esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Final Remarks on Security Concerns  
A detailed summary of the main problems identified in the two implementations of the API interfaces is provided in Figure 5.  
Figure 8: OAI vs. Open5 CS vulnerability assessment comparison.  
To conclude the paper we would like to provide a high-level view of the security issues, highlighting also architectural aspects of the implementations:  
* Requests to the NRF can be sent by any machine that _can reach_ it, i.e., there is no **authentication token** check to verify identity, and thus authorization, to access the network and services. Relying on perimeter security (firewalls and traffic closures) to secure an NF is a weak approach; instead, we must assume a zero-trust design, assuming that anyone can contact the NFs. A DELETE NF Instance by any NF within the core would result in a DoS attack.
* **Traffic not encrypted in SBI:** in the core 5G, another high-impact issue is the interception of SBA traffic. In both Open5 GS and OAI, traffic in the SBI is not encrypted, exposing the core to replay attacks, API injections, and other attacks.
* **No rate-limiting** on requests to the AMF or NRF: in fact, all requests are answered. This means that no security mechanism is in place to mitigate a DDoS attack, like a SecGW might be.
* No control mechanism (**filtering**) on user-set values: reliance is placed on client-side filtering when control should also be carried out server-side.
* **Replay attacks on N1/N2 interface** successfully executed: no security mechanism (sequence number) inserted to counter this attack.  
Finally, it is worth mentioning that the implementation of the considered frameworks is still evolving and some features might be included only temporarily. However, the analysis methodology presented in the paper can be applied with minor changes to any new release and will permit tracking the evolution of open-source software for the 5G core network.  
Conceptualization, F.D., R.G.G. and M.P.; investigation, F.D.; resources, F.D.; writing--original draft preparation, R.G.G. and M.P.; writing--review and editing, R.G.G. and M.P.; supervision, R.G.G. and M.P.; funding acquisition, R.G.G. and M.P. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Italian Ministry of Education and Research (MIUR) in the framework of the FoReLab project (Departments of Excellence) and by the University of Pisa in the framework of the PRA_2022_64 \"hOlsistic Sustainable Management of distributed softWARE systems (OSMWARE)\" project.  
The GitHub repository [6] contains traffic captures, scripts, and results of the tests described in this paper.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Experimental Validation and Results Analysis
### Experimental Setup  
The structural block diagrams of the over-the-air (OTA) measurement equipment and the actual laboratory equipment photo are presented in Figures 7 and 8, respectively. The 5G new radio (NR) intermediate frequency (IF) signal with a bandwidth of 100 MHz centered at 3 GHz is generated from a vector signal generator (VSG)(R&S SMBV100B). The Agilent E3247C outputs an unmodulated signal of 12.5 GHz, which is multiplied to 25 GHz by a MITEQ-MAX2M200400 frequency multiplier, and then sent to a power divider as a local oscillator (LO) signal for the up-converter (KTX321840) and down-converter (KRX321840). The 28 GHz bandpass filtered signal is linearly amplified by a pre-amplifier (Ducommun APH-26063325) away from its saturation region to drive the APA into its compression state. The APA device is based on the Amotech AAiPK428 GC-A0404, which consists of four Anokiwave AWMF-0158 transceivers and incorporates 16 branches attenuators, phase shifters, and PAs in a 4-by-4 APA, as well as 16 patch antennas. The main beam signal is captured by an observation horn antenna. After down-conversion to IF, it is collected and converted to a baseband signal by an R&S FSW spectrum analyzer. Lastly, the baseband signal is processed by MATLAB.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 4 Results  
Table 4 gives the coefficients fitted for Equation (1), for each band and sensor combination. The coefficients were fitted using Ordinary Least Squares regression, and as such are specifically for estimating Landsat reflectance from Sentinel-2A reflectance. They should not be inverted to use in the reverse sense.  
Figure 1: Landsat scenes used to create the comparison datasets. Path numbers are in the range of 89–108, and rows in the range 68–87.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Training** & **Validation** \\ \hline Landsat-7 & 625,291 & 312,361 \\ Landsat-8 & 610,449 & 304,456 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of points (single 30 m pixels) used for training and validation, for Landsat-7 and Landsat-8 comparisons.
# Monitoring Students at the University: Design and Application of a Moodle Plugin
## 2 Materials and Methods
### Statistical Analysis  
A normality analysis of the distributions was performed using asymmetry and kurtosis statistics. The data distributions were also tested with Analysis of Variance (-ANOVA-) fixed-effect factor (type of degree) parametric statistical tests. In addition, both supervised (Multiple Regression Analysis (MRA)), Decision Tree (DT), and unsupervised (k-means clustering) Machine Learning techniques were used.
## 2 Acquisition, Pointing, and Tracking in Inter-Satellite Laser Links
### Acquisition Schemes  
The primary approaches to complete the acquisition phase are stare/scan, stare/stare, scan/scan, and scan/stare [19; 34; 43].
# Fine-Tuning Large Language Models for Kazakh Text Simplification
## 1 Introduction
KazSim remained stable under both prompt variants, confirming its robustness and suitability for multilingual deployment. To complement automatic evaluation, we conducted a human study assessing the fluency, meaning preservation, and simplicity of simplified Kazakh texts. We selected two sets of sentence pairs: one from our KazSim model (LLaMA-3.3-70B) and another from GPT-4o-mini. Three native Kazakh speakers rated each pair on a 5-point Likert scale. KazSim achieved high scores across all dimensions, especially in simplicity (4.42), indicating its effectiveness in producing natural, accurate, and easy-to-understand simplifications. * First parallel corpus for Kazakh text simplification: We introduce the first dataset comprising 8709 complex-simple Kazakh sentence pairs, automatically constructed using a heuristic-based complexity detection pipeline (validated on 400 manually annotated sentences) and augmented with LLM-generated simplifications. * KazSim model and baseline: We propose KazSim, a fine-tuned, instruction-following simplification model built on multilingual LLM backbones (e.g., Llama-3.3, Qwen2). KazSim serves as the first benchmarked simplification model specifically optimized for the Kazakh language. * Extensive model evaluation: We provide a comprehensive evaluation of KazSim against standard Seq2 Seq architectures, domain-specific Kazakh LLMs, and zero-shot instruction-following models, using both automatically generated and semi-manuallycreated benchmarks. Evaluation metrics include BLEU, SARI, ROUGE, BERTScore, and human evaluations from native Kazakh speakers. * Analysis of prompt-language impact: We systematically compare the effect of English versus Kazakh instructions on simplification performance, demonstrating the advantages of native-language prompts and providing practical insights for multilingual model deployment. * Kazakh-specific error analysis: Through detailed human evaluation, we identify and characterize the main error categories (tone change, tense change, semantic drift), reflecting linguistic features unique to Kazakh, such as agglutinative morphology and flexible syntax.
## 3 Results  
Forty images were tested on an AMD Athlon 1.25 GHz PC using the MATLAB platform. Each image took approximately 18 minutes for FCM clustering and another three minutes for morphological reconstruction. The result from the coarse segmentation was used as input to the fine segmentation using morphological reconstruction.  
After fine segmentation, most of the classified exudate regions are true exudate pixels, which gives a smaller true positive value; however, it also reduces the false positive value because misclassification of non-exudate pixels is also lower. Figure 6 displays the comparison of exudate detection from the first cluster resulting from coarse segmentation and result of FCM clustering followed by morphological reconstruction and a ground-truth image. Three examples of exudates detection result of FCM clustering followed by morphological reconstruction are shown in Figure 7.  
The performance of our technique was evaluated quantitatively by comparing the result of extractions with ophthalmologists' hand-drawn ground-truth images. Ten examples of detailed results of performance measurement using FCM clustering followed by morphological reconstruction are displayed in Table 1. The sensitivity, specificity, PPV, PLR and accuracy of validation results are shown in Table 2.  
Figure 6: Comparison of exudates detection. (A) Coarse segmentation using FCM clustering. (B) Fine segmentation using morphological reconstruction (C) Ground truth image.  
Figure 7: Exudates detection on low contrast images (A), (C) and (E) are original images, (B), (D) and (F) are detected exudates superimposed on original images of (A), (C) and (E) respectively.  
From experimental results, if FCM clustering is the only technique used; it gives a high true positive value but also a high false positive value. However, the PPV and PLR values are low. Using FCM clustering followed by morphological reconstruction, we have higher accuracy with a lower false positive value. Comparing with baseline algorithm, the results indicate that the FCM clustering followed by morphological performs better in PPV, PLR, accuracy than nearest neighbor.
## 4 Main Results
### Closed-Loop Stability Analysis  
The stability of the closed-loop system with the proposed decentralized control scheme and the IADRC is considered in the following theorem. Before proceeding with the stability analysis of the closed-loop system, the following two assumptions were required. **Assumption 1**. : _There is a stable NHOESO given, as in Equation (19), which produces the estimated states \(\dot{\xi}_{i,l}\) and \(i\in\{1,..,\ p\}\), \(l\in\{1,\ldots,\ \gamma_{i}\}\) and the estimated generalized disturbance \(\dot{\xi}_{i,\ \gamma_{i}+1}\). These estimated states and the estimated generalized disturbance approach respectively as \(t\rightarrow\infty\) to the states \(\xi_{i,\ l}\), \(l\in\{1,..,\ p\}\) and \(l\in\{1,\ldots,\ \gamma_{i}\}\) and a generalized disturbance \(F_{i}^{\prime}\) of the MIMO uncertain nonlinear system, expressed in Equation (10), i.e., with \(i\in\{1,2,\ldots,p\}\) and \(l\in\{1,\ldots,\ \gamma_{i}\}\)._  
\[\lim_{l\to\infty}\bigl{|}\xi_{i,l}-\hat{\xi}_{i,l}\bigr{|}=0,\ i\in\{1,2,\ldots,p\}, \tag{22}\]  
\[\lim_{l\to\infty}\bigl{|}F_{i}^{\prime}-\hat{\xi}_{i,\gamma_{i}+1}\bigr{|}=0 \tag{23}\]  
**Assumption 2. **_There is a convergent TD given as in Equation (17), which generates a profile \(r_{i,l}\), \(i\in\{1,2,\ldots,p\}\), and \(l\in\{1,\ldots,\ \gamma_{i}\}\) for the reference signal with minimum MSE. These trajectories \(r_{i,l}\) approach the reference trajectory \(r_{i}^{(l-1)}\) for \(l\in\{1,\ldots,\ \gamma_{i}\}\) as \(t\to\infty\), i.e.,_  
\[\lim_{l\to\infty}\Bigl{|}r_{i}^{(l-1)}-r_{i,l}\Bigr{|}=0,\ i\in\{1,\ldots,\ p\},\ \ l\in\{1,2,\ldots,\gamma_{i}\} \tag{24}\]  
**Theorem 1. Closed-Loop Stability. **_Given the MIMO nonlinear uncertain system of Equation (10), which has a linearization control law (LCL) \(u_{i}^{*}\) of the form,_  
\[u_{i}=v_{i}-\frac{\hat{\xi}_{i,\gamma_{i}+1}}{b_{i,i}}\,\ i\in\{1,..,\ p\} \tag{25}\]  
_where \(v_{i}\) is given as:_  
\[v_{i}=\mathbf{\kappa}_{i,1}(\overline{\epsilon}_{i,1})\overline{\epsilon}_{i,1}+ \ldots+\mathbf{\kappa}_{i,j}\Bigl{(}\overline{\epsilon}_{i,l}\Bigr{)}\overline{ \epsilon}_{i,l}+\ldots+\mathbf{\kappa}_{i,j,\gamma_{i}}\Bigl{(}\overline{ \epsilon}_{i,\gamma_{i}}\Bigr{)}\overline{\epsilon}_{i,\gamma_{i}} \tag{26}\]  
_with \(i\in\{1,..,\ p\}\) and \(l\in\{1,2,\ldots,\gamma_{i}\}\), where \(\mathbf{\kappa}_{i,1}:\mathbb{R}\to\mathbb{R}^{+}\) is a even nonlinear gain function and \(\overline{\epsilon}_{i,1}=\tau_{i,1}-\hat{\xi}_{i,1}\) is the closed-loop tracking error \(,i\in\{1,..,\ p\}\) and \(l\in\{1,2,\ldots,\gamma_{i}\}\). If Assumptions 1 and 2 hold, then the closed-loop system is asymptotically stable, i.e., \(\lim_{l\to\infty}\bigl{|}\overline{\epsilon}_{i,1}\bigr{|}=0,\ i\in\{1,..,\ p\}\) and \(l\in\{1,2,\ldots,\gamma_{i}\}\)._  
**Proof.
## 3 Background of SDN
### Control Plane  
In SDN, the control plane acts as the brain that performs a set of actions; for example, it applies flow rules to handle the received ethernet frames that decide the traffic destination ports. SDN controller program network resources, control communication between applications and forwarding devices. SDN controller translates application plane requirements into policies and distributes these custom policies into forwarding devices. Control planefunctionalities include network topology storage, configuration of devices, notification of state information, and shortest path routing. Some of the SDN controller architectures proposed in literature are NOX [46], Floodlight [47], POX [48], OpenDayLight [49], Ryu [50], and Beacon [51]. SDN controller has three interfaces for communication: southbound, eastbound westbound. Control data plane interfaces (CDPIs) are interfaces between the data and control plane. CDPIs are also called SBIs, used for forwarding devices to exchange control policies and network state information with the control plane of an SDN controller. SBIs allow programmatic-based control of all device capabilities such as event notifications, advertisements, statistic reports, and forwarding operations. NBIs are exploited by applications to get an abstract view of the network to facilitate automation, analyze specific network behaviors, and analyze network requirements. Eastbound interfaces (EBIs) and westbound interfaces (WBIs) are used in the multi-controller SDN solutions. In multi-controller SDN networks, the exchange of information is important between controllers to provide a global network view to the applications. Examples of distributed control architectures are HyperFlow [52] and Onix [53]. EBIs and WBIs are private and cannot communicate with each other. SDN communication-interfaces [54], distributed-control plane (CIDC) [55], and east-west bridge [56] are some proposals for communication between different SDN controllers.
## 2 Materials and Methods  
### Data Collection  
The data of this study consisted of the online and offline farm data collected from a commercial dairy farm in South Korea. Offline farm data pertains to the herd records, such as the animal type (heifer, pregnant, calving, lactating, dry-off), breed type (Holstein-Friesian, Jersey, Guernsey), breeding system (straightbred, crossbred), parity (1-7), and lactation stage (early, mid, late). Online farm data pertains to the records that are measured daily, transferred to the server of the farm, and collected for processing using the proposed data-mining hub. These online farm data include the environmental conditions measured by the meteorological equipment fitted in the farm, the physiological conditions of the dairy cows as measured by the sensors equipped in the farm, the behavioral data of the dairy cows as measured by sensors or surveillance video cameras, the milk yield and composition records as analyzed by a commercial milking laboratory connected to the farm, and lastly the feed information, including the composition of the ratios, feed intake of the cow per day, and the cost per kilogram of the ration as measured by the automatic feeding system in the farm. The set of equipment used by the farm in measuring parameters included the following: body weight measured by walk-over weighing system; body condition score (BCS) of the cows scored manually by farm staff; body temperature measured by thermosensors; rumen temperature, rumen pH, and rumination measured by rumen boluses; respiration rates measured by heart rate monitors, standing, lying, sitting, and drinking; behaviors of the cows measured by pedometers and surveillance video cameras. All of the unprocessed data collected on the farm were made available by the dairy farmer and provided to the research group for the perusal of this study.
# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
## 5 Experiment
### Comparative Experiment  
#### 5.2.1 Baselines  
To verify the effect of MFHE on HIN information extraction and aggregation, we use the following algorithms to compare with our algorithm.  
DeepWalk [6]: Deepwalk is the first algorithm that applies the learning idea in NLP to network embedding. It samples nodes in the graph by random walk and obtains a sufficient number of node access sequences, and then performs vector learning via the Skip-Gram model. Note that the heterogeneity of HIN is ignored when using DeepWalk.  
HERec [26]: HERec generates a node sequence based on the meta-path random walk, which is represented by learning nodes with fusion function, and then integrated into the matrix decomposition model. In this paper, HERec experiments are carried out on all meta-paths.  
GraphSAGE [37]: GraphSAGE optimizes the sampling of the whole graph to the sampling of the current neighbor nodes. At the same time, it exploits the node feature information and structure information to learn the embedding based on changes in the neighbor relationship of nodes. In this paper, we transform HIN into multiple homogeneous information networks based meta-paths for GraphSAGE experiments.  
GAT [38]: GAT classifies the nodes of the homogeneous information network based on the self-attention mechanism and convolution network. Each node in the graph can assign different attention values according to the feature of neighbor nodes. In this paper, we transform HIN into multiple homogeneous information networks-based meta-paths for GAT experiments.  
Metapath2 Vec [40]: Metapath2 Vec constructs a heterogeneous neighborhood of each vertex based on random walks of meta-paths, and then completes the embedding of the vertices using the Skip-Gram model. Note that Metapath2 Vec can only walk based on a single meta-path, and cannot fuse the semantic information of multiple meta-paths. In this paper, Metapath2 Vec experiments are performed on all meta-paths.
## 6 Conclusions  
1. Through field investigation, it was shown that the dynamic phenomenon of mine pressure is obvious and there is a high static load in the 8204-2 island working face of an extra-thick coal seam with a hard roof. In the first widened coal pillar area, the coal burst phenomenon mainly occurred in the range of 8.2 m-33.5 m during roadway excavation. In the second widened coal pillar area, the phenomenon of coal burst was more frequent during roadway excavation. In different regions and ranges, there were noticeable variations in the occurrence of coal burst phenomena and strong coal bursts. The high static load on the island working face is influenced by the specific characteristics of each region.
2. The stress distribution of the island working face was determined to be primarily influenced by five elements through theoretical analysis and mechanical modeling: coal seam thickness, coal pillar width, gob length, buried depth, and working face length. The stress distribution characteristics of the entire island working face, the evolution of the coal pillar's plastic zone, and the fitting equation for the entity coal's peak stress were obtained by means of numerical simulation, which also analyzed the stress distribution state of the island working face under various conditions of the five influencing factors.
3. Preventing and controlling rock bursts poses a significant challenge to coal mine safety. Currently, the primary approach to controlling rock bursts is through defensive measures for underground hazard relief. The primary cause of dynamic disasters is the concentration of stress in the coal body and the disruptive effects of a hard roof breaking. The technical means of fracturing the hard roof on the ground have a positive effect by changing the geological dynamic environment and can prevent and control the dynamic disaster from the source. Applying ground fracturing technology, the hard rock strata are weakened and modified, so that the concentrated stress and energy transfer are released to reduce the risk of rock bursts from the source, the impact mine is changed into no impact, and then the underground mining activities can be safely carried out.  
X.D., methodology, software, data curation, and writing--original draft; R.G., methodology, formal analysis, investigation, writing--review and editing; W.G., investigation, data curation; D.B., investigation; X.H., investigation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 52274135), Key Research and Development Program of Shanxi Province (Grant No. 202202090301011), Young Elite Scientists Sponsorship Program by the Chinese Association for Science and Technology (Grant No. YESS20220245), and Excellent Youth Training Program of Shanxi Province (Grant No. 202203021224006).  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.