esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Ultrasound PIV System and Its Principle  
The ultrasound PIV system mainly contains an ultrasound imaging device, two probes, an interface program, and a related physical model. The two probes are convex array or linear array, which are used to connect to the host machine of the ultrasound imaging device. They are used to generate a 2D ultrasonic image by pulsing the array elements in the probe at different times [26; 27]. Each array element transmits an ultrasonic pulse into the flow, and sand particles in the flow can reflect ultrasonic echoes to the array elements where they can be recorded [28]. The amplitudes of the reflected ultrasonic echoes and transmission time delays are used to create a series of ultrasonic images [29; 30]. The time delays are used to determine the spatial position of the sand particles in the flow, and the amplitudes are used to assign the reflected intensity of the particles. To obtain the ultrasonic image, the needed time is determined by the convex array probe when it has finished the transmission and reflection of all array elements. The acquisition and analysis process of the ultrasonic image is shown in Figure 1. Then, these ultrasonic images are used to analyze and calculate the flow velocity vectors in silt carrying flow through the interface program.  
Just as with optical PIV methods, the measuring principle of the ultrasound PIV system is also based on the basic principle of cross-correlation shown in Figure 2. The number of pixels in the ultrasonic image is generally smaller than in the optical image, so the spatial resolution should be increased. As for the limitation of the ultrasound PIV system when applied to flow velocity measurement, the dynamic range of ultrasonic waves is the primary limitation. The frame rate of the formed ultrasonic image also has the limitation of image acquisition speed because of needing more time to generate high-resolution ultrasonic images for the device when compared with the optical PIV method. Therefore, higher frame rates and higher image resolution systems can be more available, and the actual application can be more accurate and welcome. In this paper, an improved measurement system that can enhance the measured accuracy of ultrasonic images is described [31] and provides a novel method based on the cross-correlation principle through discrete window offset [32]. This approach can make full use of the deformation of the interrogation window and rebuild this translation by reconsidering the rotation and shear of imaging particles. The displacement and detected offset in the ultrasonic image can be obtained and corrected using some iterative steps through the multi-grid deformation technique.
## 2 Methods
### Data Preparation and Classification  
The classification module (Figure 6) was used to classify the elements in the MRA dataset into either normal, prehypertensive, or hypertensive subjects. The inputs for this module were the feature vectors built in module 2. We classify subjects based on these features for two reasons: (1) these features were clinically observed to precede the onset of hypertension, (2) we wanted to prove the efficacy of using these features to predict hypertension before its symptomatic onset. Using supervised learning techniques, classification results could compare predicted labels to actual labels. The reported classification accuracy would measure the efficacy of using the selected vascular features in the early detection of hypertension. Each feature vector was built using values that represent the change in vascular diameters, vascular tortuosity, and differences between blood pressure measurements taken at two time points(\(t_{baseline}\), \(t_{followup}\)) separated by approximately 700 days. Blood pressure measurement information from both the baseline and the follow-up have been used in the classification process to determine whether a change in the blood pressure has occurred or not (either decrease or increase). This was done by incorporating the differences between blood pressure measurements taken at baseline and follow-up with a sign (\(-/+\)) included to determine if the change was an increase or a decrease. For instance, in Table 1, delta MAP (Mean Arterial Pressure) refers to the difference between MAP values at \(t_{baseline}\) and \(t_{followup}\). Similarly, delta SBP/delta DBP refers to the difference of the systolic BP/diastolic BP values at \(t_{baseline}\) and \(t_{followup}\). Some statistical values (i.e., average, and median) were also calculated for vascular diameter and tortuosity changes and included in each feature vector. Additionally, values in feature vectors have gone through a normalization process to remove any outliers or biasing and to enhance the homogeneity of data. Feature normalization was performed using the scaling (min-max) method that scales the values of each feature in a range between the minimum and maximum values of that feature for all dataset subjects. Each feature vector was assigned a label that indicates to which class each specific subject belongs. The three class labels are: normal, prehypertensive, and hypertensive, and were defined based on the blood pressure readings and the 2017 hypertension guidelines [3]. Different linear and non-linear classifiers with different parameters and validation scenarios were used in this module to test the accuracy of the classification based on the cerebral vascular changes occurring over the 700 day period.  
Figure 5: Module 2: Estimation of descriptive vascular features and building feature vectors.  
Figure 6: Module 3: Classification of data into normal, prehypertensive, and hypertensive.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 3 Characteristics of the LTE-U System
While cellular network providers tend to rely on radio spectrum for which they have exclusive licenses, LTE-U would share space with Wi-Fi devices already existing there (smartphones, laptops, and tablets connecting to home broadband networks, free hotspots provided by companies, etc.). The LTE-U is designed to enable cellular networks to increase data transmission speeds over short distances without having to use a separate Wi-Fi network. Unlike Wi-Fi connections, there is a control channel using LTE, but all data (not only phone calls) flow through the unlicensed 5 GHz band [12].
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
###### Abstract  
This paper develops a novel communication method for an ultra-high-speed train that runs in an evacuated tube. The proposed method significantly reduces the number of needed base stations to provide adequate coverage and data rates. Moreover, the time connectivity for each base station was enhanced considerably. The proposed method can provide improvements in terms of transmitted power and received power, either fixed or variable; this method offers a fixed or variant data rate. Moreover, the paper studies the effects of the divergence angle on transmitted and received power. Additionally, the proposed communication procedure might produce a system with a fixed data rate, such as 1.25 Gbps. It can also create a design with adaptive divergence angles (that can be altered dynamically) depending on the train distance to the base station. The results show that this method is promising for working for an ultra-high-speed train that runs in an evacuated tube. It can reduce the base stations number from 500 to less than 10 base stations with respect to the data rate and power consumption. Furthermore, a new handover method is proposed and addressed in this work.  
free space optics; FSO; ultra-high-speed train; evacuated tube transportation +
Footnote †: journal: 2022, 12, 8545, [[https://doi.org/10.3390/app12178545](https://doi.org/10.3390/app12178545)]([https://doi.org/10.3390/app12178545](https://doi.org/10.3390/app12178545))
## 3 Results
### Applying RF Model  
The outcomes of the confusion matrix for RF model are shown in Table 4. The result shows that the model predicted 2487 non-gullies as non-gullies and 256 non-gullies as gully. On the other hand, the RF model predicted 2677 gullies as gullies and 66 gullies as non-gullies. Moreover, the out-of-bag error (OOB) for RF was 5.82%. This means that the model has a precision of 94.18%, which expresses the excellent accuracy of the model in predicting gully erosion.
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Ship Arrivals and Departures
#### 4.4.4 Solution  
Before entering the port area, the ship is required to contact the port authority regarding its estimated time of arrival (ETA), give particulars of the ship and her cargo, check for berth availability, and request a marine pilot to direct it to the port area. The ship may also require the assistance of a tugboat for maneuvering inside the port area and completing entrance and mooring operations. Similar operations are carried out when the ship leaves the port area [53].  
All berthing, unberthing, and maneuvering of vessels within the port limits must be done under the supervision of a marine pilot. It is the duty of the Harbor Master to inform the pilot of any special conditions, difficulties, or weather conditions.
# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
## 4 Experimental Results and Analysis
### Analysis of Target Detection Experiment Results
#### 4.3.2 AP Detection Accuracy Comparison  
Comparing the data in Table 4, it can be seen that compared with Faster RCNN, CF-RCNN's detection accuracy \(\text{AP}_{50}\) increases by 13.9 percentage points to 76.2%, and the average processing time is slightly lower than that of Faster RCNN, but it can still meet the real-time requirements. In order to verify the impact of the improvement measures on the detection ability of small-sized objects, objects smaller than 32 \(\times\) 32 pixels are selected as the experimental dataset to compare the detection accuracy. The AP value is the average AP accuracy achieved for 10 different IOU thresholds of 0.50:0.05:0.95. The results are shown in Table 5.  
It can be seen from Table 5 that after FPN and Soft-NMS are introduced into the Faster RCNN model, the AP value for small-sized object detection is increased by 15.4 percentage points, which alleviates the weak detection performance of the target detection algorithm based on small-sized objects.
# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
###### Abstract  
In microscopy, laboratory tests make use of cell counters or flow cytometers to perform tests on blood cells, like the complete blood count, rapidly. However, a manual blood smear examination is still needed to verify the counter results and to monitor patients under therapy. Moreover, the manual inspection permits the description of the cells' appearance, as well as any abnormalities. Unfortunately, manual analysis is long and tedious, and its result can be subjective and error-prone. Nevertheless, using image processing techniques, it is possible to automate the entire workflow, both reducing the operators' workload and improving the diagnosis results. In this paper, we propose a novel method for recognizing white blood cells from microscopic blood images and classify them as healthy or affected by leukemia. The presented system is tested on public datasets for leukemia detection, the SMC-IDB, the IUMS-IDB, and the ALL-IDB. The results are promising, achieving 100% accuracy for the first two datasets and 99.7% for the ALL-IDB in white cells detection and 94.1% in leukemia classification, outperforming the state-of-the-art.  
leukocyte detection; leukemia classification; blob detection; deep learning Article
## 3 Problem Formulation
### Inner Model: Stochastic Optimal Dispatching Model of IPGES
#### 3.1.2 Power System Operating Constraints  
The stochastic optimal dispatch model of IPGES takes into account the power grid security constraints. The power system adopts the DC power flow model [15]. The main constraints are as follows.  
Figure 3: Diagram of the integrated power and natural gas energy system with P2G.  
1. Generator output constraint \[u_{i}^{state}P_{i,\min}\leq P_{i,t}\leq u_{i}^{state}P_{i,\max}\ \ ;\ 1\leq i\leq N_{c}+N_{g}\] (7) 2. Unit climbing constraint \[-R_{i}^{down}\leq P_{i,t+1}-P_{i,t}\leq R_{i}^{up}\ \ ;\ 1\leq i\leq N_{c}+N_{g}\] (8) 3. System reserve constraint As wind power output is difficult to accurately predict, the system needs to reserve a certain amount of upward reserve and downward reserve adjustment capabilities to cope with the uncertainty of wind power output. \[\sum\limits_{i=1}^{N_{w}}(P_{s,t}^{up}-P_{i,t}^{up}-\Delta P_{i,t}^{up,up}) \leq\sum\limits_{i=1}^{N_{c}+N_{g}}\min[R_{i}^{down},(P_{i,t}-u_{i}^{state}P_ {i,\min})]\ \ ;\ 1\leq t\leq T,\forall s\in S_{set}\] (9) \[\sum\limits_{i=1}^{N_{w}}(P_{i,t}^{up}-P_{s,t}^{up})\sum\limits_{i=1}^{N_{ c}+N_{g}}\min[R_{i}^{up},(u_{i}^{state}P_{i,\max}-P_{i,t})]\ \ ;\ 1\leq t\leq T,\forall s\in S_{set}\] (10) where \(P_{s,t}^{up}\) represents the random output of wind power in scenario \(s\), which can be obtained by the method described in Section 4.1. 4. Power flow constraint For the transmission network level, DC power flow constraint should be adopted for branch power flow. \[P_{t}^{l}=-B_{mn}(\theta_{m,t}-\theta_{n,t})\ \ ;\ 1\leq t\leq T,1\leq l\leq N_{br}\] (11) \[\theta_{m,\min}\leq\theta_{m,t}\leq\theta_{m,\max}\ \ ;\ 1\leq t\leq T,1\leq m\leq N_{bus}\] (12) 5. Line safety constraint \[-k_{safe}P_{j,\max}\leq P_{j,t}\leq k_{safe}P_{j,\max}\ \ ;\ 1\leq t\leq T,1\leq l\leq N_{br}\] (13) 6. Node power balance constraint \[H_{i}^{c}P_{t}^{c}+H_{i}^{g}P_{t}^{g}+H_{i}^{l}P_{t}^{l}+H_{i}^{up}P_{t}^{up} -H_{i}^{p2g}P_{t}^{p2g}=H_{i}^{D}P_{t}^{D}\ \ ;\ 1\leq t\leq T,1\leq i\leq N_{bus}\] (14)
## 3 Results
### Inversion Using Various Solutions  
To overcome the ill-posedness of the model inversion and to enhance the LAI retrieval, the use of multiple solutions were examined. The significance of using multiple solutions instead of a single solution corresponding to the minimum RMSE has been shown in our earlier studies [24; 86]. Hence we further assessed how the use of the best 10, 50, and 100 solutions from the RapidEye and Sentinel-2 LUTs would affect the retrieval accuracy of LAI. The relationship between measured and retrieved LAI values using alternative solutions was then studied in both datasets using the R\({}^{2}\), RMSE, and NRMSE values. The results of these analyses are demonstrated in Table 3. In general, using multiple solutions, higher  
Figure 6: The average absolute errors (AAEs) calculated between RapidEye and Sentinel-2 measured reflectance and their corresponding best-fit reflectance from LUTs. The AAE has been calculated from RapidEye (n = 30) and Sentinel-2 (n = 20) reflectance and their corresponding best fit reflectance from the LUTs, respectively. The AAE units are reflectance.  
Figure 7: Measured and retrieved LAI using RapidEye (2015, n = 30) and Sentinel-2 (2016, n = 20) data (smallest root means square error (RMSE) criterion). The black line is the 1:1 relationship and the pink line presents the relationship between the retrieved and measured LAI.  
retrieval accuracies were observed for all alternative solutions from Sentinel-2 data in comparison to those from RapidEye. As can be realised from the table, using the RapidEye and Sentinel-2 data and the mean of the first 10 best solutions resulted in higher R\({}^{2}\) (0.51 and 0.59) and lower NRMSE (0.24 and 0.16), respectively compared to the use of minimum RMSE. Using the mean of the best 50 and 100 solutions in both datasets decreased the R\({}^{2}\) values between measured and estimated LAI with almost no effect on their NRMSE values.
## 3 Experiments
### Experimental Setup  
Based on the new collected bare land samples and image data of the experimental areas, the model training and change detection experiments were carried out firstly. In order to obtain relatively ideal initial change parcels, the efficiency and accuracy of the proposed deep Siamese CNN change detection model was evaluated from the aspects of backbone network selection, hyper-parameter adjustment, etc.  
The change detection results of the CNN model are evaluated in the standard metric mean intersection over union (MIoU). As shown in (6), the MIoU is the ratio between the intersection and union of the two datasets, which represent the ground truth and predicted change area, respectively:  
\[\text{MIoU}=\frac{1}{k+1}\sum_{i=0}^{k}\frac{P_{ii}}{\sum_{j=0}^{k}P_{ij}+ \sum_{j=0}^{k}P_{ji}-P_{ii}} \tag{6}\]  
where \(k\) denotes the number of classes, \(i,j\) denotes a specific class, \(P_{ii}\) represents the pixel number of TPs, and \(P_{ij}\) and \(P_{ji}\) denote FPs and FNs, respectively. Moreover, TP, FP, and FN are the number of true positives, false positives, and false negatives, respectively. The MIoU is calculated on a per-class basis and then averaged. In this paper, the change detection task is mainly divided into two classes, i.e., change and unchanged.  
To verify the effects of the pre-processing and post-processing of new detected bare land parcels proposed in this study, experiments such as confidence calculation, proximity calculation and discrimination, and comprehensive post-processing were carried out, and then the results were analyzed. Furthermore, a comparison experiment between the proposed aggregation method and that of the ArcGIS aggregation tool was designed to analyze the effect of parcel aggregation and ascertain the advantages of the proposed proximity evaluation model and aggregation criteria.  
To evaluate the improvement between the parcels processed by the proposed optimizing method and the original automatic extraction results in terms of accuracy and data volume, a comparison test between the experimental results and the true value from manually labeled was conducted. The number of parcels, false and missed detection rates before and after processing, were calculated to determine the final accuracy.  
As shown in (7), the false detection rate is calculated as follows:  
\[\text{FDR}=(\text{A}-\text{C})/\text{A}*100\% \tag{7}\]  
Additionally, the missed detection rate is calculated as follows:  
\[\text{MDR}=(\text{B}-\text{C})/\text{A}*100\% \tag{8}\]  
where A denotes the number of change parcels in different processes output by the experiments, B denotes the number of manually labeled parcels, and C denotes the number of intersecting parcels between A and B.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 2 CAF and Secure CAF  
### Caf  
As the first step, we review existing results for secure CAF. For this aim, we prepare an important notation. The symbol \(\oplus\) expresses the arithmetic sum over a finite field, and the symbol \(+\) denotes the sum over the real numbers. A typical setting for secure CAF has two transmitters, \(V_{1}\) and \(V_{2}\), and one receiver, \(R\). Suppose that Transmitter \(V_{i}\) has message \(M_{i}\in\mathbb{F}_{q}\), and Receiver \(R\) is linked by a (noisy) MAC that has two input variables from the two transmitters \(V_{1}\) and \(V_{2}\). In this scheme, Receiver \(R\) is required to obtain the modulo sum \(M_{1}\oplus M_{2}\) via the (noisy) MAC, as depicted in Figure 1.  
Many papers proposed a protocol for CAF over a Gaussian MAC. Suppose that the transmitter \(V_{i}\) sends the complex-valued variable \(X_{i}\) for \(i=1,2\). When the channel fading coefficients are given as \(h_{1},h_{2}\in\mathbb{C}\), Receiver \(R\) receives the complex-valued variable \(Y\) as:  
\[Y=h_{1}X_{1}+h_{2}X_{2}+N, \tag{1}\]  
where \(N\) is a complex Gaussian random variable with zero mean and a variance of one. The remaining part of this section assumes multiple uses of the above Gaussian MAC.  
References [24; 33; 34] obtained an achievable rate under the energy constraint by using lattice codes. This rate is called the computation rate. Here, to seek a practical scheme, we consider the BPSK scheme, in which \(X_{i}\) is coded to \((-1)^{A_{i}}\) with \(A_{i}\in\mathbb{F}_{2}\). Hence, (1) can be rewritten as:  
\[Y=h_{1}(-1)^{A_{1}}+h_{2}(-1)^{A_{2}}+N. \tag{2}\]  
The reference [35] showed that the rate \(I(Y;A_{1}\oplus A_{2})_{\text{Equation}(2)}\) is achieved when the task of CAF is imposed, where the mutual information is given by the independent and uniform random numbers \(A_{1}\) and \(A_{2}\). (More precisely, the quantity\(I(Y;A_{1}\oplus A_{2})_{\text{Equation}(2)}\) is defined as the mutual information when \(A_{1}\) and \(A_{2}\) are independently subject to the uniform distribution. This rule will be applied later when the equation number such as Equation (2) is given as a subscript of a mutual information). Then, references [29; 30]  
Figure 1: CAF (Computation-and-forward).  
studied LDPC codes, in particular, spatial coupling LDPC codes and regular LDPC codes, to achieve this task under the BPSK scheme. In fact, the method introduced by references [29; 30] can be efficiently implemented with a rate close to \(I(Y;A_{1}\oplus A_{2})_{\text{Equation}(2)}\). Furthermore, the recent reference [36] studied its quantum extension.
## 3 Computational Methodology
### Energy Efficiency Proposal Using Passive and Active Technologies  
The integration of passive and active technologies search reduces CO2 emissions and improve the medical center's electrical energy performance. In the case of passive systems, the literature reports various strategies to improve the energy efficiency of buildings under hot or tropical climate conditions [30]. In this work, we opted for assess thermal insulation panels, reflective coating, and green roofs because they are readily available materials in  
Figure 5: Comparison between measured electricity consumption data and calibrated results from the outpatient medical center. the region, require few or no structural modifications, and thermally have demonstrated advantages of use at high temperatures. Thermal insulation using construction material was evaluated considering the expanded polystyrene (EPS) panel, a light, rigid, and formable thermoplastic. These panels are installed on the roof using plugs or adhesives and are protected with a fiberglass mesh covered with plaster. Although the labor of this material is not expensive, the price of the panels and masonry work considerably increases the initial investment cost [31]. In the case of reflective coatings, these are special paint (usually white) designed to be applied on the envelope and increase the reflectance of the roof and walls. This represents the most direct way to reduce incident solar energy and is considered the most straightforward passive measure because most of these coatings can be installed the same way as regular paint, which implies reduced implementation and labor costs [32]. In the case of green roofs, the structure consists in vegetation, soil layer, filter and drain membrane, root barrier and waterproof layer (Figure 6) The vegetation type affects the leaf area index (LAI), leaf reflectivity, leaf emissivity, and stomatal resistance [33; 34]. For this study, the green roof analyzed is one of the most applied in Mexico, considering a LAI of 3.75, the reflectivity of 0.25, emissivity of 0.9, and a stomatal resistance of 175 [34]. Table 4 summarizes the optical and thermophysical properties of the materials considered, as well as their market and installation prices. The three materials were assessed on the calibrated model considering their application on the roof (and the exterior walls for the cases of reflective coating). Regarding active strategies, replacing conventional air conditioning equipment (on-off technology) with inverter technology was considered an energy efficiency measure. Based on this, the improvement in energy performance and economic savings were evaluated. This strategy was combined with the passive alternatives to identify the best cost-energy choice. Table 4 also contains detailed information on the current and replacement devices and their respective technological replacement costs. Figure 6: Typical structure of green roof layers.