANOVA with one factor was used for variables that met the normality assumption, applying a post hoc Tukey-b test if statistically significant differences were found. For variables that did not meet the normality assumption, the Kruskal-Wallis test was employed as a non-parametric alternative. We calculated the effect size: partial eta squared (\(\eta^{2}\) partial) for ANOVA (small: 0.01, medium: 0.06, large: 0.14) and the coefficient \(r\) for Kruskal-Wallis (small: 0.1, medium: 0.3, large: 0.5). The relationship between qualitative categories and the comparison of these categories across the five weight categories were analyzed using the chi-square test. A significance level of \(p<0.05\) was considered for all statistical tests. To control for Type I error due to the multiple comparisons made, the Benjamini-Hochberg correction was applied. This method was selected for its suitability in exploratory studies with multiple tests, allowing control of the false discovery rate without excessively compromising statistical power.  
Additionally, post hoc power analysis and sample size calculations were conducted using G*Power, version 3.1. These analyses were performed to assess the adequacy of the sample size and to calculate the achieved statistical power for the observed effect sizes. Specifically, the power analysis focused on the Kruskal-Wallis test, given its frequent use in this study. The range of effect sizes observed across variables was used to evaluate the statistical power. The post hoc analysis revealed that the sample size was adequate for detecting moderate to large effect sizes (e.g., \(f=0.271\)), with achieved power levels around 32%. However, for smaller effect sizes (e.g.,\(f=0.181\)), the power was considerably lower, at approximately 19%. This highlights the inherent limitations in detecting subtle differences with the available sample size. Despite these constraints, the decision to analyze only matches from the round of 16, consistent with our previous study, ensured methodological rigor and comparability across studies. This approach provides valuable insights while acknowledging the limitations imposed by the exclusive nature of Olympic-level data.
## 4 Conclusions  
This study focused on optimizing the headspace solid-phase microextraction-gas chromatography-flame photometric method (HS-SPME-GC-FPD) for detecting dimethyl sulfides. The standard curve equations, limit of detection, lower limit of quantification, spiked recoveries of the experimental methods, and optimal extraction conditions were determined for the extraction of DMS, DMDS, and DMTS. The optimal extraction conditions were determined as using 50/30 \(\upmu\)m DVB/CAR/PDMS extraction fiber at 20% ion concentration, 87 min extraction time, and 50 \({}^{\circ}\)C extraction temperature. Linear fits for DMS, DMDS, and DMTS were performed using ln(peak area) as the vertical coordinate and ln(concentration) as the horizontal coordinate. The correlation coefficients of the standardized working curves were 0.9967, 0.9907, and 0.9994, respectively, indicating a good linear relationship. Using this method, detection levels of ng/L were achieved for DMS, DMDS, and DMTS. The limits of detection for DMS, DMDS, and DMTS were 29 ng/L, 1.2 ng/L, and 5.0 ng/L, respectively. The recoveries of the spiked standards for DMS, DMDS, and DMTS were 97.22-99.07%, 93.39-99.34%, and 91.17-99.25%, and the relative standard deviations were 5.18-5.94%, 3.08-6.25%, and 2.56-5.47%, respectively. The headspace solid-phase microextraction-gas chromatography-flame photometric method established in this study is stable and reliable and can be used to analyze volatile sulfides in water collected from freshwater lakes.  
Figure 4: Distribution characteristics of DMSs in the water column of Lake Ulansuhai.  
**Author Contributions:** All authors contributed to the study and manuscript. [PERSON] designed the investigation and research protocol. [PERSON], [PERSON], [PERSON] and [PERSON] conducted the field investigation and sampling. [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] drafted and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financially funded by the National Key Research and Development Program of China (2022 YFC3202703), the Science and Technology Program of Bayan Nur (K202324), National Natural Science Foundation of China (41877488), Science and Technology Achievement Transformation Foundation of Inner Mongolia Autonomous Region (2021 CG0013), Key Projects of Ningbo Public Welfare Category Science and Technology Plan Projects (2022S006), and Key Subjects of Ningbo College of Health Sciences (2020Z06).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in this study are available upon request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
## 5 Experiment  
### Simulation  
#### 5.1.1 Effect of Friendly Jamming  
The CFJ-DMZ model proposed in this paper uses three drones as friendly jammers to protect the confidentiality of D2D communication. At this time, the three drones are theoretically the smallest number to make a two-dimensional space, and they protect the communication of the two legitimate nodes by forming a cost-performance effective secure zone. The jamming effect on the eavesdropper is affected by the distance between the source node and the eavesdropper (Source \(-\) Eve distance, \(\rm{d_{SE}}\)) and the distance between the jamming drones and the eavesdropper (Drone \(-\) Eve distance, \(\rm{d_{DE}}\)). Figure 3 shows the BER of the eavesdropper according to changes in \(\rm{d_{SE}}\) and \(\rm{d_{DE}}\) measured using the simulation. The BER of the eavesdropper was measured by changing each distance from 1 to 100 m. As a result, we found that when \(\rm{d_{DE}}\) is shorter than \(\rm{d_{SE}}\), the BER of the eavesdropper increases, reducing the communication quality of the eavesdropper.