esa-sceva/satcom-chunk-collection huggingface dataset:
# Stabilization of Road Embarkments on Peat Soils Using Oil Shale Ash and Pozzolanic Additives
## 3 Experimental Procedure
### XRD/XRF Measurements  
XRD/XRF tests were performed to determine the phase and elemental composition of the samples.  
XRD was performed on a SmartLabTM diffractometer (Rigaku, Tokyo, Japan), tube power 8.1 kW, using Cu rotating anode working at 45 kV and 180 mA, coordinate sensitive 1D detector D/tex Ultra and Bragg-Brentano optical setup. The diffraction pattern was measured between diffraction angles of 8 and 90\({}^{\circ}\) with a step size of 0.01\({}^{\circ}\) (20) and a scan speed of 4 deg/min. PDXL software (Rigaku) and database PDF-2 (International Centre of Diffraction Data, 2018) were used for phase identification. TOPAS 6 Academic software was used for the analysis of mass concentrations of crystalline and amorphous phases implementing the Rietveld refinement method.  
The initial structural parameters of phases required for the refinements were taken from the database ICSD 2019 (FIZ Karlsruhe, Germany).  
The concentration of the amorphous phase was estimated by a quantitative XRD analysis method using zircon (ZrSiO\({}_{4}\)) as an external standard material for the calculation of calibration constants.  
The quantities of Rwp and Rwp' (given in tables below) were defined as the overall residual error of the Rietveld approximation, i.e., the weighted difference between the  
Figure 6: Preparation of specimens for the oedometer tests.  
theoretically calculated and measured diffraction patterns of the phases found, taking into account the background and excluding the background, correspondingly.  
XRF was performed on a spectrometer (ZSX 400, Rigaku, Tokyo, Japan) using an Rh X-ray tube working at a power of 3 kW.
# Novel Designated Ownership Transfer with Grouping Proof
## 3 Security Analysis
### Backward Secrecy  
Assume an attacker can intercept all forward messages transmitted between all communicating parties after ownership transfer.  
However, in our protocol, new ownership by shared key \(Ky_{i}^{q}\) is computed by the verifier by encrypting the old ownership by shared key \(Ky_{i}^{q^{\prime}}\) and a random number \(Nt_{i}^{q}\) generated by the tags (Figure 3). Therefore, without the new ownership by the shared key, the former owner (supplier) cannot further track messages transmitted between the new owner and the tags.
## 4 Results and Discussion
### Kalman Filtered Forecasts of Solar Radiation
#### 4.3.2 KF_Kt Forecasts
As in the case of MBE, RMSE (Figure 9b) of KF_Kt are consistently much smaller than KF_TS under different sky conditions. Figure 8: A comparison of the MBE (**a**) and RMSE (**b**) of KF_TS (solid gray bar) and KF_Kt (solid white bar) predictions with respect to the raw WRF predictions (solid black bar) for three sky conditions. The marked lines stand for the relative improvement of the KF_TS (red) and KF_Kt (black) against the raw forecasts of the WRF model at the Maizuru site. Overall, although the forecasting errors of solar radiation can be reduced by using both KF_TS and KF_Kt, the impact of the latter is much more apparent for both sites, especially the site of Qinghai. This might indicate that the clearness index-based Kalman filter can overcome the shortcomings to some extent when the Kalman filter resolves the data series with substantial day-to-day changes.
### Definition and Characteristics of Sprouted Grains  
Sprouted grains are whole cereal or pseudocreal grains that have undergone controlled germination under specific conditions of temperature, moisture, and time. This process enhances the nutritional quality and functional properties through enzyme activation and comprehensive metabolic changes [12; 13].  
The sprouting process can be applied to diverse grain types. Commonly sprouted cereals include barley (_Hordeum vulgar)_, oats (_Avena sativa_), brown rice (_Oryza sativa_), wheat (_Triticum aestivum_), and sorghum (_Sorghum bicolor_), each offering distinct nutritional advantages. Nutritionally rich pseudocereals such as quinoa (_Chenopodium quinoa_), amaranth (_Amaranthus_ spp.), and buckwheat (_Fagopyrum esculentum_) are also excellent candidates for sprouting [14]. Additionally, legumes including mung beans (_Vigna radiata_), soybeans (_Glycine max_), and black beans (_Phaseolus vulgaris_) contribute to improved protein quality and enhanced bioactive compound content when sprouted [13].  
Sprouting is a food processing technique that harnesses the early stages of germination--typically up to radiole protrusion--to trigger enzymatic breakdown of macronutrients (e.g., starch, protein) and promote the biosynthesis of bioactive compounds. During germination, three distinct phases can be recognized: Phase I involves rapid water uptake that initiates metabolism; Phase II features limited water uptake but major metabolic activities, including synthesis of hydrolytic enzymes such as \(\alpha\)-amylase, endoxplanase, and phytase; and Phase III is characterized by radiole emergence. In contrast to full germination, sprouting is deliberately halted during Phase II or early Phase III to optimize nutrient enhancement while preserving desirable textural properties [15].  
According to the American Association of Cereal Chemists (AACC), malted or sprouted grains that retain all of their original bran, germ, and endosperm qualify as whole grains, provided that the sprout growth does not exceed the kernel length and nutrient integrity is maintained [12]. In addition to whole sprouted grains, various derivatives such as bran extracts and enzymatic hydrolysates are increasingly used in functional food formulations due to their concentrated bioactive profiles [16; 17].  
Detailed changes in the nutritional profiles and bioactive compounds of sprouted grains are presented in Section 3.
###### Abstract  
Underwater wireless optical communication (UWOC) is a promising solution for Gb/s rate and long-distance underwater communication. However, random changes in the local temperature and salinity of seawater have caused different refractive indices of ocean water. This study investigated the UWOC system in different saline water while simultaneously changing the temperature and water flow. A maximum bit error rate (BER) of 4.851 \(\times\) 10\({}^{-6}\) was measured at \(-\)7.41 dBm in 3 m of 45.56 g/L saline water. By changing the temperature to 30 \({}^{\circ}\)C, the bit error rate (BER) value reached 5.12 \(\times\) 10\({}^{-6}\) in the saline water. On the other hand, water flow was generated in various types of water salinity to compare simultaneous environmental effects in the UWOC system. In 45.56 g/L of saline water with water flow, the UWOC system was still capable of reaching a BER value of 4 \(\times\) 10\({}^{-4}\).  
underwater wireless optical communication; BER; saline water; environmental factors 2023203100383330/photonics1004038330/1004038330/  
dissolved, the salinity is higher, and the water becomes turbid. In our experiment, we measured the water with different types of salinity to compare the impact on the UWOC system. [PERSON] et al. proposed a high-sensitive long-reach UWOC system with an Mbps-scale data rate across a 10 m underwater channel. Their experiment investigated different water types, and the maximum distance that the 2 Mbps signal could be extended up to was 100 m in the first type (c = 0.056 m\({}^{-1}\)) of seawater [15]. [PERSON] et al. proposed a blue gallium nitride (GaN) micro-light-emitting-diode (micro-LED)-based UWOC system at a distance of 2.3 m with a maximum data rate of 790 Mbps. They found that as sea salt, Maalox, and chlorophyll increased, it affected the light attenuation mechanism [16]. Therefore, we measured the UWOC system in various types of salinity by using 155 Mbps, 622 Mbps, and 1.25 Gbps to compare the environmental impact at a 3 m distance.  
In our previous work [2], tap water was used to measure the impact of temperature and turbulence (water flow) issues; we did not study temperature issues on seawater or the various types of salinity experiments. This paper further discusses the impact of saline water on the UWOC system with two simultaneous and changing environmental factors such as temperature (or water flow) and various types of saline water. Furthermore, the transmission channel in saline water could degrade the light beam's power more than in tap water, which could impact the data transmission's quality.
## 4 Methodology
### Hardware Used, System Architecture, Computational Environment, and Training Details  
The computational experiments were primarily conducted on a purpose-built PC (64-bit Ubuntu 22.04.4 LTS PC with Intel(r) Core(tm) i7-8700 CPU and NVIDIA GeForce GTX 1060 6 GB GPU) and in the Google Colaboratory (Colab) environment. This platform provides access to cloud-based graphics processing units (GPUs), specifically NVIDIA GPUs, which were used to accelerate the training and inference processes of deep neural networks.  
The core of the developed solution is built upon a Python-based deep learning framework, employing several established libraries and tools. Data management and preprocessing were handled by NumPy 2.0.2 and Scikit-learn 1.6.1. Additionally, we used Matplotlib 3.10.0 and torchmetrics for utilities and visualization.  
The used loss function was cross-entropy that is usually used in case of classifications. The Adam optimizer was used as optimizer in the training. In the GRU block (see Figure 3), the GRU itself was followed by 3 fully connected layers. The training and test sets were split as usual in a ratio of 75% and 25%.  
Considering the hyperparameters, it is worth noting that the value of lstm_hidden_dimension was 35, the number of lstm_layers was 2, and the value of learning_rate was \(1\times 10^{-3}\).
## 3 Results  
Mean CFU comparison for _E. coli_ with the control for each disinfecting regime and growth time is presented in Table 1. From the results, it was found that all the specimens incubated at 24, 36, 48 and 96 h and exposed to MW-DW-DC and MW-DW disinfection for 1, 2 and 3 min demonstrated significant difference in mean CFU of _E. coli_ compared to the control without disinfection. However, for the MW-DW-DC group, complete disinfection was achieved at 2 min of disinfection at any growth time (_p_-value < 0.05). Conversely, for MW-DW disinfection, CFU was significantly zero at 3 min when compared with control at any growth time (_p_-value \(\leq\) 0.05). For DC-DW specimens, CFU levels were approximately zero at five minutes when compared with the control at different growth durations (_p_-value \(\leq\) 0.05) (Table 1).  
Mean CFU comparison for _S. aureus_ among the control for each disinfecting regime and growth times is presented in Table 2. For MW-DW-DC, CFU were significantly zero at 2 min of disinfection when compared with control at any growth time (_p_-value \(\leq\) 0.05). For MW-DW specimens, CFU were significantly zero at 3 min of disinfection when compared with control at each growth time (_p_-value \(\leq\) 0.05). For DC-DW disinfection, CFU were significantly zero at five minutes when compared with control at each growth time (_p_-value \(\leq\) 0.05) (Table 2).  
Mean CFU comparison for _S. mutans_ with the control for each disinfecting regime and growth time is presented in Table 3. For MW-DW-DC, CFU were significantly zero at 2 min when compared with control (_p_-value \(\leq\) 0.05). For MW-DW disinfection, CFU were significantly zero at three mins when compared it with control at each growth time (_p_-value \(\leq\) 0.05). For DC-DW, CFU were significantly zero at 5 min when compared with control at each growth time (_p_-value \(\leq\) 0.05) (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Growth Time \&** & & & \\
**Exposure Time vs. Control** & **Control CFU** & **MW-DC-DW** & **MW + DW** & **DC + DW** \\ \hline
24 h & 2254 & MD (\(p\)-Value) & MD (\(p\)-Value) & MD (\(p\)-Value) \\
1 min & & \(-2166\) * & \(-1251\)* & \(-551\  
+++ ==WARNING: Truncated because of repetitions==
) \\  
++  
SEM analysis of the samples showed positive consortia-harboring cocci and bacilli (Figure 5). Using SEM, live cells of _E. coli_ (rods), _S. aureus_ and _S. mutans_ (cocci) attached to the PMMA specimens were observed. After the disinfection procedure, dead cells were observed attached to the PMMA surface in the form of a small, agglomerated, mass of irregular-shaped cells (Figure 5).
## 3 Results
### Kriging Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **Kriging Method** & **RMSE** & **MAE** & **NRMSE** & **NMAE** \\ \hline \multirow{2}{*}{Temperature (\({}^{\circ}\)C)} & Universal & 1.452 & 1.090 & 0.539 & 0.404 \\  & Ordinary & 1.857 & 1.340 & 0.707 & 0.505 \\ \hline \multirow{2}{*}{Humidity (\%)} & Universal & 7.440 & 5.580 & 0.667 & 0.491 \\  & Ordinary & 7.852 & 5.903 & 0.697 & 0.513 \\ \hline \multirow{2}{*}{Precipitation (mm)} & Universal & 0.201 & 0.072 & 1.096 & 0.358 \\  & Ordinary & 0.201 & 0.072 & 1.095 & 0.355 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Nationwide comparison of universal and ordinary kriging methods based on cross-validation results using multiple error metrics. the implementation switched to using the pseudo-inverse of the kriging matrix. This approach enhanced numerical stability and successfully completed the interpolation despite increased computational costs. Temperature and humidity parameters did not require these fallback mechanisms and were processed using standard kriging methods throughout the interpolation process. Figure 4: Cross-validation performance comparison between universal and ordinary kriging methods across meteorological parameters using nationwide station data. (**a**) Normalized Root Mean Square Error (NRMSE) demonstrating universal kriging’s superior accuracy for temperature and humidity interpolation. (**b**) Normalized Mean Absolute Error (NMAE) confirming universal kriging’s consistent performance advantage across parameters. (**c**) Percentage improvement in NMAE achieved by universal kriging, with substantial gains for temperature (20.0%) and moderate improvements for humidity (4.3%), while precipitation shows minimal difference between methods (0.7%) favoring ordinary kriging. Figure 5: Temperature distribution in mountainous counties of southern Poland using two kriging interpolation methods. (**a**) Ordinary kriging interpolation showing temperature patterns without accounting for elevation effects. (**b**) Universal kriging interpolation with elevation drift, demonstrating how temperature varies with topography. The maps display spatial temperature variation at a specific time across six counties: \(\dot{z}\)ywiecki, suski, nowotarski, tatrzanski, limanowski, and myslenicki. Universal kriging reveals a strong temperature–elevation relationship, with colder temperatures at higher elevations, and exhibits better alignment with station measurements compared to ordinary kriging. Overall, the universal kriging approach successfully generated continuous weather surfaces that respected both the spatial configuration of measurement stations and the topographical influences on weather patterns (Figures 7 and 8). The interpolated values showed reasonable spatial coherence and aligned with expected meteorological patterns across Poland's terrain.
# Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling
## 3 Multitask Learning with Knowledge Base for Joint Slot-Filling and Intent-Detection  
Intent detection and slot filling are important modules in a task-oriented dialog system. These two modules can convert the text that a user inputs into a semantic representation, which provides task information to support the dialog system. However, completely utilizing mutual promotion and shared resources between the two modules are difficult by only relying on joint models that are presently used. And the value of a knowledge base to these modules has not yet been explored. To solve these problems and improve the accuracy of the intent-detection module and the F1 score of the slot-filling module, this paper proposes a joint model based on MTL with a knowledge base. Benefiting from the MTL framework and external knowledge, our model obtains the shared parameters and features between two modules and implements joint optimization by using a weighted-loss function. Moreover, a knowledge base is needed to improve model performance. In Figure 1, we show the flow chart of this model.  
The input of the model is the text \(x\) of an utterance, which is a sequence of words--\(x_{1},x_{2},x_{3},\cdots,x_{T}\)--and \(T\) is the length of the utterance. The model consists of two kinds of output, i.e., the intent label \(y^{l}\) and the slot label sequence \(y^{\text{s}}_{i}\). Our method includes the following steps. First, we propose a general LSTM-CNN shared presentation layer in which a text sequence sequentially passes through the LSTM and CNN in order to obtain the text shared-representation features. Then, we establish the Bi-LSTM model with the attention mechanism for the intent-detection and slot-filling modules respectively, according to the differences between the intent-label information and the slot label information. Taking both these modules into consideration, WordNet as a knowledge base is introduced in the Bi-LSTM model. This is conducted with the objective of extracting characteristics of each task based on the shared representation features, and modifying the hidden vector of the original Bi-LSTM model via the knowledge base. Afterwards, a weighted-loss function based on the weighted self-learning method is used for joint optimization. Finally, the model is optimized by adaptive moment estimation (Adam).  
Next, each layer of the model will be explained in more detail.
## 5 Conclusions and Future Work  
In this study, we reviewed the performance of IoT attack detection models that use machine learning techniques to analyze and evaluate attacks. We identified 49 primary studies between 2016 and 2020 after a comprehensive investigation following an organized process. We summarized these primary studies based on the datasets, ML techniques, types of IoT attack, independent variables, evaluation metrics, and monitoring infrastructure via DevSecOps pipelines. We summarize the main findings as follows:  
* Most primary studies used IoT device testbed datasets, and others used public datasets. NSL-KDD, UNSW-NB15, and KDDCUP99 repositories were found to be the most frequently used datasets among researchers.
* BN, DT, NN, clustering, SVM, FS, and EL were the ML techniques used in primary studies, and NNs were the most widely used technique for IoT attack detection.  
* DOS, U2R, and R2L attacks were most widely considered in the primary studies based on the results we obtained.
* Accuracy, recall, and precision were the most widely used evaluation metrics in the primary studies.
* Few studies analyzed device log traces from IoT devices to identify IoT attacks and monitor infrastructure using DevSecOps pipelines.  
For future studies on IoT attack detection using machine learning techniques, the following are recommended:  
* More data preprocessing and data cleaning techniques should be applied to improve the quality of public datasets.
* Using data from real IoT device traffic will enhance the effectiveness of ML techniques.
* The performance of IoT attack detection models should continue to be enhanced through integration with other algorithms.
* Infrastructure configuration should continue to be monitored using methods based on software pipelines.
* Machine learning techniques should be used for advanced supervision and monitoring.  
Conceptualization, A.B. and H.F.; methodology, A.S.; software, A.A.; validation, A.B., H.F. and L.E.; formal analysis, H.F.; investigation, A.B.; resources, H.F.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.A.; visualization, A.S.; supervision, L.E.; project administration, H.F.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
## Appendix A Simulation Setup for the IEEE 802.11 Family
### Received Signal for the IEEE 802.11 Protocols  
The received signals \(R(x)\) for all protocols are captured under various channel propagation conditions \(H(x)\) as summarized in Table 1. Each IEEE 802.11 standard is simulated under five propagation channels \(H(x)\) independently. Each channel \(H(x)\) is measured with a range of SNR levels. Three values for the T-R separation \(d\) between the transmitter and receiver are incorporated to explore the propagation path loss model effect with small T-R separation \(d~{}=~{}3\) m, medium T-R separation \(d=10\) m, and large T-R separation \(d=30\) m. Additive White Gaussian Noise (AWGN) is added at different SNR levels \(n(x)\) for all channel propagation models. Moreover, the receiver noise is added to the received signal \(R(x)\) with a Noise Figure (NF) equals 9 dB.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 3 Related Work
### User Mobility Modeling Techniques
A high level signifies a detailed and comprehensive depiction of complex interactions among POIs. * Enhanced Spatial and Temporal Dynamics: This criterion focuses on the model's integration of spatial and temporal data, which is essential for dynamic modeling and providing context-aware recommendations that adapt to real-time environmental changes. The evaluation is binary: either there is no integration of spatial and temporal data, or there is an effective integration of these data types. * Scalability and Flexibility: This criterion considers the model's capability to accommodate new data inputs and user preferences efficiently, allowing for updates and customizations without necessitating a complete system overhaul. The evaluation levels are categorized as follows: a low level indicates a limited ability to scale and integrate new data inputs, a medium level denotes a moderate ability to scale and integrate new data inputs, and a high level represents a high capability to scale and integrate new data inputs efficiently. * Real-Time Data Handling: This criterion examines the model's effectiveness in processing and integrating data in real-time, which is a critical feature for applications requiring immediate response, such as traffic management and event-driven adaptation strategies. The evaluation is binary: either the model is ineffective in real-time data handling, or it is effective in processing and integrating real-time data. Table 1 offers an insightful overview of various user mobility models highlighting their diverse capabilities and limitations. [PERSON] et al. 's model [19] excels in spatial-temporal dynamics but face challenges in relationship management and real-time data handling. [PERSON] et al. 's modeling [20] has been noted for its effective handling of complex relationships and real-time data, although its scalability is moderate. [PERSON] et al. 's work [21] struggles with scalability and real-time responsiveness, though they manage spatial-temporal dynamics to varying degrees. [PERSON] et al. 's work [22] stands out with strong scalability and adept spatial-temporal integration but lacks real-time capabilities. In contrast, our graph-based model sets a benchmark with high scalability and excellent real-time data handling, effectively adapting to dynamic urban environments without the need for constant recalibration. This model's integration of advanced graph-based techniques provides precise, contextually-aware insights into urban mobility, leveraging real-time data to enhance traffic management and urban planning significantly. [PERSON] et al. [23], while their work is capable of real-time data handling, were limited by their model's lack of scalability, restricting broader application. Our approach, with its comprehensive features, not only matches but surpasses existing models, making it exceptionally suited for sophisticated user mobility solutions.