esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of the Control Algorithm for an Active Bearing Support
## 4 Active Adjustment of Bearing Preload
Figure 9: Diagram of the measurement track for a test stand with active regulation of bearing preload. 1—measuring card type 9264 from National Instrument, 2—amplifier for piezoactuators type SVR 150/3 from Piezomechanik GmbH, 3—piezoactuators type PSt 150/40/10 from Piezomechanik GmbH with a built-in strain gauge system, 4—vibration exciter type GW V20 from Data Physics, 5—PA 100E vibration exciter amplifier from Data Physics, 6—PC computer with a control application in LabVIEW, 7—QuantumX MX 840 measurement system from HBM, 8—tested spindle, 9—non-contact displacement sensor (IPPT design PAN Warszawa [25]), 10—C5160D-DB-PER32 high-speed motor from Teknomotor, 11—PC computer with a sound card to generate the forcing signal, 12—Sinamics V20 3 AC380-480V 7.5 kW inverter from Siemens, 13—switches (on/off, left/right, brake on/off). After selecting the appropriate controller settings, measurements were carried out for two rotational speeds: 500 and 1000 rpm. The range of rotational speeds was limited due to the way the replacement mass was placed on the spindle tip and the external forcing of the system. Between the replacement mass and the spindle, a bearing type 61903 from FAG was installed, which was mounted on the shaft through a press-fit connection. Another limitation was the time loop of the control algorithm. Due to the lack of a signal processor [26], the sampling frequency of the measurement signal and the determination of control values based on the while loop were significantly limited. The sampling frequency of the measurement signal was 900 Hz and the time of one measurement cycle was 120 s. Five repetitions were performed for each rotational speed. Then, the measurements were pre-averaged, obtaining data packages of 1080 samples. Research tests defined in this way were carried out for both shaft lengths (shafts A and B). Further analyses were carried out on the basis of the data prepared in this way. Examples of spindle tip displacement charts recorded using a non-contact sensor for both shaft lengths and the same rotational speed of 500 rpm are shown in Figure 11 for shaft A and Figure 12 for shaft B. We observed the average value from five trials for two time courses, without and with active control. We observed that the active system worked properly and reduced the amplitude of the spindle tip displacement. The graphs for the higher rotational speed of 1000 rpm were of a similar nature; therefore, they were not included in this article. However, they were used to conduct statistical analysis to confirm the correct operation of the control system. Figures 11 and 12 show two graphs of the spindle tip displacement amplitude. The measured value without the active control system is marked in red, and the measured value is marked in blue after the control is applied. We see that for each excitation value in the frequency range 150-450 Hz, there was a decrease in the value of the spindle tip displacement amplitude.
###### Abstract  
C-SAR/01 and C-SAR/02 serve as successors to the GF-3 satellite. They are designed to operate in tandem with GF-3, collectively forming a C-band synthetic aperture radar (SAR) satellite constellation. This constellation aims to achieve 1 m resolution imaging with a revisit rate of one day. It can effectively cater to various applications such as marine disaster prevention, monitoring marine dynamic environments, and supporting marine scientific research, disaster mitigation, environmental protection, and agriculture. Geometric correction plays a pivotal role in acquiring highly precise geographic location data for ground targets. The geometric positioning accuracy without control points signifies the SAR satellite's geometric performance. However, SAR images do not exhibit a straightforward image-point-object-point correspondence, unlike optical images. In this study, we introduce a novel approach employing high-precision automatic trihedral corner reflectors as ground control points (GCPs) to assess the geometric positioning accuracy of SAR images. A series of satellite-ground synchronization experiments was conducted at the Xilinhot SAR satellite calibration and validation site to evaluate the geometric positioning accuracy of different C-SAR image modes. Firstly, we calculated the azimuth and elevation angles of the corner reflectors based on satellite orbit parameters. During satellite transit, these corner reflectors were automatically adjusted to align with the radar-looking direction. We subsequently measured the exact longitude and latitude coordinates of the corner reflector vertex in situ using a high-precision real-time kinematics instrument. Next, we computed the theoretical image coordinates of the corner reflectors using the rational polynomial coefficients (RPC) model. After that, we determined the accurate position of the corner reflector in the Single Look Complex (SLC) SAR image using FFT interpolation and the sliding window method. Finally, we evaluated and validated the geometric positioning accuracy of C-SAR images by comparing the two coordinates. The preliminary results indicate that the positioning accuracy varies based on the satellite, imaging modes, and orbital directions. Nevertheless, for most sample points, the range positioning accuracy was better than 60 m, and the azimuth positioning accuracy was better than 80 m. These findings can serve as a valuable reference for subsequent applications of C-SAR satellites.  
C-SAR satellite; geometric correction; automatic corner reflector; RPC model; accuracy evaluation +
Footnote †: journal: remote sensing  
020231547444[[https://doi.org/10.3390/rs15194744](https://doi.org/10.3390/rs15194744)]([https://doi.org/10.3390/rs15194744](https://doi.org/10.3390/rs15194744))
## 2 Background and Related Works
### Medical Background  
Haematology and blood smear analysis fields have attracted the attention of researchers in the medical field over the years. In the context of PBS analysis supported by information technology tools, three main targets have been exploited [24]: malaria detection, leukaemia diagnosis and blood cells classification. The solutions proposed can vary according to the addressed task and considering that a PBS generally includes three main types of blood cells: thrombocytes, erythrocytes (RBC) and leukocytes (WBC). For example, from a medical point of view, the diagnosis of leukaemia requires one or more hematochemical analyses and correct history of the subject, with further confirmation by a bone marrow examination [25]. In general, there are four main groups of leukaemia: acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), chronic lymphocytic leukaemia (CLL) and chronic myeloid leukaemia (CML), as well as some less common types [26]. Each type of leukaemia affects a different type of blood cells that, in all cases, are overproduced by the bone marrow and released into the peripheral circulation when still immature [26]. Although the diagnosis could be long and tedious depending on the stage, the identification could be made much more immediately. Referring to the object of this study, indeed, the National Cancer Institute (NCI) has outlined some criteria for identifying the ALL, including age, the presence of related diseases, and the white blood cell count (WBCC) [27]. Moreover, the World Health Organisation (WHO) considers morphology and other complementary tests and molecular biology essential for the integrated diagnosis of ALL [28]. For these reasons, timely and accurate identification and diagnosis are crucial for effective disease management [29].  
As previously said in Section 1, this article discusses a particular case study involving leukocytes analysis geared toward predicting the type of leukaemia called acute lymphoblastic leukaemia. ALL is one of two leukaemia that affect lymphoid cells, which would develop into lymphocytes, and require in-depth analysis of blood leukocytes [30; 31]. More specifically, it is characterised by the development, in the bone marrow, of large numbers of immature lymphocytes, called lymphoblasts. They are then released prematurely into the bloodstream [26].
## 3 Transformer Encoder-Based BiLSTM Network Model
### BiLSTM Network  
Long Short-Term Memory (LSTM) networks, a specialized type of Recurrent Neural Network (RNN), are widely used for processing time-series data due to their ability to better capture correlations in sequential information. By incorporating input, forget, and output gate mechanisms, LSTMs regulate the flow of feature information, effectively addressing challenges such as gradient explosion and vanishing gradients that are common in traditional RNNs. The bidirectional LSTM (BiLSTM) network consists of two LSTM components: a forward LSTM that processes the input data in the forward direction, and a backward LSTM that processes the data in the reverse direction, thereby enhancing the network's ability to capture contextual information from both directions [20; 21; 22].  
The BiLSTM network captures both past and future information simultaneously, effectively modeling bidirectional dependencies in the input data, as illustrated in Figure 3. The input is processed by both the forward and backward BiLSTM components, and their outputs at each time step are concatenated to produce the final output \(y_{t}\).  
\[\overset{\rightarrow}{\mathbf{h}}_{t}=f\bigg{(}\overset{\rightarrow}{\mathbf{W}}_{x }x_{t}+\overset{\rightarrow}{\mathbf{W}}_{h}\overset{\rightarrow}{h}_{t-1}+ \overset{\rightarrow}{\mathbf{b}}_{h}\bigg{)} \tag{11}\]  
\[\overset{\leftarrow}{\mathbf{h}}_{t}= f\bigg{(}\overset{\leftarrow}{\mathbf{W}}_{x}x_{t}+\overset{ \leftarrow}{\mathbf{W}}_{h}\overset{\leftarrow}{h}_{t+1}+\overset{\leftarrow}{ \mathbf{b}}_{h}\bigg{)} \tag{12}\] \[y_{t}=\bigg{[}\overset{\rightarrow}{\mathbf{h}}_{t},\overset{ \leftarrow}{\mathbf{h}}_{t}\bigg{]} \tag{13}\]  
Figure 3: The structure of BiLSTM.  
Here, \(\overrightarrow{\mathbf{h}}_{t}\) and \(\overrightarrow{\mathbf{h}}_{t-1}\) represent the LSTM cell state vectors at the corresponding time steps for the forward propagation layer, respectively. \(\overleftarrow{\mathbf{h}}_{t}\) and \(\overleftarrow{\mathbf{h}}_{t+1}\) are the input weight matrices for the forward and backward propagation layers, respectively. \(\overrightarrow{\mathbf{W}}_{x}\) and \(\overleftarrow{\mathbf{W}}_{x}\) are the input weight matrices for the forward and backward propagation layers, respectively. \(\overrightarrow{\mathbf{W}}_{h}\) and \(\overleftarrow{\mathbf{W}}_{h}\) are the forget weight matrices for the forward and backward propagation layers, respectively. Finally, \(\overrightarrow{\mathbf{b}}_{h}\) and \(\overleftarrow{\mathbf{b}}_{h}\) are the biases for the forward and backward propagation layers, respectively.
# Three-Dimensional Printed Custom-Made Prostheses after Partial Scapullectomy: A Case Report
## 2 Detailed Case Description  
### Oncological Staging  
A 39-year-old patient presented to our center with a history of progressive pain in the left shoulder. Four years before the presentation of symptoms, the patient underwent a CT scan and MRI of the shoulder for pain with the occasional finding of a lytic area around the glenoid of the left scapula; the neoformation was not further examined before he arrived at our center. At the clinical examination, no masses were detectable by the scapula palpation. The patient had no functional limitations in extension, flexion, abduction/adduction, or shoulder rotation. The surrounding skin had a normal temperature, good sensation, and blood circulation of the left upper extremities, with no redness signs or ulceration. No lymph nodes were appreciable in the axilla and upper extremity. The only symptom was occasional pain around the joint.  
An X-ray of the left scapula was performed; an irregularity of the bone involving the glenoid area was detected and located in the S2 and partially the S1 region according to [PERSON]'s and Musculoskeletal Tumor Society classification [10; 11] (Figure 1).  
A CT scan of the shoulder indicated a lytic area extended from the glenoid cavity to the medial third of the scapula's body, infiltrating the acromion process partially with a dimension of 70 \(\times\) 45 mm (Figure 2).  
Figure 1: X-ray of the left scapula at the presentation.  
Comparing the CT scan with the previous one performed, the neoplastic process was stable, without further bone extension. A CT-guided needle biopsy was executed, and the histological results suggested low-grade central osteosarcoma (Figure 3).  
The absence of metastases of the lungs, mediastinum, abdomen, or bones was confirmed by a thoracoabdominal CT scan. The final diagnosis turns out to be left scapula low-grade central osteosarcoma, scapular sites S1 and S2, Enneking IA stage [12], grade 2. This case has been discussed collegially with a board composed of oncologists, oncological orthopedic surgeons, radiologists, and pathologists; in consideration of the low malignancy of the neoplasm, the anatomical part with only an area of neoplastic invasion, the high functional request for a young active patient, a total scaplectomy with humeral suspension would have been too demolitive of a choice. However, wide margins were necessary to reduce the risk of local recurrence of malignancy. A partial scapulectomy with a customized reconstruction was agreed upon as a treatment for this patient.  
Figure 3: Histologically, the lesion was composed of a hypocellular spindle cell proliferation with mild atypical nuclear features, in a collagenous matrix; parallel-aligned bone trabeculae were also present. The whole histological and genetical features were consistent with low-grade central osteosarcoma (Hematoxylin & Eosin, 20\(\times\)).  
Figure 2: CT scan of the left shoulder at the presentation.
## 5 Challenges, Open Research Problems, and Future of WBANs
### Bandwidth Problems
#### 5.1.4 Transport Layer Challenges  
WBANs deals with life-critical data, therefore a lost frame or packet of data can cause an alarm situation to be missed totally or misinterpreted. Therefore, reliable data delivery is needed. Although there are reliability procedures at different layers such as automatic repeat request at MAC layer, critical WBANs require total end-to-end reliability procedures [93]. The reliability for WBANs may need either packet level or event level solutions. For periodic traffic, packet level reliability is essential whereas for emergency event reporting such as a sudden fall detection then event reporting is more important than individual packet reporting. Consequently, we need to design a cross layer protocols for ensuring reliable delivery for different types of traffic is also important.  
The congestion and flow control procedures at the transport layer are also rare. For instance, the Event-to-Sink Reliable Transport protocol aims to guarantee delivery of enough numbers of packets of data. The Event-to-Sink Reliable Transport protocol is applicable for event reporting applications. On the other hand, for periodic physiological data reporting like electrocardiograms, or heart rate, every single packet has to be delivered reliably; therefore transport protocols addressing this issue still needed to be discussed and developed.
## 3 Results
### Bearing Capacity Analysis Results
This indicates that a linear approach does not adequately capture the complexity of the data. The quadratic model showed a higher value, showing a better value than the linear model. This improvement highlights the possible non-linear \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Coefficients 1a**} \\ \hline \multirow{4}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Standardized Coefficients**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**Sig.**} & \multicolumn{2}{c}{**95\% Confidence Interval for B**} \\ \cline{3-3} \cline{5-8}  & & & & & & **Lower Bound** & **Upper Bound** \\ \hline \multirow{4}{*}{1} & (Constant) & \(-\)16.430 & 124.229 & & \(-\)0.132 & 0.903 & \(-\)411.784 & 378.923 \\ \cline{2-8}  & Excavation level & 0.471 & 1.986 & 0.379 & 0.237 & 0.828 & \(-\)5.849 & 6.792 \\ \cline{2-8}  & Groundwater level & 2.202 & 6.519 & 1.298 & 0.338 & 0.758 & \(-\)18.545 & 22.950 \\ \cline{2-8}  & Unit weight & 10.094 & 35.070 & 0.683 & 0.288 & 0.792 & \(-\)101.514 & 121.702 \\ \cline{2-8}  & Water content & \(-\)0.088 & 0.194 & \(-\)0.428 & \(-\)0.454 & 0.681 & \(-\)0.705 & 0.529 \\ \cline{2-8}  & Sieve No. 10 & 0.125 & 0.122 & 0.879 & 1.029 & 0.379 & \(-\)0.262 & 0.512 \\ \cline{2-8}  & Sieve No. 200 & 0.272 & 0.202 & 0.937 & 1.346 & 0.271 & \(-\)0.371 & 0.915 \\ \cline{2-8}  & Internal friction angle & \(-\)1.628 & 12.123 & \(-\)3.138 & \(-\)0.134 & 0.902 & \(-\)40.208 & 36.951 \\ \cline{2-8}  & Cohesion & 0.110 & 1.045 & 2.160 & 0.106 & 0.923 & \(-\)3.214 & 3.435 \\ \hline \hline \end{tabular}  
*: Dependent Variable: Bearing Capacity.  
\end{table} 
Table 6: Multiple regression analysis results to predict Bearing Capacities. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**ANOVA a**} \\ \hline \multirow{2}{*}{1} & **Model** & **Sum of Squares** & **df** & **Mean Square** & **F** & **Sig.** \\ \cline{2-6}  & Regression & 4.279 & 8 & 0.535 & 0.320 & 0.912 b \\ \cline{2-6}  & Residual & 5.019 & 3 & 1.673 & & \\ \cline{2-6}  & Total & 9.298 & 11 & & & \\ \hline \hline \end{tabular}  
*: Dependent Variable: Bearing Capacity; b: Predictors: (Constant), cohesion, sieve no. 200, water content, excavation level, sieve no. 10, unit volume weight, groundwater level, internal friction angle.  
\end{table} 
Table 5: ANOVA results for Bearing Capacity. relationships between soil parameters and bearing capacity. The third-order model, which provided the best fit among the three, achieved the highest value. The performance of this model highlights the importance of higher-order relationships in accurately modeling soil bearing capacity. Comparison of these models reveals that the third-order estimation is the most suitable model to capture the interactions between soil parameters. Further investigation with a larger dataset and additional parameters may increase the robustness of these predictive models. Figure 4: Linear prediction model and R\({}^{2}\) value for Bearing Capacities. Figure 5: Quadratic prediction model and R\({}^{2}\) value for Bearing Capacities.
## 3 Compression Standards
### General Compression Schemes  
Results for general compressors (not image compressors) are shown in Table 5. These results have been computed dividing the total size in bits for all compressed files by the total number of pixels in the images. All compressors have been invoked in best compression mode. Best results in all datasets are obtained with Bzip2, which is especially efficient for the Yeast set.
# Extending Battery Lifetime by Avoiding High SOC
## 5 Results
### User Case Study  
It is obvious that using a larger battery in relation to the vehicle size will increase the lifetime. The result of the lifetime from the two user cases, for the three different vehicles, is presented in Figure 10. As expected, the longest lifetime is achieved for Vehicle 2, which has the largest battery in relation to the vehicle size. Vehicle 3, which has the smallest battery, in relation to the vehicle size, ageing.  
In Figure 10a, the spread in lifetime for the vehicles is very large. The difference is mainly related to the C-rate, though also to the DOD and the SOC placement of the DOD. Vehicle 3 has the highest C-rate, however by not fully charging the battery (only to 62% SOC) when driving the shorter trips, the lifetime can be improved with more that 1.4 years, 59%, compared to Case 3 ref. For Vehicles 1 and 2, this difference is even larger in number of years, showing that the placement of the DOD highly affects the lifetime, increasing the lifetime with 44% and 56%, respectively.  
In the second user case, Figure 10b, the C-rates are lower as mixed driving is assumed for the trips during the weekdays. Here, the DOD is also smaller and all vehicles can start at 50% SOC. The improvement in lifetime by placing the DOD at a lower SOC level is even more prominent here, especially for Vehicle 3 with an improved lifetime of 130%. Vehicles 1 and 2 each have a 70% lifetime increase.  
Figure 10: Model result for the three different vehicles at 30 \({}^{\circ}\)C while used according to **(a)** User 1; and **(b)** User 2.  
Figure 9: Ageing for the three vehicles while assuming driving according to, **(a)** WLTC medium; **(b)** WLTC; and **(c)** WLTC Extra High for five full charge/discharges per week all year around with a battery temperate of 30 \({}^{\circ}\)C.
## 2 Materials and Methods
### Quantification Speed Test  
The speed of quantifying optical and perfusion properties using the two-stage LUT was evaluated based on a set of simulated diffuse reflectance images (Sections 2.3.1 and 2.3.2). The performance of the stage-2 LUT was compared to the Beer-Lambert Law-based quantification (Section 2.3.3). All quantification methods were implemented on both a CPU and GPU to fully evaluate their efficiency. GPU acceleration was achieved by simply converting a regular MATLAB array into a GPU-compatible array with the MATLAB function gpuArray(). The testing platform consisted of an Intel CPU (i9 10900K), 32 GB of memory, and a Nvidia GPU (RTX 3080). A speed test script was written in MATLAB, and only the built-in MATLAB functions were used. We performed a speed test on three sets of simulated images with resolutions of 512 \(\times\) 512 pixels, 1024 \(\times\) 1024 pixels, and 2048 \(\times\) 2048 pixels. Each set of images consisted of 50 images. The three image sizes are later referred to as small, medium, and large sizes, respectively.
# Semi-Analytical Planetary Landing Guidance with Constraint Equations Using Model Predictive Control
## 2 Problem Description and Decoupled Trajectory Planning  
In this section, we transform the planetary landing guidance into an optimal control problem and introduce a three-dimensional decoupling method. In Section 2.1, we review the one-dimensional problem, and the optimal control mode which consists of either full thrust or a period of zero thrust followed by full thrust is obtained [26]. In Section 2.2, we apply the one-dimensional optimal control mode in the three-dimensional problem and assume that the proportion of the thrust for horizontal control is fixed after ignition. Based on [PERSON]'s maximum principle, we derive that the direction of horizontal thrust changes at most once after ignition. This decoupling method significantly reduces the difficulty of the three-dimensional analytical derivation, despite sacrificing some optimality and control smoothness.