esa-sceva/satcom-chunk-collection huggingface dataset:
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 5 Conclusions  
This work presented a model for the detection of high impedance faults. The framework leverages WAMS technology, protective relaying schemes, and state estimation algorithms to produce a solution that yields high detection rates, 100% in the scenarios studied as part of this work. Encouraging results combined a philosophy that emphasizes generality, making this framework a promising alternative to solve the HIF problem. The performance of this method was studied in simulations based on \(IEEE\) benchmark systems. The key outcome of this work is a solution that is highly effective at detecting HIFs, well beyond 100 \(\Omega\) of fault resistance. This is done by observing the characteristics of the system during normal operation and measuring deviations from the normal, in contrast to other HIF solutions, which focus on the characteristics of the faults.  
Factors such as line impedance, loading on the line, and available fault current impact how the eigenvalues behave during normal operating conditions. To a lesser extent, these factors also affect how eigenvalues drift during fault conditions. Drift during fault conditions is mainly driven by the characteristics of the fault. The size of the system (the number of buses, lines, and sources) also has direct influence on the signature (eigenvalue drift pattern) of the lines. Changing the size of the system is equivalent to changing the overall impedance of the system. One of the strengths of the method presented in the paper is that it adapts to changing conditions and to virtually any topology. The only requirement is that the system be allowed to establish a baseline of acceptable values, and then the algorithm will look for deviations.  
Presently, the main limitation of this framework seems to be distinguishing HIFs (over 500 \(\Omega\) in fault resistance) from large injections of measurement noise, however, statistical tools and machine learning could hold the key to overcoming this challenge. Another limitation is the lack of a fault location module. Future work will focus on addressing those two limitations in addition to expanding the framework to include detection of faults of multiple impedance profiles and faults of a dynamic and transient nature.  
Conceptualization, G.P.; methodology, G.P.; software, G.P.; validation, G.P.; formal analysis, G.P.; investigation, G.P.; resources, G.P.; data curation, G.P.; writing--original draft preparation, G.P.; writing--review and editing, A.S.B.; visualization, G.P. and A.S.B.; supervision, A.S.B.; project administration, A.S.B.; funding acquisition, A.S.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSF ECCS Award # 1809739.  
Not applicable.  
Data Availability Statement:[[http://www.ercot.com/gridinfo/load](http://www.ercot.com/gridinfo/load)]([http://www.ercot.com/gridinfo/load](http://www.ercot.com/gridinfo/load)) (accessed on 10 October 2021).  
Conflicts of Interest:The authors declare no conflict of interest.
## 2 Principle of the Miniature Michelson Interferometer (MMI)
### Correction of Laser Diode Wavelength in the MMI Kit  
The wavelength corrector kit is based on the grating diffraction principle and the autocollimation principle, as shown in Figure 4. The divided reference beam is transmitted through BS1 and impinged into a grating. A group of diffraction beams can be observed. Here, the zeroth (0 th) order and positive first (+1 st) order diffraction beams are picked up for calculating laser wavelength. Based on the diffraction grating equation, the following equation can be obtained:  
\[d(\sin\theta_{i}+\sin\theta_{d})=\lambda_{n}, \tag{8}\]  
where \(d\) is the grating pitch, \(\theta_{i}\) is the incidence angle, \(\lambda_{n}\) is the nominal laser wavelength, and \(\theta_{d}\) is the +1 st order diffraction angle with respect to \(\lambda_{n}\). As seen from Equation (8), the wavelength can be calculated if the +1 st order diffraction angle can be measured at known incidence angle and grating pitch. This is also the measurement principle of typical spectrometers [26]. In the designed wavelength corrector, the laser beam is set to normally project onto the grating; thus, the incidence angle is 0.  
Figure 3: Optical layout of the miniature Michelson interferometer (MMI) kit: (**a**) three-dimensional (3D) image; (**b**) geometric configuration of generating four interference fringes with 90\({}^{\circ}\) phase shift.  
However, if the nominal wavelength is varied by \(\Delta\lambda\), the diffraction angle will have a drift. Thus, Equation (8) can be modified to  
\[\lambda_{n}+\Delta\lambda=d\sin(\theta_{d}+\Delta\theta_{d}), \tag{9}\]  
where \(\Delta\theta_{d}\) is the drift of +1 st order diffraction angle due to \(\Delta\lambda\).  
From Equation (9), it is seen that, if \(\Delta\theta_{d}\) can be measured in real time, \(\Delta\lambda\) can be immediately calculated, and the real-time laser wavelength can then be automatically self-corrected without the need of the air sensor and the Edlen equation. This is the significance of the proposed self-wavelength correction.
## 2 Background
### Object Detection
#### 2.2.5 Mask R-CNN  
This method extends the Faster R-CNN concept by adding a branch for predicting an object segmentation mask in parallel with the bounding box prediction. In that case, the two units work for class prediction, both within an RoI. Segmentation is widely used for estimating poses, although it requires a much more refined spatial layout of an object. This method harnesses the velocity and, principally, Faster R-CNN's accuracy to get over the precision requirements and get over the known image segmentation approaches [25].  
A mask encodes an input object spatial layout, and thus, a mask is predicted for each RoI using an FCN. This mask maintains the spatial layout without collapsing into vector representations. Instance segmentation is challenging since it requires correct object detection and precise segmentation to classify each pixel into a fixed set of categories. Recalling, this method adds a segmentation mask on each RoI. The added branch for segmentation comprises a small FCN applied to each RoI, predicting a segmentation mask in a pixel-to-pixel manner. This scenario only adds a small computational extra requirement, non-considerable compared to the needs of the standard R-CNN methods and other segmentation algorithms. The process is shown in Figure 8.  
Furthermore, it is essential to remark that the authors proposed the RoI-Align layer, a quantization-free layer instead of RoI-Pooling that has misalignments between the RoI and extracted features. RoI-Pooling does first the same step for all RoI abstraction methods, dividing each coordinate by \(K\), from the new relative coordinates the RoI is cropped. Finally, with quantization, the cut part is divided into bins that give an \(n~{}\times~{}n\) RoI. RoI-Align presents a standard operation to extract small feature maps, properly aligning with the input to show the per-pixel spatial correspondence by defining the RoI with four points from the feature's division coordinates map is then bilinear interpolated to result in the final RoI, as shown in Figure 9. This process results are critical for the mask prediction, and has an enormous impact, improving the mask accuracy between 10-50%.  
The model outperformed the baseline of other state-of-the-art instance segmentation models. The authors reported the inference time of architecture, capable of running at 195 ms per image on Nvidia Tesla M40 (plus 15 ms CPU by resizing outputs). This method has been satisfactorily used in different applications. However, it presents a slightly higher accuracy than other mature architectures; there is a dilemma because faster architectures are capable of real-time working, such as YOLO (explained in the next section), which shows an adequate working velocity.  
Figure 8: Mask RCNN architecture.
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 2 Materials and Methods
### Experimental Setup  
To help validate the performance of the proposed TCH-based MC communications, we implemented a macroscale test bed. In our experiment, we used pH levels to test the molecules as information carriers. The different pH measurements are indicators of how acidic or basic the water is: pH's above 7 indicate a basic solution and below 7 an acidic solution, on a scale between 0 and 14. The pH measures the amount of free hydrogen (H\({}^{+}\)) and hydroxyl (OH\({}^{-}\)) in the water. This means that water with more free hydrogen ions is acidic and water with more free hydroxyl ions is basic. The scale on which pH is measured is logarithmic, which means that increasing or decreasing an integer value changes the concentration by a factor of ten [31; 32]. Thus, we use an acid and a base to send information. The used base solution has a pH level of 9 and the used acid solution has a pH of 3. Utilizing pH could constitute a problem because of the limited scale, from 0 to 14. For example, in the case of bit 1 being represented by the base and the bit-0 by the acid, if many consecutive bit-1's were transmitted, information could be lost. To avoid this, we decided to use Manchester codes in this experiment to transmit the bits. Thus, bit-1 is represented by sending an acid and then a base, and bit-0 is represented by sending a base and then an acid. This prevents the information from being lost as it ensures the same amount of acid and base solution is released in the channel independently of the information sequence.  
Next, we will explain the constituents of the system namely, the transmitter, the channel, and the receiver. The main diagram of the experiment is presented in Figure 5a.  
Figure 4: TCH decoder [25].  
Figure 3: Typical autocorrelation function of a B-TCH polynomial with \(n=256\), [24].
# On Neighborhood Structures and Repair Techniques for Blocking Job Shop Scheduling Problems +
## 5 Neighborhood Structures
### Characteristics and Evaluation  
#### 5.2.1 Connectivity of the Neighborhoods  
A neighborhood is said to be connected, if every existing feasible schedule can be transformed into every other existing feasible schedule by (repeatedly) applying a given neighbor-defining operator, see [13; 39; 45]. Here, the neighbor-defining operators consist of a move and a repairing scheme. The connectivity of the neighborhood is of significant importance in the application of search procedures, since it guarantees that the methods are capable of finding optimal solutions. However, such a structural result can only be interpreted as an indication for the actual performance of a neighborhood-based heuristic solution approach on practically relevant instances.  
**Proposition 3**: _Given general release dates \(r_{i}\in\mathbb{Z}_{\geq 0}\) for \(J_{i}\in\mathcal{J}\) and the minimization of total tardiness as the optimization criterion, the proposed neighborhoods, namely the API neighborhood, the TAPI neighborhood and the TJ neighborhood, are **not connected**._  
Consider API and TAPI moves first. As described in Section 5.1, in a feasible schedule, there may exist two subsequent operations \(O_{i,j}\) and \(O_{i^{\prime},j^{\prime}}\) on a machine in the schedule which are considered as non-adjacent due to an idle time caused by the release date of the succeeding job \(J_{i^{\prime}}\). Such a pair of operations can never be chosen for an API or a TAPI move in constructing neighboring schedules. Thus, a schedule involving the ordering \(O_{i^{\prime},j^{\prime}}\to O_{i,j}\) cannot be reached by applying the neighbor-defining operators even if it is feasible for the BJSP. Thus, the API and the TAPI neighborhoods are not connected.  
Furthermore, regarding the TJ move which shifts all operations of a currently tardy job in the permutation, the limitation to choosing a job with a strictly positive tardiness value implies that the neighborhood of feasible schedules with a total tardiness of 0 is empty. Even if optimal solutions for the BJSPT are found in this case, these schedules are isolated by definition and no other feasible schedule can be constructed subsequently. Thus, the TJ neighborhood is not connected.  
Despite these negative findings on the connectivity of the neighborhoods, the proposed structures are still supposed to be successfully applicable in a metaheuristic search method for the BJSPT. It can be expected that extraordinarily widespread release dates, which cause a disjoint partitioning of the search space, do not occur in practically relevant problems. Furthermore, in most of the cases, it is not necessary to continue the search once an optimal solution is found.  
However, the questions on whether the described API neighborhood is connected for the special case \(r_{i}=0,\ J_{i}\in\mathcal{J}\) or for a specific combination of release date and processing time ranges remain open. It is conjectured that the API neighborhood together with the ART feature the connectivity property for the BJSP without release dates.
# The Effect of the Water Table on the Bearing Capacity of a Shallow Foundation
###### Abstract  
Immersion is an important part of reservoir engineering investigation and evaluation. Determining the reasonable and effective burial depth of the critical immersion water table is one of the key scientific issues in the impact assessment of the bearing capacity of reservoir immersion foundations. In this study, basic physical and mechanical property tests were carried out on the soil in the typical immersion area of Xiaonanhai Hydropower Station, and the influence mechanism of saturation on the mechanical properties of building foundation soil and immersion on the bearing capacity of a shallow foundation was obtained. According to the test results, the influence depth of the rising groundwater level on the stability of the building foundation is analyzed, and a method to determine the critical depth of immersion groundwater in the reservoir is proposed. Taking the typical building foundation of Luohuang Town in the immersion area of Xiaonanhai Reservoir as an example, the validity of the critical water depth is further verified. The results show that the safety limit depth of the independent foundation affected by the rise of the water table increases with the increase of the width of the foundation and decreases with the increase of the buried depth of the foundation. Considering the safety limit depth, the critical depth of building immersion is 4.830 m, and without considering the safety limit depth, the critical depth of building immersion is 4.05 m. To a certain extent, it can reduce the impact of water table changes on the bearing capacity of shallow foundations.  
reservoir immersion; shallow foundation; bearing capacity; critical burial depth; safety limit depth +
Footnote †: journal: Applied Sciences  
1
## 1 Introduction  
Wireless sensor networks (WSNs) have recently arisen as a data-gathering approach with a variety of utilizations. A WSN is commonly made from minimal-expense, low-power, thickly conveyed, and haphazardly dispersed sensors [1]. As well as checking the climate by taking spatial or fleeting estimations, sensors are likewise capable of steering detecting information back to an inside or outside sink [2; 3; 4]. IoT is expected to be the next major communication revolution [5]. Creating the ideal network of all wireless devices that are capable of Internet-based communication is the primary goal of the Internet of Things. An Internet of Things ecosystem is atypical due to its many diverse elements, which range in size from tiny sensors to massive, dynamic data centre nodes; its strong execution environment; and the clear nature of the data produced via intelligent phenomena [6]. Gathering physical data and turning them into useful information and labour involve a number of processes, all of which depend on the IoT ecosystem for support. In particular, certain applications require complex transformations, such as data and time-series analysis, and some are latency sensitive [7]. IoT devices, like sensors, are not very good at storing large numbers of data or sending out complex tasks. They also have limited energy and transformation support. As a result, strong components are needed to complete the transformation processes that Internet of Things applications demand. Data centers, gateways, and smart phones could be among these gadgets [8].  
Resource management includes updating firmware, setting up the network architecture, and keeping an eye on performance [9]. Resource management is typically connected at the Internet of Things network end, producing all the relevant information at the user end, where device managers handle the large number of devices and data. IoT stands for Internet of Things, a development in computer technology and communication that attempts to link intelligent items together. Anything that environments may communicate with a smart object [10]. Figure 1 shows the WSN network. One of the key challenges in reaping the benefits of this new typology is combining different technologies and concerns [11]. By obtaining environment data and surrounding information, a WSN may take centre stage. The installation of a WSN design, however, is necessary to meet the innovative challenges that have been posed online and must be evaluated before we can benefit from such considerations [12]. The primary obstacle facing the Internet of Things is the limited availability of resources, such as electricity, processing power, memory, and wireless communication range and bandwidth, all of which have an impact on routing [13].  
The availability of real devices, such as cars, buildings, and anything with actuators, sensors, implanted hardware, or programming, is made possible by the Internet of Things (IoT) [14]. These smart devices can collect and exchange data more skillfully for different purposes by enabling IoT [15].
## 5 Conclusions
### Performance of Machine Learning Models  
The study demonstrated that traditional ML models, such as Decision Trees (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB), could effectively classify fish samples with varying degrees of accuracy depending on the feature type and measurement position. However, the Convolutional Neural Network (CNN) model, particularly when combined with the \"prep + derl\" feature extraction technique, outperformed these traditional methods. The CNN model's ability to achieve an accuracy of 83.9% underscores the potential of DL techniques in handling complex spectral data, where subtle patterns in the NIR spectra can be leveraged for more accurate classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Position-Independent**} & \multicolumn{2}{c}{**Position-Dependent**} \\ \cline{2-5}  & **Accuracy (\%)** & **Feature** & **Accuracy (\%)** & **Position** & **Feature** \\ \hline DT & 75.8 & prep & 79.6 & Skin, nape & prep + der2 \\ KNN & 78.9 & derl/prep + der2 & 78.8 & Skin, stomach & prep + der2 \\ SVM & 75.1 & prep & 78.1 & Skin, stomach & prep \\ XGB & 80.3 & der1 & 81.6 & Skin, stomach & prep + der1 \\ CNN & 72.3 & der1 & 83.9 & Skin, stomach & prep + der1 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of the best cases of different classifiers.
## 3 Results and Discussion
### Discussion
#### 3.6.3 Limitations and Future Directions  
Despite its promising performance, PLM-FRP has several limitations that present opportunities for future research. The primary limitation is the relatively small size of the current benchmark dataset; expanding it with more diverse, experimentally validated FRPs would likely enhance the model's generalizability. Additionally, while our negative dataset was carefully constructed, the evolving nature of biological discovery means some proteins currently labeled as non-FRPs might later be identified as having a role in ferrotposis, introducing potential label noise. Furthermore, the interpretability of the hybrid features, particularly the abstract embeddings from ESM2, remains a challenge. Future work should focus on developing advanced interpretation techniques to map these predictive features to concrete biological properties. Addressing these limitations by curating larger datasets and improving model interpretability will be crucial for developing the next generation of predictive tools for ferrotposis research.
## 2 Materials and Methods  
CZTS NPs were prepared by hot-injection methods from the Schlenk line. The Cu, Zn, and Sn sources were 0.133 mmol copper (II) acetylacetonate, 0.915 mmol zinc acetylacetonate, and tin (IV) bis (acetylacetonate) dichloride, respectively. The precursors were dissolved in oleylamine under purging with nitrogen gas at RT three times. The sulfur-OLA solution was heated to 60 \({}^{\circ}\)C and injected into a flask at 130 \({}^{\circ}\)C under nitrogen gas. This reaction was maintained for 10 min, and then the temperature was increased to 225 \({}^{\circ}\)C for 30 min. After the finished process, the flask was cooled to RT, and NPs were washed with hexane, isopropanol, and methyl alcohol. The solution was centrifuged at 8000 rpm for 5 min to separate the CZTS NPs and remove the solvent. The powder was dried under an inflow of 4 sccm nitrogen gas.  
To apply the CZTS NPs on the substrate, CZTS ink was formed by dispersion of CZTS NPs in 1-hexanethiol at 200 mg/mL. The deposited CZTS thin film was dried at 80 \({}^{\circ}\)C for 10 min, followed by 150 \({}^{\circ}\)C for 5 min, and then the photo-sintering process was applied.  
The IPL system (PulseForge 3300, NovaCentrix; Austin, US) was used to perform the photo-sintering process, and the CZTS films were irradiated with different bank voltages while pulse duration was fixed for 1 ms. The preferential orientation and crystallinity of the films were studied by X-ray diffraction (XRD) measurements. Also, the film surface morphology was examined by an ultrahigh-resolution field emission scanning electron microscopy (FE-SEM). A Raman study was conducted using an inVia Raman microscope and a 50 mW air-cooled argon ion laser was used to excite the samples at 514 nm. The UV-vis spectrophotometry was used to measure the optical properties at wavelengths from 200 to 1000 nm.  
To fabricate a solar cell device, the photo-sintered CZTS film was deposited on the Glass/Mo substrate using the CZTS ink. The film of CZTS was coated with the single-step screen printing on the substrate using a doctor-blade with the thickness of 1.5 \(\upmu\)m. Figure 1 illustrates the coated CZTS thin films, which were treated by the IPL for rapid thermal processing. The thin films absorbed the energy and the exposed areas resulted in a rapid rise in temperature, heated locally. This led to the metal nanoparticle films undergoing solid state and chemical modifications, becoming conductive bulk films. By controlling the energy, the CZTS films were irradiated with an energy density of 2.82, 2.96, and 3.09 J/cm\({}^{2}\), respectively. After irradiation, a CdS buffer layer was deposited on the CZTS layer using a chemical bath deposition process. Radiofrequency (RF) magnetron sputtering formed another buffer layer of intrinsic zinc oxide (i-ZnO) (50 nm) and a high-conductivity window material of aluminum-doped zinc oxide (AZO) (500 nm). Finally, 500 nm of Al front contact was deposited by a thermal evaporator, and the area of the 0.39 cm\({}^{2}\) solar cell device was fabricated.