esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Classification Metrics  
In the test phase, the classification accuracy of our model was evaluated by using test data. An answer was considered correct when an image of a differentiated cell was classified as such (true positive, TP) or an image of an undifferentiated cell was classified as such (true negative, TN). An answer was considered incorrect when an image of an undifferentiated cell was classified as a differentiated cell (false positive, FP) or an image of a differentiated cell was classified as an undifferentiated cell (false negative, FN). As evaluation metrics, we used average classification accuracy (ACA), recall, precision, specificity, and F-measure, which were defined as follows:  
\[\mathrm{ACA}=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\[\mathrm{Recall}=\frac{TP}{TP+FN} \tag{3}\]  
\[\mathrm{Precision}=\frac{TP}{TP+FP} \tag{4}\]  
\[\mathrm{Specificity}=\frac{TN}{FP+TN} \tag{5}\]  
\[\mathrm{F-measure}=\frac{2\cdot\mathrm{Recall}\cdot\mathrm{Precision}}{ \mathrm{Recall}+\mathrm{Precision}} \tag{6}\]  
We set the highest ACA score during epoch for each cross-validation. At this epoch, we calculated recall, precision, specificity, and F-measure for each cross-validation. We implemented our CNN model in Python 3.6 and used Chainer [27], which is an open-source deep-learning framework. We used an NVIDIA Tesla P100 (1189 MHz, 9.3 TFLOPS) graphics processing unit for the learning and classification calculations; the P100 was connected to Reedbush-H, the calculation server of the University of Tokyo Information Infrastructure Center, Tokyo, Japan.
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 3 Methods
### Statistical Evaluation of GPPs against Gauge Observations  
In order to analyze the performance of GPPs in capturing precipitation, three types of indices were used:  
1. [label=()]
2. Four basic statistical indicators, including correlation coefficient (CC), standard error (SE), root-mean-squared deviation (RMSD), mean absolute error (MAE), and percentage bias (PBIAS), all computed at different temporal and spatial scales (Table 3).
3. Evaluation of the capability of the GPPs to detect rain and non-rain days, which plays an important role in hydrological applications [39]. Therefore, in this study, three indicators--including probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR)--were employed (Table 3). POD is typically used to describe the proportions of rainy days that are correctly detected by GPPs to the total observations [40]; CSI reflects the overall proportion of rainfall events that are correctly detected by GPPs; and FAR describes the proportions of rainy days that are not recorded by the rain gauges to the total observations.
4. Evaluation of the variability and distributions of the GPPs following different rainfall intensities by classifying five daily rainfall thresholds: (1) rain \(\leq\) 0.1 mm (no rain); (2) 0.1 \(<\) rain \(\leq\) 1 mm (little rain); (3) 1 \(<\) rain \(\leq\) 20 mm (light rain); (4) 20 \(<\) rain \(\leq\) 50 mm (moderate rain); (5) rain \(>\) 50 mm (heavy rain) [41].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **Longitude (Degree)** & **Latitude (Degree)** & **Area (km\({}^{2}\))** & **\% of Whole Basin** \\ \hline Chiang Saen & 100.08 & 20.27 & 189,000 & 23.8 \\ Luang Prabang & 102.14 & 19.89 & 268,000 & 33.7 \\ Nong Khai & 102.72 & 17.88 & 302,000 & 38.0 \\ Mukhdan & 104.74 & 16.54 & 391,000 & 49.2 \\ Pakse & 105.80 & 15.12 & 545,000 & 68.6 \\ Stung Treng & 106.02 & 13.55 & 635,000 & 79.9 \\ Total & & & 795,000 & 100 \\ \hline \hline \end{tabular} Note: Data Source: MRC [33].  
\end{table}
Table 2: The hydrological stations used for the Soil and Water Assessment Tool (SWAT) model’s calibration.
## 3 Proposed Strategy
### Main Contribution  
According to Equations (6)-(10), the closed-form solutions of the optimal sensing threshold at the FC for the N-out-of-K based CSS under the HCRN situations cannot be obtained in the case where local sensing results are independent and non-identically distributed. As a result, reasonable approximations for the false alarm probability and detection probability in Equations (8) and (9) are needed to obtain the optimal solution on the sensing threshold at the FC.  
In this paper, the detection probability and its corresponding false alarm probability in Equation (8) and (9) are respectively approximate to two Poisson distributions [33] as it is shown in Equations (11)-(14):  
\[P_{F}=P(X>k_{0}|H_{0})=\sum_{k=k_{0}}^{K}\frac{\lambda_{F}^{k}}{k!}e^{-\lambda _{F}}, \tag{11}\]  
\[P_{D}=P(X>k_{0}|H_{1})=\sum_{k=k_{0}}^{K}\frac{\lambda_{D}^{k}}{k!}e^{-\lambda _{D}}, \tag{12}\]  
\[\lambda_{F}=\sum_{i=1}^{K}P_{f-i}^{FC}, \tag{13}\]  
\[\lambda_{D}=\sum_{i=1}^{K}P_{d-i}^{FC}, \tag{14}\]  
where \(k_{0}\) denotes the sensing threshold of the N-out-of-K rule at the FC. \(\lambda_{F}\) and \(\lambda_{D}\) are the corresponding parameters of the two Poisson distributions. The approximation error will be discussed in the next section.  
Then, the sensing threshold \(k_{0}\) can be obtained by minimizing the error probability (the sum of false alarm probability and missed detection probability). The optimal sensing threshold \(k_{0}\) can be obtained by the convex optimization and \(k_{0}\) can be written as it is shown in Equation (15):  
\[k_{0}=\bigg{\lfloor}\frac{(\lambda_{F}-\lambda_{D})}{\ln(\lambda_{F}/\lambda_ {D})}\bigg{\rfloor}, \tag{15}\]  
where \(\lfloor\cdot\rfloor\) denotes the rounding up function.
# High Pressure and Pasteurization Effects on Dairy Cream +
## 2 Materials and Methods
### pH and Color  
The pH of all cream samples was measured at room temperature (21 \(\pm\) 2 \({}^{\circ}\)C) in triplicate with a glass electrode (pH electrode 50 14, Crison Instruments, S.A., Barcelona, Spain).  
The color was assessed using a Konica Minolta CM 2300d (Konica Minolta, Osaka, Japan) spectrophotometer on three random spots per sample, recorded according to the CIELab system, and the data were processed with the SpectraMagicTM \(\mathrm{N}\mathrm{x}\) software (Konica Minolta, Osaka, Japan). The obtained parameters were L*-lightness, a*-redness, and b*-yellowness. The total color difference (\(\mathrm{\Delta E}^{*}\)) was calculated using Equation (1) [16].  
\[\mathrm{\Delta E}^{*}=[(\mathrm{L}^{*}\cdot\mathrm{L}^{*}_{0})^{2}+(\mathrm{a} ^{*}\cdot\mathrm{a}^{*}_{0})^{2}+(\mathrm{b}^{*}\cdot\mathrm{b}^{*}_{0})^{2}]^{ 1/2} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **HPP Conditions** & **Storage Period** & **Nomenclature** & \multicolumn{2}{c}{**To Study the Effect of HPP after Processing and during Each Storage Period On:**} \\ \cline{2-5}  & **Pressure (MPa)** & **Duration (min)** & **(Days)** & **Nonenclature** & **General microbiology (TAP, LAB, and ENT) and physicochemical parameters \\ \hline \multirow{3}{*}{First batch} & - & - & - & Raw & \multirow{3}{*}{General microbiology (TAP and LAB) and physicochemical parameters} \\  & - & - & & Pasturized & \\  & 450 & 5 & 0, 5, 9, 18, 33, 51 & 450/5 & \\  & 600 & 5 & 600/5 & & \\ \hline \multirow{3}{*}{Second batch} & - & - & - & Raw & \multirow{3}{*}{General microbiology (TAP and LAB) and physicochemical parameters} \\  & - & - & & Pasteurized & \\ \cline{1-1}  & 600 & 5 & 0, 3, 10, 28, 52 & 600/5 & \\ \cline{1-1}  & 600 & 15 & 600/15 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental design of each cream batch and the aim of the study.  
where \(\Delta\)E* is the total color change between a sample and the control (initial values identified with the subscript \"0\").
# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 4 Discussion
The wearable device highlighted the ability to detect subtle changes in health status. This aspect highlights the feasibility of these proof-of-concept systems for use in early interventions that can prevent medical complications. The proposed wearable device, based on the MSStack module, integrates the MAX30100 and MPU-6050 sensors to collect vital data continuously. The device utilizes the ESP32 microcontroller, which operates at a frequency of 240 MHz and is programmed using the Arduino IDE environment and  
Figure 14: HADA accuracy (>98%) in early anomaly detection using synthetic datasets. the C++ programming language. To optimize energy consumption, the M5 Stack module is configured to enter deep sleep mode between measurements, thereby extending the battery life to 22 h. Tests conducted over two years have shown that the device can detect subtle changes in the patient's health, such as abnormal increases in heart rate or significant decreases in sleep quality. These anomalies are identified using the HADA, which generates real-time alerts. The study reported a sensitivity of 100% and an accuracy of 98.5%. These values highlight the feasibility of this conceptual system for preventing medical complications. Research of this nature requires ongoing funding, which becomes accessible after the proof of concept is published. Once the idea is publicly demonstrated and validated, attracting the necessary financial support for further development and scaling becomes easier. The HADA system provides a strong foundation for further exploration into elderly care and health monitoring, demonstrating the viability of real-time, data-driven interventions, with the proposed infrastructure never investigated before in the literature. While this research does not claim to offer a fully mature commercial product, it provides a promising solution that can be further developed with appropriate funding and support. With additional testing, optimization, and comparisons to alternative methods, this system could be invaluable in enhancing the quality of care for elderly individuals. The authors believe that, with the right investment, this solution has the potential to make a significant impact in the field of remote health monitoring.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Precision  
A key attribute of land surface reflectance and temperature measurements is precision or repeatability. It is imperative to know that the same measurement is being made across instruments, operators, and time. The degree to which ASD- and UAS-based measurements are repeatable is the focus on this section.
# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 4 Results
### Results of Image Enhancement  
After the extraction of urban areas of Wuhan, image sharpening was conducted to the original image. To decrease the noises produced by the sharpening process, a median filter was used. Additionally, the results were compared with the original image and are shown in Figure 8.  
Figure 6: Changing trend of perimeters of Wuhan.  
Figure 7: (**a**): Urban area intensity of Wuhan when threshold value is 190; (**b**): urban area boundary of Wuhan when threshold value is 190.  
Figure 8a shows the original extraction result of the urban areas and the DN values range between 190-2669. Additionally, the image after the sharpening process is shown in Figure 8b, where the DN values range between 1 and 8456. We can find the range of the DN values has been extended comparing Figure 8a,b: The minimum value has decreased from 191 to 1, and the maximum value has increased from 2669 to 8456. Therefore, the image contrast of Figure 8b is higher than the original image. The road profile in Figure 8b is also clearer than that in Figure 8a. However, some abnormal points also emerged in Figure 8b, the DN values of them were much larger than the nearby pixels. Through the middle filter, we can find the number of these points has been decreased, as shown in Figure 8c. Two regions, A and B, were chosen to show the details in Figure 8d. A represents the regions with high light intensity. B represents the regions with low light intensity.  
The histograms of the original image and the sharpened image (also after middle filtering processing) were shown in Figure 9. Through the comparison, we can find that the range has been obviously expanded. The DN values of the histogram of the original image were distributed mainly between 200 and 350, and the DN values showed a decreasing trend with the increasing DN values. The DN values of the histogram of the sharpened image were distributed in a larger range (50 to 800) with an obvious fluctuation.
## Using MCS in Industry 4.0 Scenarios
### Challenges and Open Issues  
Although Industry 4.0 could greatly benefit from the use of MCS, as proved by the number of industrial applications that would be easily enabled according to Table 1, there are still some open issues that need to be addressed.  
The use of personal data coming from personal devices introduces _security and privacy issues_, particularly with reference to workers' and customers' location. It is extremely important that personal data are secured and leakages are prevented. Nevertheless, this issue tends to fade once data are aggregated.  
Although traditional ISNs are usually more expensive and less scalable, they are also more _robust and reliable_: in ISNs, once sensors are installed, the covered area is guaranteed to be monitored, provided that there are no failures. With MCS, the covered area cannot be known a priori nor guaranteed. Furthermore, the quality of measurement is usually lower.  
Handling big amounts of data that need to be transmitted, processed and stored, _resource allocation_ becomes a crucial aspect to be considered, especially considering that battery-powered devices are used. Task assignment techniques need to be implemented [55], so that mobile devices, particularly personal devices, are not overloaded.  
Finally, considering only mobile crowdsourcing, _recruiting and retaining_ mechanisms for workers and customers that can provide relevant feedback need to be studied, along with techniques to _incentivize_ them when they complete a task [56]. The drawback that cannot be overlooked is that, when incentives are given, people tend to complete as many tasks as possible, even giving inaccurate information or reducing workers' efficiency.
## 3 Principles of Signal Transmission and Processing
### Frequency Correlation Detection  
The approach to frequency correlation detection involves cross-correlating the data derived from photon pulse counting with the frequency characteristic values at the receiver end. Within the FPGA decoding program, a design consisting of four frequency detection channels, each corresponding to a carrier frequency, is implemented. The processed data from photon pulse counting form an array \(s_{c}\), which comprises \(k\) elements. The value of \(k\) is determined by the equation \(s_{c}\), which can be expressed as  
\[s_{c}(t)=[N^{t}(T_{c}),N^{t+1}(T_{c}),\ldots,N^{t+k-1}(T_{c})] \tag{15}\]  
where \(s_{c}\) represents an array composed of pulse counts \(N\) at time \(t\), consisting of \(k\) elements, and \(c\) denotes the index of carrier frequency \(f\). \(s_{c}\) is cross-correlated with the carrier frequency \(f_{c}\) characteristic values to obtain the cross-correlation result \(P_{c}(t)\):  
\[P_{c}(t)=\sum_{i=1}^{k}s_{c}(i,t)\cdot\alpha(i) \tag{16}\]  
where \(s_{c}(i,t)\) represents the _ith_ element of the array \(s_{c}\) at time \(t\); \(\alpha(i)\) denotes the _ith_ element of the frequency characteristic array \(\alpha\); \(k\) is the length of both arrays; and \(P_{c}(t)\) signifies the cross-correlation result between the pulse count of carrier frequency \(f_{c}\) at time \(t\) and the frequency characteristic values.
## 1 Introduction  
With the increased use of subway lines in highly populated metropolitan areas, new tunnels constructed beneath older tunnels are becoming more common [1; 2; 3; 4; 5; 6; 7]. The primary difficulties in such parallel tunneling projects are ensuring the safety and serviceability of the current tunnel while building the new tunnel. The mechanical characteristics of the existing tunnel are difficult to investigate, owing to the intrinsic intricacies of soil-tunnel interactions.  
Many researchers over the last few decades have evaluated the influence of undercrossing tunneling on existing tunnels using field investigations and monitoring [8; 9; 10; 11; 12; 13; 14], analysis methods [15; 16; 17; 18], numerical modeling [19; 20; 21; 18], and laboratory tests [22; 23; 24]. [PERSON] and [PERSON] [8] investigated the reaction of an existing tunnel during new undercrossing twin tunnels. [PERSON] et al. [9] showed the sedimentations of existing shield tunnels and the ground surfaces following the excavation of new twin horseshoe-shaped tunnels. [PERSON] et al. [10] described a method for protecting in-tunnel grouting when building twin tunnels below existing twin tunnels. The sedimentation tank and the produced stress of the existing tunnel induced by shield tunneling were also analyzed using field monitoring data. [PERSON] et al. [11] investigated the mechanical responses of twin tunnels in sandy soil strata caused by nearer shield undercrossing. [PERSON] et al. [12] used finite element analysis (FEA) and site monitoring to study the deformation response of overlapping tunnels in Tianjin, China, taking into account the features of different tunneling types.  
According to the literature, there is a lack of an idea of the reality of the load evolution and mechanical response of the existing large-diameter tunnel during the construction of the new twin horseshoe-shaped tunnels. As a result, this article offers a thorough case analysis of new twin horseshoe-shaped tunnels constructed below Beijing's existing Qinghuayuan Tunnel in gravel. The present tunnel reinforcing measure (composite pre-reinforcement) was described and reported. The settlement, longitudinal joint tensioning, load development process of earth pressures, and hoop stress of existing tunnels were analyzed using monitoring data. The findings of this research may be useful in the design and construction of future tunneling projects.
## 3 Two-Stream UDN for Pedestrian Detection
### Training Tricks of Two-Stream UDN  
The Global network and the Local network of the two-stream UDN network are first trained separately, and each subnetwork adopts the following variance loss shown in Equation (8):  
\[J(W,b,x,y)=\frac{1}{m}\sum_{i=1}^{m}(\frac{1}{2}\|h_{W,b}(x^{(i)})-y^{(i)}\|^{ 2}) \tag{8}\]  
where \(y\) represents the ground-truth label of the image, \(x\) represents the input of the network, \(h\) represents the prediction label of the input \(x\), and \(m\) represents the number of total training samples.  
After the two sub-networks are pre-trained independently, the Global network \(N_{G}(W_{1},b_{1})\) and the Local network \(N_{L}(W_{2},b_{2})\) are fixed temporally.  
Then, we jointly train the two sub-networks with the loss function defined in Equation (9) to update the parameters of the global network and the local network, which are \((W_{1},b_{1})\) and \((W_{2},b_{2})\).  
\[J(W_{1},b_{1},W_{2},b_{2};x,y)\!=\!\left[\frac{1}{m}\sum_{i=1}^{m}\!\left(\! \frac{1}{2}\|\beta h_{W_{1},b_{1}}\!\left(x^{(i)}\right)+(1-\beta)h_{W_{2},b_{2 }}\!\left(x^{(i)}\right)-y^{(i)}\|^{2}\!\right)\right] \tag{9}\]  
During the joint training, the two sub-networks are updated alternately due to the differences in network input sizes and parameter volumes between the global and local networks.  
Since pedestrian images are cut from street view videos captured by in-vehicle cameras, the number of positive samples is too small. In order to improve the generalization ability of the proposed method, it is necessary to expand the number of positive samples. This paper augments data by applying mirror transformation, clockwise rotation, and counterclockwise rotation to positive samples. The rotation angle is three degrees and bilinear interpolation is adopted during rotation. Figure 7 shows some examples of mirror transformation and angular rotation.
# Miniaturized MIMO Antenna Array with High Isolation for 5G Metal-Frame Smartphone Application
## 3 Results and Discussion
### User's Hand Effects and SAR Analysis  
In this subsection, the impact of user hand models was studied. It is worth noting that the MIMO antenna system for Sub-6 GHz is only used for data transmission, so the study of the head model is not included here [5].  
In Figure 18, the proposed MIMO antenna system is simulated with and without the user's right-hand model. By comparing Figure 18a,b, it can be observed that human tissue forms a reflective surface so that the radiation of the entire antenna system is the largest in the direction away from the human body, while the radiation is relatively more minor in the direction close to the human body.  
First, the designed antenna grabbed by a right hand is set up and simulated. Due to the direct contact of the fingers with the antenna, inevitable shocks may be caused to the antenna elements, resulting in some performance degradation. In Figure 19a, the impedance matching of the reflection parameter is significantly reduced. However, the effect is relatively small in the target frequency band. Figure 19b shows that the isolation drops by about 3 dB and can remain above 19 dB, which is still relatively higher in related fields. As shown in Figure 19c, the efficiency of the eight antenna elements changes only slightly and the efficiency range remains above 50%. In general, the effect of user hand shape on antenna performance is acceptable within the target frequency band.  
Figure 17: Simulated and measured 3-D radiation patterns of Ant 1.  
Figure 18: Simulated 3-D radiation patterns of Ant 1 of the proposed MIMO antenna system at 4.75 GHz. (**a**): Radiation pattern with a right-hand model and (**b**): radiation pattern without a right-hand model.  
Finally, we studied and analyzed the specific absorption rate (SAR) value. The SAR value characterizes the power absorbed from the mobile terminal to the human body. As the number of antennas increases, it remains challenging to obtain lower peak levels. Figure 19d presents the simulation results of the four antenna elements (Ant 2, Ant 3, Ant 4, and Ant 5) closer to the user's fingers in the MIMO antenna system. It can be observed that the SAR level of Ant 2 at 4.7 GHz, Ant 3 at 4.75 GHz, Ant 4 at 4.75 GHz, and Ant 5 at 4.8 GHz are below 1.6 W/kg, which conforms to the standards formulated by the United States, Europe, and China.