esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Unique Challenges  
### Hardware Constraints  
We have chosen to use wireless motes (such as the Crossbow MICA [22] and the Moteiv Tmote Sky [23]) as the sensor nodes in our design for several reasons. First, the motes are much smaller and lighter than other types of portable embedded devices (e.g., PDAs), making them easier to carry. Second, our specific application typically does not require complex data processing and has a low data transfer rate (e.g., 4 packets/second or less). As such, we need a device with a reasonable amount of computing power and data transmission capability, while maintaining extremely low energy consumption during sleep in order to support long-term monitoring. The Tmote Sky motes seem to have these characteristics, making them well suited for our goals. Using motes also allows for ease of prototype development, as the motes include an onboard CPU and enjoy the support of an active research community with a wealth of open-source software and tools. We note that it is certainly possible that motes with higher power CPUs could work (or even outperform the Tmote Sky) in our application, a point we plan to investigate in the future.  
Unfortunately, _the resource constraints in wireless motes make it challenging to meet the above-mentioned security requirements_. Motes have much lower processor speed, memory, link bandwidth, and energy supply than mobile PCs or PDAs, so our security mechanisms must be very resource-efficient. The MICA motes, for example, use an 8-bit, 8 MHz processor, and the comparable Tmote Sky platform employs a 16-bit, 8 MHz processor. Conventional security mechanisms incur high costs; some RSA operations can take over 80 seconds to run on a low-power 8-bit CPU [17]. In addition, the radio bandwidths of recent IEEE 802.15.4-compliant motes are limited to 250 kbps.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 2 Theoretical Analysis of Blasting Seismic Energy Generation and Attenuation under In Situ Stress
### Seismic Energy Attenuation  
The seismic energy on the elastic boundary calculated from Equation (3) is the initial seismic energy generated in rock blasting. As the seismic waves spread outward from the elastic boundary, the energy carried by the seismic waves gradually decreases, which is called seismic energy attenuation. It is well known that the geophysical laws of seismic wave attenuation include viscoelastic attenuation and attenuation due to geometric spreading. The seismic wave attenuation due to geometric spreading depends on the wave propagation distance. The viscoelastic attenuation is generally described by the seismic \(Q\), and a greater \(Q\) value represents less energy attenuation. Many studies have shown that the seismic \(Q\) varies with rock types, water content, porosity, temperature, and stress states of rock masses [43]. The experimental data in [PERSON] and [PERSON] [44] show that the seismic \(Q\) increases with the pressure exerted on the rock masses, and the increase rate depends upon the rock types and distribution of rock discontinuities. [PERSON] et al. [45] found that the seismic \(Q\) increases rapidly with pressure at lower pressure levels but tendsto be constant after the pressure reaches a higher level. [PERSON] et al. [25] further pointed out that the seismic \(Q\) would decline with pressure when the pressure is high enough to cause rock discontinuities to extend. For rock blasting in deep depths, the seismic \(Q\) varies with in situ stress. Then the seismic energy attenuation is bound to be affected by in situ stress, which makes it different from that in shallow rock masses.
## 3 Results and Discussion
### Performance Tests of the Membranes for the Removal of Emulsified Oil
#### 3.5.5 Reusability Test for the Membranes  
TNF/CNF membrane reusability was examined using ten filtration cycles. After each filtration cycle, the membrane was washed with distilled water and reused again for the next cycle. Oil concentration in the permeate was measured after each reuse cycle. As seen in Figure 12, throughout the 10 operating cycles, oil rejection rates were over 99% for all cycles. The permeate's oil content was consistently below the US EPA's oil disposal limit of 42 ppm. These findings show that the TNF/CNF nanocomposite membranes have excellent durability. It is worth noting that for the membrane washing, there is no need for any chemicals to be used (only distilled water used), which strongly suggests the good fouling-resistance of the membranes. The TNF/CNF membranes' superhydrophilic and oil-repellent surface can be consistently preserved. Figure S3 shows the long-term stability tests under acidic environment. Figure S4 shows the long-term stability tests under basic environment.  
Figure 11: (**a**) Performance test (oil rejection rate and water flux (LMH/bar)) of the TNF/CNF membrane using oil emulsions with different saline environments. (**b**) Oil concentration in the permeate for oil emulsions with different saline environments.
# Central Heating Cost Optimization for Smart-Homes with Fuzzy Logic and a Multi-Agent Architecture
## 4 Case Study
### Evaluated Proposal
#### 4.2.2 Control's Design  
It is important to describe how the control has been designed. The fuzzy control and the communication of the output to the Simulink's model will be defined in this subsection. The fuzzy agent will incorporate a fuzzy controller developed with the Scikit Fuzzy Python API [65]. The fuzzy model developed for this case study has four input variables and one output variable. The four inputs are related to the measured values of the sensors and the output value is the control action determined by the fuzzy logic. The fuzzy logic controller is based on Takagi-Sugeno architecture [66]. The main characteristic of this kind of fuzzy controllers is that the output of the control can be defined by the designer of the system. This kind of fuzzy system is considered to be the appropriate one for the current work since the output of the system should be \"on\" or \"off\" (one or zero). Therefore, the output of this fuzzy controller will not experience any fuzzification or defuzzification processes. On the contrary, other systems such as Mamdani assume that the output must be defuzzified, so that the relevant fuzzy sets would have to be defined and the output would be defined by one of these fuzzy sets and its value of belonging to the one that is active. This type of output is not the desired one in the proposed system, but we only want to obtain two values 0 to 1, off or on, respectively. This type of output is provided by the Takagi-Sugeno system that has been designed. Therefore, in the designed Takagi-Sugeno system, the output does not need to be configured. However, the four inputs of the system have to be adjusted or defined to infer the fuzzy logic principles over them. So, the membership functions for each one of these inputs has to be defined. Table 4 collects all the information needed to delimit the inputs' membership functions. But first, the four inputs have to be described. The first input is \"temperature gap,\" it is the difference of the aimed temperature and the current temperature value collected by the sensor agent. The second is \"outside temperature,\" it is the current temperature value from outside of the building. \"Humidity\" is the humidity outside the building, and \"people\" indicates the number of individuals inside the house. Input universe refers to the space where the values of this input are going to be, although, if a value gets out of this universe, the fuzzy system can deal with it without any problems. On the other hand, input intervals refer to the different groups or fuzzy values that the input can have. The types of membership functions are defined as triangular and trapezoidal; trapezoidal functions are used in larger intervals with the aim of achieving greater membership during the whole interval, while
## 4 Experimental Results
### Data Imbalance  
#### 4.6.1 Oversampling and Undersampling  
Data imbalance can impact training, especially for classes with fewer samples. To investigate the impact of unbalanced data, we employed sampling methods: oversampling and undersampling [35] on stream-level detection. Oversampling augments the minority samples with multiple copies, thus balancing the classes. Conversely, undersampling entails reducing the number of samples from majority classes by deleting them until a balanced distribution is achieved. We use the Transformer with word-level embedding, a model that offers the best performance. The data were sampled at levels ranging from 30% in the step of 10% until the balance was achieved. The results, shown in Figure 14, illustrate that oversampling provides more favorable solutions than undersampling. With an oversampling ratio of 60%, the model outperforms the original model by achieving an accuracy of 97.25%. As the oversampling ratio increased, the number of false negatives (FN) and false positives (FP) either decreased or remained stable within 22\(\sim\)24 and 4\(\sim\)14, respectively. The undersampling strategy showed little improvement for either the FN (24\(\sim\)33) or FP (5\(\sim\)23) rate. We believe that the sampling method has a greater effect on the importance of features, especially the undersampling, reducing the number of features, hence causing the classifier to miss useful information (patterns) that could be used in determining the decision boundary between the classes [36]. Generally, one can choose the best method by evaluating the performance at different levels of resampling. The overall results of the experiments conducted in this study suggest that our proposed method is the best and can effectively handle unbalanced data.  
Figure 14: Sampling results. The data were sampled in the range of 10% until the balance was achieved.  
Figure 13: Confusion matrix for stream-level malware detection using MalCode2 Vec embedding at the character level (**left**) and word level (**middle**) and integer encoding at the word level (**right**).
# Low-Temperature and Low-Pressure Silicon Nitride Deposition by ECR-PECVD for Optical Waveguides
## 2 Fabrication
### Annealing  
To investigate the presence and removal of hydrogen in the films, annealing under ambient N2 flow was carried out in a quartz tube furnace. We selected 1000 \({}^{\circ}\)C for 1 h for hydrogen removal based on multiple findings [29; 30]. Characteristics reported for hydrogen release include lower optical losses, decreased thicknesses and increased indices of refraction due to film densification. The films were annealed with an open exhaust and N2 flow rate of 500 sccm in a 3-inch \(\times\) 2-meter-long furnace. Films were inserted into the furnace at maximum temperature on a quartz wafer boat and pulled out of the furnace but left in the tube and allowed to cool adjacent to the heating zones during a 4 h ramp down.
## 3 Coherent Pointing Estimation using Notch and Boresight Pattern
### Pointing Retrieval using Correlation Technique  
The generated complex-valued profile has two degrees of freedom: amplitude and phase. In addition to the gain drop of the notch pattern at boresight, also the 180\({}^{\circ}\) phase change at the notch position is characteristic and can be retrieved in the profile. While polynomial fitting is often used for conventional pointing estimation [8], a joint estimation of amplitude and phase is advantageous for the here mentioned technique as more information about the antenna pointing is available. The joint estimation can e.g., be performed by complex-valued correlation between the measured elevation profile and the predicted range profile derived, e.g., from an antenna model.  
The complex-valued cross-correlation function is defined as:  
\[\left(f\star g\right)\left(\tau\right)\equiv\sum_{t=0}^{N}f^{*}(t)\cdot g(t+\tau) \tag{4}\]  
where \(f\) and \(g\) are the complex measured and predicted range profiles in gain and phase, \(\star\) is the correlation operator, \(f^{*}\) denotes the complex conjugate of \(f\), and \(N\) is the number of samples. From the location of the amplitude peak of the correlation function the misalignment can be estimated. To verify the accuracy of the whole pointing estimation process, the sample Pearson correlation coefficient between measured and modeled range profiles is proposed. An amplitude value close to 1 signifies a good agreement while values near 0 indicate no correlation.
## 3 Results
### Economical Implications  
According to the method proposed by [61] for assessing the relative enrichment of trace elements, concentrations of trace elements in coal can be categorized into six groups: highly enriched (CC \(\geq\) 100, where CC represents the concentration coefficient, indicating the ratio of trace element concentrations in the samples being studied compared to the averages for WHC), significantly enriched (10 \(\leq\) CC \(<\) 100), enriched (5 \(\leq\) CC \(<\) 10), slightly enriched (2 \(\leq\) CC \(<\) 5), normal (0.5 \(\leq\) CC \(<\) 2), and depleted (CC \(<\) 0.5). The concentration coefficient (CC) of P and Ti major elements and trace elements in our samples was determined by referencing the values established for world hard coal (WHC) by [38]. This CC helps ascertain whether elements are enriched or depleted by comparing the concentration of trace elements in our coal ash samples to the corresponding WHC values (Figure 3). While we aimed to calculate CCs for all major elements, most lacked corresponding WHC values for normalization. Consequently, we specifically measured the CCs for the major elements P, Ti, and Mn. The element P exhibits a CC ranging from 0.3 to 1.7 in Jurassic and Carboniferous coal ash samples, placing it within the normal concentration range. Similarly, Ti has a CC between 0.3 and 1.5, also falling within the normal concentration category. Mn has a CC of 0.4 in Jurassic samples, placing it in the depleted category, while in Carboniferous samples, its CC is 1.0, indicating a normal concentration. Trace elements in the five mine samples, including Yb, have a CC ranging from 1.4 to 3.8 in Jurassic and Carboniferous coal ash samples, indicating they fall into the slightly enriched category. Other trace elements, such as Cu, As, and Y, with CC values ranging from 0.5 to 1.4, fall into the normal concentration category across the samples from the five coal mines. Elements such as Cr, Pb, V, Ga, and Zr in Jurassic samples have CC values below 0.5, placing them in the depleted category. Similarly, in Carboniferous samples, Cr, Zn, Pb, and Ni have CC values below 0.5 and are also categorized as depleted. However, in the same Carboniferous samples, V, Ga, and Zr have CC values greater than 0.5, placing them in the normal concentration category (see Figure 7). The concentration coefficients (CCs) of certain elements in the studied coal ash samples provide insights into their potential economic value. Elements like Yb, slightly enriched across Jurassic and Carboniferous coal ashes, may attract economic interest due to their applications in high-tech industries. Although elements such as Cu, As, and Y fall within normal concentration ranges, they could still hold value depending on the feasibility of extraction. Conversely, the depleted levels of elements like Cr, Pb, V, Ga, and Zr suggest limited economic recovery potential from these samples. These findings highlight the selective recovery potential of specific trace elements, with implications for resource utiliza
## 2 The Operation Characteristics of an MMC System  
### DC Fault Analysis of an MMC Before Blocking  
The equivalent circuit representation of an MMC composed of an HB or FB configuration prior to the fault detection is shown in Figure 1. In this period, the DC fault current \(i_{2}\) has not yet reached the fault detection limit. Considering KVL, the upper loop can be written as:  
\[V_{x}=L_{x}\frac{di_{xg}}{dt}+R_{ixg}+L_{arm}\frac{di_{xu}}{dt}+R_{arm}i_{xu}- v_{xu}+L_{sc}\frac{di_{xz}}{dt}+R_{sc}i_{z}+v_{nN} \tag{1}\]  
where \(x=a,b,c\) and \(L_{sc}\) and \(R_{sc}\) represent the equivalent values of the inductance and resistance of the transmission line.  
Figure 1: Equivalent circuit of a modular multilevel converter (MMC) system composed of a half bridge (HB) or full bridge (FB) configuration.  
The lower loop for each phase can be represented by:  
\[V_{x}=L_{x}\frac{di_{xg}}{dt}+R_{i}i_{xg}-L_{arm}\frac{di_{xl}}{dt}-R_{arm}i_{xl}-v_ {xl}+v_{nN} \tag{2}\]  
Also applying KCL, in each node we obtain:  
\[i_{x}=i_{xu}-i_{xl} \tag{3}\]  
\[i_{z}=i_{al}+i_{bl}+i_{cl} \tag{4}\]  
\[i_{z}=i_{au}+i_{bu}+i_{cu} \tag{5}\]  
Subtracting Equation (2) from (1) yields:  
\[L_{x}\frac{d(i_{xu}+i_{xl})}{dt}+R_{z}(i_{xu}+i_{xl})+L_{sc}\frac{di_{z}}{dt}+R _{sc}i_{z}=(v_{xu}+v_{xl}) \tag{6}\]  
Equation (6) is valid for all the phases. Adding Equation (6) for each phase, we obtain:  
\[(2L_{z}+3L_{sc})\frac{di_{z}}{dt}+(2R_{z}+3R_{sc})i_{z}=\sum_{x=a,b,c}{(v_{xu}+ v_{xl})} \tag{7}\]  
For an MMC at any instant of time, \(N\) number of submodules are switched on, where \(N\) is the number of submodule (SM) per arm. Therefore, it can be written that:  
\[(V_{xu}+V_{xl})=NV_{sm} \tag{8}\]  
According to Reference [18], at this point in time all the capacitors are parallel and the DC current can be expressed as:  
\[i_{z}=\frac{2C_{sm}}{N}\sum_{x=a,b,c}{(v_{xu}+v_{xl})} \tag{9}\]  
where \(C_{sm}\) is the capacitance of each individual SM. Substituting Equation (9) into Equation (7) will give:  
\[(2L_{z}+3L_{sc})\frac{d^{2}i_{z}}{dt}+(2R_{z}+3R_{sc})\frac{di_{z}}{dt}+\frac{ N}{2C_{sm}}i_{z}=0 \tag{10}\]  
Equation (10) provides the equation of the DC current \(i_{z}\). This equation will be valid until \(i_{z}\) reaches the limit [19].
## 2 Related Work
### Open-Categorical Classification  
There are two situations in the recognition system: closed-set and open-set identification. Closed-set recognition refers to the fact that the test set is a known category. On the contrary, the open-set recognition is that the test sets have the unknown classes. In the theory of statistical learning, it is often only focused on the recognition of closed sets. However, in reality, open-categorical detection has a great practical value and application prospect, such as ignorant obstacle recognition in automatic driving [23], open-categorical aircraft recognition in image processing [24], stranger detection in speech recognition [25], open-categorical text classification [26; 27], open-categorical recognition of wireless signals [28], open-categorical identification of IoT wireless sensors [29], voice signal authorization of mobile phones [30], etc.  
OCC has both similarities and dissimilarities with outlier detection, anomaly detection, and novelty target recognition. In the model of outlier detection, the purpose of training the system is to detect equipment trouble caused by mechanical faults, changes in system behavior, human errors, or instrument errors, etc. [PERSON], [PERSON] et al. [31] propose a method based on a triangular geometric algorithm to detect and filter out the abnormal points. Moreover, [PERSON], [PERSON] et al. [32] define that extraction of unknown and new data from the huge dataset that has been left during the classification process is considered as novelty detection. In comparison to the above definitions, the problem is defined as OCC if the training classes are only partly known beforehand [33].  
Recently, the techniques of OCC have made great progress. One-class Support Vector Machine (OC-SVM) aims to detect outliers relative to a single training class [34], and [PERSON] et al. propose \"1-vs-set Machine\" for single-class detection and Weibull-calibrated SVM (W-SVM) for multi-class classification in open set scenarios [35; 36], so that, the one-versus-rest multi-label support vector machine (OvR-SVM) is one of the most popular algorithms for OCC. Besides, [PERSON] et al. propose distributed networks for open set learning that can learn and model different novel classes [37]. Unfortunately, although the above methods for OCC have achieved some good results, there are still difficulties in theoretical support and practical applications. In the individual identification of IoT devices, because of the high similarity of each device, the mapped feature spaces are often very close. To have a better performance for open-categorical individual identification of the same batch of IoT devices, further improvement of the model construction is required.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing
#### 3.3.3 Data Augmentation  
DDPM's generative capabilities extend to data augmentation. By generating realistic variations of existing images, DDPM aids in enriching training datasets for other machine learning tasks. This is particularly useful in scenarios where obtaining a large annotated dataset is challenging [43; 53].
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 3. Maritime Federated Learning Use Cases
### Underwater Applications  
Another important maritime area comprises IoUT deployments and underwater vehicles. Currently, most studies are based on centralized learning paradigms that might pose difficulties for resource-constrained edge nodes to participate in model training processes; for example, when autonomous underwater vehicle (AUV) swarms are employed in search-and-rescue or pollution-monitoring applications [27; 28; 29].  
Regarding FL-aided techniques, in an ocean-of-things setting, relying on an acoustic network to acquire underwater information, ref. [23] employed federated meta-learning (FML) to tackle the lack of insufficient training data at a single buoy. FML improved the performance of DNN-based acoustic receivers by leveraging the model parameters from multiple buoys. The convergence performance was analyzed and a closed-form expression for the convergence rate was derived, considering the impact of scheduling ratios, local epochs, and data volumes on a single node. The experiments showed that FMLoffered better bit error rate performance and reduced complexity compared to conventional matched filter-based detectors. Also, FML achieved improved generalization performance over conventional FL.