esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In this paper, a gear sensor is presented and employed that can be retrofitted in many cases and offers a space-efficient alternative to conventional encoders mounted on the rotating equipment, which add rotational inertia and space requirements. Furthermore, it is not directly integrated to the gear and, therefore, does not have such limitations regarding the energy supply and the weakening of the gear. However, it uses the gear as a material measure and thus has a short transfer path. The aim of this paper is to apply the rotational angle measurement with magnetoresistive (MR) sensors for gear systems [15] to different manifestations of gear damage and to classify these with a machine learning algorithm. The results are compared to a rotational angle sensor at the same shaft as the gear wheel sensor and an acceleration sensor at the gearbox housing. The gear wheel sensor can be retrofitted in many cases and provides a package-efficient alternative to classical encoders applied to the rotating equipment, causing additional rotational inertia and build space requirements. Measurements for a sample transmission show the applicability of the proposed method, expanding the current state of the research. The novelty of the present study lies in its utilization of the gear wheel sensor for the classification of various kinds of artificial damage under stationary and transient operating conditions in comparison with existing measurement methods. The results of our research indicate that rotational angle measurements, facilitated by a gear wheel sensor, offer a robust and reliable method for identifying and classifying gear faults. This approach not only enhances the accuracy of fault detection but also provides valuable insights into the severity and progression of gear damage. Our findings contribute to the growing body of knowledge on angle-based condition monitoring and highlight the potential for further advancements in this field. Our methodology is presented in Section 2, which includes the test stand with the sensor setup, with a particular focus on the integration of the MR sensor as a gear wheel sensor. Furthermore, the artificial damages are presented according to the test setup. Finally, the data acquisition procedure, the data evaluation process, and the classification algorithms are explained. The classification accuracy is presented Section 3 using confusion matrices, demonstrating the effectiveness of rotational angle measurements in detecting and classifying artificial gear damages. The results are also discussed in this section. Finally, Section 4 concludes with the implications of our findings for future research, as well as the practical applications of our methodology in gear condition monitoring.
###### Abstract
An additional five cities with populations ranging from approximately 50,000 to 99,000 participated in the CLIMCITIES (\"CLIMate change adaptation in small and medium-sized CITIES\") project following the MPA44 methodology [22; 23]. ClimCities focused on climate change adaptation in five small and medium-sized Polish cities (Belchatow, Tomaszow Mazowiecki, Ostroleka, Siedlce, and Nowy Sacz). The climate projections for all cities were the first step in developing Urban Climate Adaptation Plans (UCAPs) in collaboration with authorities, residents, and experts. No other local climate projections were available for Poland at that time. The results of these projects have already been used in current policymaking for climate change adaptation in city development plans in Poland, as demonstrated by [24]. To map the broadest possible range of climate outcomes, we chose to use the RCP 8.5 (MESSAGE) scenario with the assumption of making a small effort to reduce GHG emissions and the effect of reaching a radiative forcing level at 8.5 W/m\({}^{2}\) in 2100 [25]. Moreover, stabilization scenario RCP 4.5, based on the miniCAM level 2 scenario, assumed that radiative forcing would be reached at level 4.5 W/m\({}^{2}\) in 2100 and never exceed it [26; 27; 28]. Despite the improbability of the RCP8.5 scenario [29], it remains valuable for assessing the critical risk levels associated with adaptation plans addressing climate change and extreme events. Climate change scenarios for 49 cities in both projects were developed using a homogeneous climate dataset prepared by the EURO-CORDEX [30; 31] as a result of the downscaling procedure with Regional Climate Models of global projections from CMIP5 [32] for the European domain. This paper summarizes spatial and temporal variability of the future climate conditions of selected climate indices in 49 Polish cities.
## 2 Derivation of Appropriate Mixing Ratio for SFRM
### Recommed Mixing Ratio  
Table 5 summarizes the mixing ratios that achieved target performance for each test category. In Table 4, S/C20-W/C40, S/C15-W/C35, and S/C10-W/C32 have flow characteristics corresponding to the target flow, 180 mm. In the adhesion test, only two specimens S/C20-W/C45 and S/C10-W/C32 achieved the overlayable thickness of 10 mm or higher for fiber entire fiber volume fractions. Therefore,  
Figure 4: Set up for strength test of SFRM. (**a**) Compressive strength. (**b**) Tensile strength.  
Figure 5: Compressive strength of SFRM.  
Figure 6: Tensile strength of SFRM.  
only S/C10-W/C32 is a common candidate for recommendation from both workability tests. In addition, S/C10-W/C32 with the fiber volume ratio of 1.3% shows the highest tensile strength among all the tested mixing ratios while compressive strength did not show a meaningful increase in strength with respect to the fiber volume fraction. As a result, the mixing ratio S/C10-W/C32, which showed adequate characteristics for all four evaluation categories including flow, adhesion, compression, and tensile strength, was adopted for subsequent tests of masonry prisms strengthened with SFRM overlay.
# Thermal Effects on Photovoltaic Array Performance: Experimentation, Modeling, and Simulation
## 3 Thermal Model  
This section discusses the thermal model used in an industry-standard commercial design package, PVsyst. This thermal model describes the thermal behavior and electrical performance based on the temperature dependence of PV cells, shown in Equation (1), where \(\eta_{ref}\) is the reference efficiency of the PV array at reference temperature, \(T_{ref}\), at a solar irradiance of 1000 W/m\({}^{2}\). The temperature coefficient, \(\beta\), is a material property describing the temperature dependence of PV material. If the array temperature, \(T_{array}\), is known, then the array efficiency, \(\eta\), can be calculated.  
\[\eta=\eta_{ref}(1-\beta(T_{ref}-T_{array})) \tag{1}\]  
The temperature of array can be determined by an energy balance of incident solar radiation to PV array and from PV array to the surroundings via convection and radiation. This energy balance is described in Equation (2),  
\[U(T_{array}-T_{amb})=\alpha G_{inc}(1-\eta), \tag{2}\]  
where \(T_{amb}\) is the ambient temperature, \(\alpha\) is the absorptivity of the PV array, and \(G_{inc}\) is the incident solar irradiance. \(U\) is the thermal loss factor/heat transfer coefficient (W/m\({}^{2}\cdot\)K) and is described in Equation (3). Note that \(U\) is equivalent to \(h\), shown in Table 1. This heat transfer coefficient/thermal loss factor is assumed to be linear, where \(U_{c}\) is the constant component (W/m\({}^{2}\cdot\)K) and \(U_{p}\) is the component that relates to wind velocity (W/m\({}^{2}\cdot\)K/(m/s)), which is represented as \(V\) (m/s). The \(U\) constants are dependent on where the solar array is located and how it is mounted. However, there is a lack of reliable data on its dependence on wind speed and direction [23]; this is especially true at higher wind speeds.  
\[U=U_{c}+U_{v}(V) \tag{3}\]  
As mention previously, thermal modeling for PV applications is difficult as often not enough information is known about local air flow. Although well-established empirical correlations have been developed for heated plates undergoing convection in order to quantify the mechanisms responsible for heat transfer, they require specialized knowledge and pose a challenge for PV designers.
# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 2 Materials and Methods  
### UAS Imagery  
During 2018 and 2019, fine-scale UAS aerial imagery was collected [25] as part of a walrus abundance estimation study. The aerial images (~2 cm resolution) were processed into orthoimages and the outlines of walrus aggregations resting on shore were digitized as geospatial polygons in a desktop GIS at a scale of 1:400, and full details of the UAS imagery collection, processing and interpretation are available within the metadata record of the data release [25]. UAS surveys were only flown when daylight and weather permitted (less than 15 knot winds and no precipitation). In total, 27 UAS surveys were completed, 13 in 2018 and 14 in 2019, including an unpublished survey of the beach without walruses present on 27 August 2019 (Alaska time; USGS unpublished data) with up to 2 surveys flown per day, which resulted in 8 flight days in 2018 and 9 in 2019.
## 2 Description of the Systems Under Investigation and Experimental Setups
This is expected since it is the most rigid assembly, as it lacks the flexibility a cut would otherwise give to the system. Next, the with-accelerometer cases show that as the cut width decreases, the frequencies also decrease among all modes in all systems. This trend is similar to what was shown in the no-accelerometer cases. Again, as expected, the no-cut case is shown to have the highest natural frequencies among all systems in every mode. Once both computational simulations were completed, a comparison of the natural frequencies in Table 3 was conducted, comparing the cases where no accelerometers are present on the systems to the cases where three accelerometers are present. It is noted that the presence of the accelerometers reduces the natural frequencies of each system in every mode due to the additional mass they add to the structure. As higher modes were observed, the accelerometers made even more of an impact, decreasing the natural frequency by as much as 14.2%, as seen in mode 5 of the 0.5\({}^{\prime\prime}\) cut case. The system where the accelerometers decreased the frequencies the most is the 0.1\({}^{\prime\prime}\) cut case, decreasing the natural frequencies by an average of 5.12%. This computational investigation shows the importance of introducing the accelerometers into FEA since, in doing so, greater agreement can be observed between the simulated data and what was observed when performing experimentation in the laboratory through various vibratory tests. From the initial observations made through FEA, the presence of the central cut seems to have little to no effect on the mode shapes of the system, but by comparing the cases in Table 3, the central-cut width obviously induces a linear softening effect when it is decreased. This can be determined by looking at the natural frequencies of each central cut, since as the cut width decreases, the natural frequency of each system decreases as well. The outlier is the no-cut system, but this is expected, since the system is the most rigid, as it lacks the flexibility the other systems have due to the central cut, and hence, it prohibits some coupling between the removable component and the box assembly. It should be noted that for the remainder of the study, only the simulations with accelerometers will be referenced, unless otherwise stated. Central-Cut Width Effects on the Dynamical Characteristics of the BARC System: Experimental Investigations  
In order to accurately examine the influence of the central-cut width on the dynamical properties of the BARC system, various vibratory tests were performed. These tests consisted of free vibration impact tests, random vibration tests, and harmonic tests. Each test had its own specific purpose, as previously discussed. This section goes deeper into
## 6 Analysis of the Algorithm
### Experiments on Simulated Datasets  
Aiming at solving the motif discovery problem, we implemented our algorithm in Java. Our tests were all done on a laptop with an Intel Dual Core 1.5 G CPU and 3.0 G Memory. In the first experiment, we tested our algorithm on several simulated datasets, which are all generated from our probability model with a small mutation rate. Each input set contains 20 or 15 sequences, which are of a length of 600 or 500 base pairs. Additionally, each bp of all the simulated gene sequences was generated independently with the same occurrence probability. A motif with a fixed length was randomly planted into each input sequence. The minimum Hamming distances between the results and consensus are recorded.
# _remote sensing_
## 4 Satellite Architecture and System Budgets
### Data Budget  
The amount of data generated by the HORUS instrument depends on the identified target angles. The selected HORUS implementation ensures the coverage of view angles equal to the ones of the NASA-JPL MISR payload indicated in Figure 5, but many other angles can be potentially covered on request.  
The daily data amount generated in one observation angle in one single spectral band is shown in Table 13, together with the data downlink time, in the assumption of 12 bits/pixel bit-depth and 300 Mbps communication channel.  
The actual daily data generation depends on the number of angles and spectral bands included in the observation campaign. The payload allows for large flexibility, and different operational plans can be implemented, according to selected scientific objectives.  
An evaluation of the system performance can be given, as an example, considering a daily acquisition plan including the nine observation angles of NASA-JPL MISR payload indicated in Figure 5, in all four spectral bands. The values for this observation plan are summarized in Table 14. In particular, the large amount of data generated, in the order of 260 Gbytes can be downloaded in less than 1 hour/day, relying on a very high latitude ground station, capable of reaching the spacecraft several times per day, or a ground segment based on a multiple ground stations' architecture.  
When computing the access times with reference locations for the ground stations, an example mid-latitude station will be able to communicate for approximately 30 min per day with the satellite, while high latitude stations would be able to communicate for up to 75 min per day, hence a daily data down link appears feasible by using an X-band network with a minimum configuration of one polar station or by multiple mid-latitude stations.
# A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
## 3 ILD Algorithm Mathematical Framework
### Perfect Bucket and Perfect Dataset  
Sometimes a bucket may contain only observations that are all in class 0 or 1. Such a bucket is called in this paper perfect bucket and is defined as follows.  
**Definition 3**.: _A feature bucket \(j\) is called perfect if one of the following is satisfied:_  
\[\begin{cases}m_{0}^{[j]}=0\\ m_{1}^{[j]}>0\end{cases} \tag{17}\]  
_or_  
\[\begin{cases}m_{0}^{[j]}>0\\ m_{1}^{[j]}=0\end{cases} \tag{18}\]  
It is also useful to define the set of all perfect buckets \(P\).  
**Definition 4**.: _The set of all perfect buckets, indicated with \(P\) is defined by_  
\[P\equiv\{B_{j}\ j=1,\ldots,N_{B}\left|(m_{0}^{[j]}=0\,and\,m_{1}^{[j]}>0)\, or\,(m_{0}^{[j]}>0\,and\,m_{1}^{[j]}=0)\right\} \tag{19}\]  
Note that by definition, the set \(B/P\) contains only imperfect buckets, namely, buckets where \(m_{0}^{[j]}>0\) and \(m_{1}^{[j]}>0\).  
An important special case is that of a perfect dataset, one in which all buckets are perfect. Indicating with \(B\) the set containing all buckets, we have \(B=P\). It is easy to see that we can create a prediction vector that will predict all cases perfectly, by simply choosing, for feature bucket \(j\)  
\[p^{[j]}=\begin{cases}0\text{ if }m_{0}^{[j]}>0\\ 1\text{ if }m_{1}^{[j]}>0.\end{cases} \tag{20}\]  
Remember that all feature buckets are perfect, and in a perfect feature bucket \(m_{0}^{[j]}\) and \(m_{1}^{[j]}\) cannot be greater than zero at the same time. To summaries our definitions we can define the following.  
**Definition 5**.: _A dataset \(B\) (where \(B\) is the dataset containing all feature buckets) is called perfect if \(B=P\)._  
**Definition 6**.: _A dataset \(B\) (where \(B\) is the dataset containing all feature buckets) is called imperfect if \(P\subset B\)._
## Materials and Methods
### Column Set-Up  
The experimental setup is shown in Figure 1. The experimental columns were employed to investigate the effects of inlet concentration, residence time, and heights on the removal of Cu(II). In order to simulate the use of lime sand bricks for the packing in the percolation facility, the plexiglass columns were employed with a size of 3 cm diameter and 20 cm height. The selected column height fits the design regulations of a permeable pavement by sponge city construction guide in China. In order to avoid sidewall effects as much as possible, the solution was injected from the bottom of the column using a peristaltic pump. Samples were taken at 5 cm, 10 cm, 15 cm, and 20 cm from the bottom of the column to investigate the removal of Cu(II) at different heights. Glass wool is added to each side of the columns to improve the flow rate of the water and prevent the loss of the adsorbent during the process. The quality of each pillar filled lime sand bricks was 195 g. The packing density was calculated to be 1.38 g/cm\({}^{3}\) based on the filling quality of the brick and the column size. The column porosity is achieved by controlling the flow rate of the peristaltic pump with a porosity of 50.03%. Water samples were collected from different outlets at different time intervals. The columns study was carried out at 25 \({}^{\circ}\)C. In order to ensure the exclusivity of the adsorption process, the pH of the metal solution was kept below the precipitation pH of the metal. Therefore, the pH of the initial solution was controlled to 2 by the addition of hydrochloric acid, which could ensure the pH of the effluent in the range of 5 to 6. The breakthrough curves and efficiency parameters for evaluating the adsorption performance of the fixed bed were obtained from the experimental data.  
\[q=\frac{V(C_{0}-C_{t})}{m} \tag{5}\]  
\[q_{t}=\frac{60C_{0}\cdot Q}{1000m}\int_{0}^{\infty}\biggl{(}1-\frac{C_{t}}{C_ {0}}\biggr{)}\mathrm{d}t \tag{6}\]  
where \(q\) (mg/g) is the adsorption capacity per unit mass of construction wastes at any one time; \(q_{\rm t}\) (mg/g) is the adsorption capacity per unit mass of construction wastes; \(V\) (L) is the volume of sample; \(C_{0}\) (mg/L) is the concentration of inlet Cu(II); \(Q\) (mL/min) is the inflow rate of the solution; \(m\) (g) is the adsorbent quality; \(C_{\rm t}\) (mg/L) is the outlet concentration over a certain period of time.
## 2 Research Methodology
### Magnetron Structure Analysis
#### 2.1.2 Interaction between Electrons and Fields  
An electron's motion in the interaction space depends on the orientation of the electric and magnetic fields. The magnetic field is perpendicular to the direction of electron motion, resulting in an electron trajectory in the form of a spiral path and in the direction of the electric field. Thus, both electric and magnetic fields are employed in the magnetron's operation and these fields exist in perpendicular directions so that they intersect. As a result, the magnetron is a crossed-field device. The crossed field is operated depending on the direction of each field, both of which exist at right angles to each other. In the crossed-field tube, electrons emitted from the cathode are accelerated by the electric field and gain velocity; however, the greater their velocity, the more their path is bent by the magnetic field. The electron charge is influenced by the electric and magnetic fields, which can be described as acting on the electron because of the presence of both fields due to the Lorentz force law and the equation of motion for an electron in cylindrical coordinated form, which are expressed as follows [13; 14; 15; 16]:  
\[\frac{\mathrm{d}^{2}\mathrm{r}}{\mathrm{d}t^{2}}-\mathrm{r}\left(\frac{\mathrm{d }\phi}{\mathrm{d}t}\right)^{2}=-\frac{\mathrm{e}}{\mathrm{m}}\left(\mathrm{E}_ {\mathrm{r}}+\mathrm{B}_{\mathrm{z}}\mathrm{r}\frac{\mathrm{d}\phi}{\mathrm{d }t}-\mathrm{B}_{\phi}\frac{\mathrm{d}\mathrm{z}}{\mathrm{d}t}\right), \tag{1}\]  
\[\mathrm{r}\frac{\mathrm{d}^{2}\phi}{\mathrm{d}t^{2}}=-\frac{\mathrm{e}}{ \mathrm{m}}\left(\mathrm{E}_{\phi}+\mathrm{B}_{\mathrm{r}}\frac{\mathrm{d} \mathrm{z}}{\mathrm{d}t}-\mathrm{B}_{\mathrm{z}}\frac{\mathrm{d}\mathrm{r}}{ \mathrm{d}t}\right), \tag{2}\]  
\[\frac{\mathrm{d}^{2}\mathrm{z}}{\mathrm{d}t^{2}}=-\frac{\mathrm{e}}{\mathrm{m} }\left(\mathrm{E}_{\mathrm{z}}+\mathrm{B}_{\phi}\frac{\mathrm{d}\mathrm{r}}{ \mathrm{d}t}-\mathrm{B}_{\mathrm{r}}\mathrm{r}\frac{\mathrm{d}\phi}{\mathrm{d }t}\right), \tag{3}\]
## 3 Materials and Methods
### Causal Diagram  
Causal diagrams are graphical tools that help represent system behavior and identify interactions between system variables. In causal diagrams, variables are connected to each other through arrows indicating cause-effect relationships (\(\mathrm{A}\rightarrow\mathrm{B}\)), which can be either positive or negative. In positive relationships, the values of the two variables (i.e., cause and effect) increase or decrease together; that is, an increase in the cause produces an increase in the effect, or a decrease in the cause leads to a  
Figure 1: Model analyzed in a BPE (Bottle Processing Enterprise) company.  
decrease in the effect. On the other hand, in negative relationships, an increase in the cause produces a decrease in the effect, or a decrease in the cause leads to an increase in the effect. In causal diagrams, either reinforcing loops or balancing loops, are generated when an effect impacts on a cause. Balancing loops attempt to eliminate an effect, whereas reinforcing loops destabilize the system because of their snowballing effect [39; 40].  
Figure 2 depicts the causal diagram of a typical industrial spare part procurement process. As can be observed, the diagram shows critical times and key variables in the process. Some of these variables include technical specifications review, purchase requisition time, requisition process, order approval, order to supplier, supplier lead time, and spare part inventory. Additionally, the diagram indicates that spare part shortages both influece production disruptions and affect finished goods inventory. The feedback loops of this system are described as follows:  
Loop R1. Variable Probability of Failure refers to the failure of any machine part in the production line, which triggers the activation of a spare part procurement process. This process involves different stages: technical specifications review, purchase requisition time, requisition process, order approval, order to supplier, and supplier lead time. Also, the procurement process is extensive and causes spare parts to arrive late in inventory, thus increasing the value of variable Scheduled Maintenance.  
Loops B1 and B2. If variable Total Time of Production Disruption increases as a result of factors involved in the process, variable Production Rate decreases. However, when Production Rate increases, parts in machines wear out more rapidly, thus increasing Probability of Failure and causing production disruptions.  
Loop B3. If Production Rate increases, Probability of Failure also increases. Conversely, if Production Rate decreases, Probability of Failure also decreases. Additionally, if Probability of Failure increases, Production Rate decreases.  
In the following section, we discuss the equations used to develop the simulation model, which is based on the causal diagram depicted in Figure 2.  
Figure 2: Causal loop diagram of the system for supplying special parts.