esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Buildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA's phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.  
Keywords:outliers detection; data quality; machine learning; deep learning; energy/water/gas anomalous consumption; buildings anomalous consumption detection platform +
Footnote †: journal: _Sci._**2023**, _13_, 314. [[https://doi.org/10.3390/app13010314](https://doi.org/10.3390/app13010314)]([https://doi.org/10.3390/app13010314](https://doi.org/10.3390/app13010314))
## 3 Results and Discussion
### Microstructure Evolution  
ATIG welding is autogenous welding, therefore, columnar and epitaxial grain growth was expected in the weld fusion zone from the weld fusion boundary. SEM microstructures showing grain size of the different weld zone of the A-TIG nanoscale Fe\({}_{2}\)O\({}_{3}\) flux weldments are shown in Figure 8, where a-e indicate welding zone to base metal. Among the welding zone the grains mainly consisted of equiaxed (a, b) and columnar (c) grain structures with \(\delta\)-ferrite. The grains in the heat effect zone (HAZ) (d) and base material (e) were still dominant austenite, but some coarse austenite grains transformed to coarse untempered martensite grains during subsequent cooling in welding zone and HAZ area (see Figure 8a,d, yellow circle) which may increase the hardness. The A-TIG weld fusion zone solidifies as the coarse grain, with the size of around 100-120 \(\upmu\)m. Comparing with grain size of 70-85 \(\upmu\)m [43; 44] in stainless steel of normal TIG welding, grains of ATIG with nanoscale Fe\({}_{2}\)O\({}_{3}\) flux are coarser due to the higher heat input during welding in the A-TIG joint than that in TIG joint. Homogenous grain structure throughout the weld fusion zone (from zone a to part of zone c) is observed owing to the single pass autogenous welding. The grain size measurement shows that from zone (a) to zone (c) the average grain size keeps decreasing from 120 \(\upmu\)m to around 70 \(\upmu\)m, but that is still much larger than that of the base metal (around 25 \(\upmu\)m). The large columnar grain in zone (c) should arise from the large temperature gradient in the heat effect zone. The overall larger grain sizes in zones (a, b, c) can be attributed to the resolidification from a higher temperature and larger heat input in ATIG welding than that of a conventional TIG welding.  
Figure 7: Effect of activated flux on angular distortion of the weldment (a) and welding arc voltage (b).
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 2 Related Work
### RGB and HSV Color Space Overview  
Color space is a mathematical model that represents color information as three or four different color components [42]. In the RGB color model, all other colors can be produced by adding the primary colors: red, green, and blue [43]. It can combine color expression with these three primary colors of light in different proportions. RGB is primarily used to represent colors on computer monitor screens, televisions, and color printers. The HSV color space is represented by H (hue), S (saturation), and V (value). The HSV color space corresponds closely to the human perception of colors [44]. It is suitable as a color space that deals with human visual perception. HSV color-space-based image segmentation is better than RGB color space [45].  
The HSV model, also known as hue, saturation, and brightness (HSB), defines a color space in terms of three constituent components [46]:  
* Hue--the color type (such as red, blue, or yellow) ranges from 0 to 360\({}^{\circ}\);
* Saturation--the \"vibrancy\" of the color ranges from 0 to 100%;
* Value--the brightness of the color ranges from 0 to 100%.  
The transformation of color images in the RGB into the HSV color space is achieved as in Equations (1)-(3) [42].  
\[\mathrm{H}=\arccos\frac{\frac{1}{2}(2\mathrm{R}-\mathrm{G}-\mathrm{B})}{\sqrt{ \left(\mathrm{R}-\mathrm{G}\right)^{2}-\left(\mathrm{R}-\mathrm{B}\right) \left(\mathrm{G}-\mathrm{B}\right)}} \tag{1}\]  
\[\mathrm{S}=\frac{\mathrm{max}(\mathrm{R},\mathrm{G},\mathrm{B})-\mathrm{min}( \mathrm{R},\mathrm{G},\mathrm{B})}{\mathrm{max}(\mathrm{R},\mathrm{G}, \mathrm{B})} \tag{2}\]  
\[\mathrm{V}=\mathrm{max}(\mathrm{R},\mathrm{G},\mathrm{B}) \tag{3}\]  
HSV Color-Space-Based Automated Object Localization Scenario for Robot Grasping in Factory-as-a-Service (FaaS) Platform
## 3 Experimental Results
### Quantitative Evaluation
#### 3.2.2 Objective Quantitative Evaluation
Comparing the experimental results of Proposed and Proposed *, although down-sampling does lead to some loss of image data, the differences between the down-sampled and original resolution images are not significant in terms of quantitative metrics such as AG, STD, IE, \(Q_{\text{ncle}}\) and SSIM. This indicates that in most practical applications, the processed down-sampling approach can maintain sufficient feature retention while ensuring computational efficiency.
# Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman
## 2 Materials and Methods
### Experimental Program  
All the experiments were conducted in accordance with the standards of American Society for Testing and Materials (ASTM). Before preparing the binary mixtures, the sediments were crushed in ball mill and dried in oven at 105 \({}^{\circ}\)C for 24 h. The dried reservoir sediment was added to dune sand with mass ratios as 5, 10, 20, 50, 75, 90, and 95% to the dry weight of sand to prepare the dune sand-reservoir sediment (DS-RS) mixtures. The required amount of sediment was added to a dry sand sample passing a 4.75 mm sieve. The sand and sediment were mixed thoroughly until a uniform mixture was achieved. About 10 kg of binary mixture is prepared for each mix ratio and stored in plastic containers at room temperature. The properties of dune sand were used as a reference to assess the validity of measured properties of the sand-sediment mixtures. It is anticipated that the empirical correlations from the experimental data presented in this study can be useful for future research and practicing engineers. Microsoft Excel software was used to develop respective linear, polynomial, and exponential regression models with reasonably high R\({}^{2}\) value. The fine content (F\({}_{\text{c}}\)) increased from 3.5% for the original dune sand to 74.1% for the original reservoir sediment. The data acquired from the gradation analysis has been summarized in Table 2. The results of gradation tests on the sand-sediment mixtures, together with original sand and sediment are presented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element (\%)** & **Oxygen (O)** & **Carbon (C)** & **Silicon (Si)** & \begin{tabular}{c} **Magnesium** \\ **(Mg)** \\ \end{tabular} & \begin{tabular}{c} **Calcium** \\ **(Ca)** \\ \end{tabular} & \begin{tabular}{c} **Aluminium** \\ **(Al)** \\ \end{tabular} & \begin{tabular}{c} **Iron (Fe)** \\ \end{tabular} & \begin{tabular}{c} **Potassium** \\ **(K)** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Dune sand \\ (DS) \\ \end{tabular} & 53.3 & 16.2 & 2,7 & 1.2 & 24.3 & 1.2 & 1.1 & - \\ \hline
\begin{tabular}{c} Reservoir \\ sediment \\ (RS) \\ \end{tabular} & 48.6 & 21.9 & 12.5 & 8.9 & 3.7 & 2.4 & 1.6 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elemental analysis of the tested material obtained from field emission scanning electron microscope with energy dispersive spectroscopy (EDS).
# The Construction of a Digital Agricultural GIS Application Suite
## 2 Design Considerations and Methods
### Design Considerations
#### 2.1.1 Integrated Applications  
1. Owing to its robust integration capabilities, the software suite's individual components can collaborate seamlessly, fulfilling multi-functional requirements to create a comprehensive application system tailored for professional sectors. The digital agricultural application suite is envisioned as an amalgamation of various software systems, designed to cater to the intricate needs of complex agricultural applications.
2. Featuring a uniform interface and consistent operational logic, the suite ensures that all functions and services present a cohesive and predictable appearance, irrespective of the application or operational context. This standardization is expected to enhance efficiency and minimize user errors, thereby reducing associated recovery costs.
3. The extensibility of the suite is a key feature that enhances its practicality. The development of the suite can adapt to the evolving agricultural practices and user needs. This flexibility allows the suite to customize application systems by adding new tools and services without disrupting the existing development framework.
# Analysis of Cavity Corner Geometry Effect on Recirculation Zone Structure
###### Abstract  
A numerical investigation of flow topology in open-type cavities with length-to-depth ratio \(L/h_{1}=4\) was performed in the Reynolds number range of 10-1000. Cavities with differently rounded corners were chosen for simulation. Three-dimensional numerical simulations were performed to analyse flow topology in different planes. A series of experiments was performed to ensure the validity of numerical simulations. Both numerical simulations and physical experiments were conducted with water as the working fluid. Since the results agreed acceptably, further simulations were performed. The main goal of this study was to investigate and highlight the influence of rounded cavity corners on the topology and stability of flow. Analysis revealed that fully rounded upper cavity corners decrease pressure loss compared to other investigated cases; therefore, the velocity of the main flow is increased. Additionally, fully rounded upper corners form a notably smaller recirculation zone compared to other investigated cases. Flow stability analysis showed that fully rounded cavity bottom corners negatively impact flow stability by increasing the intensity of turbulence.  
CFD; cavity flow; flow topology; open-type cavity +
Footnote †: journal: Applied Sciences
# Joint Communication-Motion Planning in Networked Robotic Systems
## 3 Cognitive-Relay-Assisted Robot System with Interference Constraints  
In this section, we enhance the robot surveillance prototype by incorporating cognitive relaying and illustrate the benefit of JCMP. We consider a system, where the sensing robot, the relay robot and the base station are secondary users (SUs), and a primary user (PU) coexists. The underlay approach is considered and the interference constraint on the primary user should be satisfied, namely, a limited amount of interference emitted by the SUs can be tolerated at the PU. The system settings are identical to those in the system model of the previous section, except that the transmit power of the sensing robot and the relay robot should satisfy the interference constraints, while the design objective is minimizing the total energy consumption of the SUs.
## 4 Experimental Results
### Passive Radar Imaging  
In this part, we aim to test the performance of the proposed methods in a passive radar imaging system. Based on the final echo Equation (17) stated in [6] which is similar to Equation (2) in this paper, sparse recovery techniques can be utilized to solve the problem of passive radar imaging. In the simulation, we choose seven digital video broadcasting (DVB) satellites as opportunity transmitters and 10 receivers, and the initialization grids are \(21\times 41\) represented for the imaging scene \(20\text{ m}\times 20\text{ m}\) (here we use the axis function in matlab to make the scene limited to be \(12\text{ m}\times 10\text{ m}\) so as to achieve a better visual effect). There are nine off-grid scatterers with different reflection coefficients in the scene of interest as the red circles illustrated in Figure 7. The number of frequency samplings corresponding to each transmitter is 10, and the SNR equals to 20 dB. Other simulation parameters are the same as displayed in [6]. Here we set \(H=2,L=S=10\) and grid refinement times \(Q=10\).  
Figure 7a shows the poor imaging result by BP due to the reason that in practice the scatterers are not located exactly on the pre-discretized grids, and the echoes are mismatched with the measurement matrix, therefore the performances of CS-based reconstructions are generally unsatisfactory. Figure 7b is the imaging result by H\(l_{1}\)-DCD, and we can see that it can seek the exact locations of the target, however the amplitude estimation of the reflection coefficients is not precise.  
Figure 7c-e represents the imaging results by the proposed methods. As expected, the proposed methods do not have the off-grid problem from the perspective of both the position and amplitude estimation results, which show the potential of our methods to be applied in practical passive radar system.  
Figure 6: MSE of position _vs._ number of signals (**a**) and amplitude _vs._ number of signals (**b**). SNR is fixed at 20 dB.  
Figure 7: Passive radar imaging results by applying BP (**a**), H\(I_{1}\)-DCD (**b**), HR-DCD (**c**), Hlog-DCD (**d**), H\(I_{p}\)-DCD (**e**). Left and Right figures show the amplitude and position estimates, respectively. Red circles denote the true scatterers of the target, and blue crosses represent the estimated results.  
Figure 8 shows the MSEs of position and amplitude estimation results _versus_ SNR by applying different methods, which are averaged over 100 Monte Carlo trials. In the simulation, we have assumed that there are nine off-grid scatterers with unit reflection coefficients distributed randomly in imaging region. It is seen that our methods outperform the conventional methods (MP, BP, FOCUSS, SBL) in further improvement of MSEs of position and amplitude estimates when off-grid target emerges. In the meantime, they have better amplitude estimation result than H\(l_{1}\)-DCD with the same parameters shown in Figure 8b. Moreover, the performances of our methods approach the OSR very closely under varying SNR, both in position and amplitude estimates.
# Potential Applications of Yeast Biomass Derived from Small-Scale Breweries
## 5 Yeast Biomass Processing Technologies
After autolysis is completed, the resulting fluid is heated up to 80 \({}^{\circ}\)C for 2 h to deactivate the remaining enzymes. In further steps, solid particles may be separated by centrifugation or filtration, and water-soluble compounds may be recovered in powder form after drying. Autolysis is considered to be a cost-effective process suitable to produce food additives. Water-soluble compounds may be further separated based on molecular mass using ultrafiltration techniques. Such approaches have been used in separating iron-interacting peptides from yeast extract [43]. The disruption of yeast cells may also be achieved with mechanical methods. Mechanical disruption includes cell mills (shaking with glass or steel balls), sonication, and French press [30]. These methods result in the fast disruption of cells and the release of intracellular content into the solution. Such approaches may be beneficial when separating biopolymers constituting cell wall or undigested proteins is considered. Cell wall fractions may be further separated with the use of different methods. The earliest method for beta-glucan purification utilized incubation in an alkaline solution (3% NaOH), followed by incubation in an acidic solution (3% HCl) [44]. This protocol was later modified with additional steps including chromatography [45]. Later, a protocol based on hot water extraction was developed, leading to the isolation of manoprotein fractions besides beta-glucan [46]. The authors claim that this procedure produces beta-glucan, which is unchanged in structure. If mannoproteins are the target, then protocols that rely on beta-glucan digestion with Zymolyase seem to be most efficient [47]. When yeast biomass is considered to provide a significant contribution to protein in human diets, reductions in nucleic acid content should be employed. This may be achieved through an enzymatic reaction following cell disruption or the separation of non-wall cell fractions. Hydrolyzed nucleotides may be separated from proteins via ultrafiltration technology. The compositions of extracts vary depending on the biomass source and processing technology (see Tables 2, 4 and 5). Enzymatic lysis (including autolysis) generates more small molecules such as amino acids, nucleotides, and nucleosides. Autolysis also results in reduced vitamin extraction (except biotin); however, it does not cause major losses when compared to other methods. Surprisingly, yeast extracts seem to be substantially enriched in cobalamin when compared with raw yeast biomass. The highest yield of hydrolyzed proteins is obtained when mild cell disruption methods like cell milling are applied.
## 3 Methodology
### Small Multi-Model Guidance
#### 3.1.2 Flexible Threshold Loss  
MRE is essentially a multi-label classification problem with images as additional information, where the output probability range \(P(r|e_{s},e_{o})\) is \([0,1]\). Thresholding is required to convert probabilities into relation labels. In the field of MRE, most methods use a fixed global threshold, i.e., binary cross-entropy loss. However, using the same global threshold for different entity pairs cannot classify well, and different relations have unequal data volumes, so global thresholding cannot enable fair learning. Especially for image-text datasets from Internet media, global thresholding fails to handle imbalanced entity pair number distributions and long-tail classes.  
Our FTL draws inspiration from ATLOP's [43] clever threshold class \(T_{H}\) setting, where the positive-negative split for each entity pair \((e_{s},e_{o})\) is determined by the special threshold class \(T_{H}\). The logit for \((e_{s},e_{o})\) greater than the logit for \(T_{H}\) is predicted as positive, otherwise it is predicted as negative. Of course, this threshold class \(T_{H}\) is learned in the same way as other classes.  
Specifically, we divide the labels for an entity pair \(T=(e_{s},e_{o})\) into two subsets: the positive subset \(P_{T}\) and the negative subset \(N_{T}\). If \(T\) has a positive relation, it must be in \(P_{T}\), otherwise \(P_{T}\) is empty. The negative subset \(N_{T}\) contains non-positive relations, \(N_{T}=R\backslash P_{T}\). Unlike ATLOP's [43] unified computation, we compute the probabilities \(P(r_{i}|(e_{s},e_{o}))\) and \(P(r_{TH}|(e_{s},e_{o}))\) separately for each relation \(r_{i}\) and the threshold relation \(r_{TH}\) of the entity pair. To address the long-tail issue, we modify ATLOP's [43] loss by incorporating focal loss [44] to balance the positive logits. The formulas are as follows:  
\[P(r_{i}|(e_{s},e_{o}))=\frac{exp(logit_{r_{i}}^{(s,o)})}{exp( logit_{r_{i}}^{(s,o)})+exp(logit_{TH}^{(s,o)})}, \tag{6}\]  
\[P(r_{TH}|(e_{s},e_{o}))=\frac{exp(logit_{TH}^{(s,o)})}{\sum_{\gamma_{j}\in N_{ T}\cup\{TH\}}exp(logit_{f_{j}}^{(s,o)})}, \tag{7}\]  
\[L_{RE}=\sum_{r_{i}\in P_{T}}(1-P(r_{i}|(e_{s},e_{o})))^{\gamma}log(P(r_{i}|(e_ {s},e_{o})))+P(r_{TH}|(e_{s},e_{o})). \tag{8}\]  
In the above loss function, \(\gamma\) is a hyperparameter that aims to pay more attention to classes with low confidence. That is, if \(P(r_{i}|(e_{s},e_{o}))\) is low, the loss contribution from the relevant class will be higher, which enables better optimization for long-tail classes.
# Inertial Parameter Identification in Robotics: A Survey
## 10 Results, Discussion and Perspectives
### Analysis and Discussion of the Results
#### 10.1.1 Noise Immunity
The DIDIM, CLIE and CLOE methods appear to be robust in general to the measurement noise in the joint position signal. This is usually expected since the DDM simulation step occurring in DIDIM, IDIM-IV, CLOE and the IDM simulation occurring in CLIE are noiseless. Please note that although IDIM-IV can also be considered to be robust to noise as suggested in particular by the results of _MCS-TX40-4-1_ and _MCS-TX40-4-2_, it tends to fail when the noise level is unreasonably high, as is for example the case in _MCS-TX40-4-3_ or _MCS-TX40-4-2_. These observations are consistent with those published in [6]. It should moreover be highlighted that when the sampling rate is high, typically above 500 Hz on a robot, there might actually be a strong correlation between the measured samples, resulting in poor conditioning of the observation matrix and consequently biased parameter estimates. In practice, sub-sampling the signal helps breaking this correlation. Nevertheless, this should be executed carefully as the high-frequency components of the signal tend to alias during the process. Filtering the signal before sub-sampling, or in other words performing parallel decimation, helps mitigating this effect. The reader is redirected toward [111] for a more detailed discussion on the topic. The influence of parallel decimation is clearly visible on the results of _MCS-TX40-4-1-MCS-TX40-4-5_, in the corresponding FOM tables, where one may observe a homogenization of the performance of the different methods with, however, the notable exception of the Kalman filters (this specific point will be addressed in more details within Section 10.1.2 of this paper). Although the performance is already satisfactory for IDIM-IV and even good for DIDIM in the case of standard joint position noise levels (typically \(\sigma_{q}=10^{-4}\) rad and \(\sigma_{q}=10^{-3}\) rad), it should be noted that parallel decimation has a positive influence over the condition number of the Jacobian in OE algorithms as pointed in the discussion of [12].