esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Measurements and Discussion
The reason why the center frequency of this work is set to 2 MHz is to illustrate that the system can blindly find a fluidic channel even under unfavorable conditions. It is worth mentioning that the center frequency of this circuit system is not limited to 2 MHz. As a proof of concept, we have utilized a relatively low frequency value. In addition, the center frequency can be increased depending on the specific target vessels, and it will require various studies to determine the optimum center frequency depending on the application. The proposed system needs a manual adjustment of the view angle. As a future work of this proof-of-concept system, hemodynamic indices such as a resistivity index \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Concept** & **Strengths** & **Limitations** \\ \hline \multirow{3}{*}{This work} & Monitoring Doppler amplitude deviation & Single transducer Detection of low-resistance arbitrary vessels & Lack of in vivo test Manual angle correction \\  & Recognition of featured A-mode pattern & Single transducer Detection of separate vessel walls & High center frequency Specific target vessel \\  & Recognition of featured shape in B-mode image & Non-expert-user-friendly Automatic tracking of carotid artery & Two transducer probes Specific target vessel \\  & Learning-based segmentation in B-mode image & Robust RG positioning & Intensive computing Complicated algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of automatic vessel detection methods. Figure 20: (**a**) Measured supply voltage of water pump to arbitrarily adjust the fluid velocity, (**b**) acquired Doppler sample volume, and (**c**) reconstructed spectrogram. and a pulsatility index are to be extracted with reconstructed spectrogram in in vivo tests. Since hemodynamic indices are based on a ratio of velocity terms, they are less sensitive to view angles [31]. Thus, we are planning evaluations on whether to extract consistent hemodynamic indices for various view angles.
## 3 Results and Discussion
### FTIR Analysis  
As demonstrated in the results of FTIR studies, heating the glass at a temperature below Tc does not affect its internal structure. An exemplary comparison of the FTIR spectra of as-prepared BBO(50:50) and BBO(50:50) + Ag samples and BBO(50:50) + Ag samples annealed at 480 \({}^{\circ}\)C for 5 h is shown in Figure 4.  
It is well established that the borate glass network is primarily composed of [BO\({}_{3}\)] triangular structural units, which can undergo transformation into [BO\({}_{4}\)] tetrahedral units in the presence of a modifying agent. Such structural rearrangement may also be induced by Bi\({}_{2}\)O\({}_{3}\)[15]. The broad absorption bands observed in the range of 680-720 cm\({}^{-1}\) and 1100-1250 cm\({}^{-1}\) are typically associated with the deformation and stretching vibrations of [BO\({}_{3}\)] groups [26; 27; 28]. Also, a characteristic band appearing between 1200 and 1400 cm\({}^{-1}\) corresponds to the stretching mode of the [BO\({}_{3}\)] groups. Furthermore, the absorption band appearing at approximately 900-1100 cm\({}^{-1}\) can be attributed to [BO\({}_{4}\)] groups [27; 28]. However, it is important to note that these bands may shift due to the influence of Bi\({}_{2}\)O\({}_{3}\), and they may potentially overlap with the Bi\({}_{2}\)O\({}_{3}\) peaks and shoulders [28]. Looking at the  
Figure 4: FTIR spectra of as-prepared BBO(50:50) and BBO(50:50) + Ag samples and BBO(50:50) + Ag samples annealed at 480 \({}^{\circ}\)C for 5 h.  
Figure 3: XRD patterns of as-prepared BBO(50:50) and BBO(50:50) + Ag samples and BBO(50:50) + Ag, BBO(60:40) + Ag and BBO(70:30) + Ag samples annealed at 480 \({}^{\circ}\)C for 5 h.  
spectra presented in Figure 5, it can be observed that neither the incorporation of Ag into the glass nor heating it below the crystallization temperature (Tc) had a significant effect on the spectra. Considering the DTA results, the chosen annealing temperature of 480 \({}^{\circ}\)C is well below the crystallization peak, and thus, it is not expected to induce any structural changes in the glass matrix. Additionally, the silver--introduced in very small quantities in the tested samples--did not alter the fundamental structural units of the glass.
## 3 LoRa Overview  
LoRa is a LoRaWAN physical layer technology, licensed free in ISM bands, characterized by low bandwidth and a limited number of messages [24; 25]. It uses Chirp Spread Spectrum (CSS) modulation that allows long-range and low-power consumption [26]. In LoRa communication, some parameters can be customized, such as the spreading factor (SF), the coding rate (CR) and the bandwidth (BW). The values of each parameter depend on the region where the LoRa devices are installed. In Europe, it uses bands of 868 MHz, 915 MHz in North America and 433 MHz in Asia [25; 27]. Nevertheless, the SF can vary between 7 and 12. The higher it is, the higher the signal-to-noise ratio (SNR), the sensitivity, the coverage range, the symbol time (Ts) [28] and the time on air (ToA); that is, the packet transmission time. LoRa modulation can transmit arbitrary frames using two types of packet formats, explicit and implicit. Generally, the packet structure consists of four elements: preamble, header (optional), data payload (limited to 255 bytes) and an optional payload CRC [27; 29]. The format is shown in Figure 1.  
A typical LoRa network operates with a bandwidth of 125 kHz, 250 kHz or 500 kHz [30]. The increase in bandwidth allows for higher transmission speed, but it becomes more susceptible to errors [31]. In Equation (1), the symbol time (\(T_{s}\)) is linked to the bandwidth and the spreading factor (\(SF\)).  
\[T_{s}=\frac{2^{SF}}{BW} \tag{1}\]  
Figure 1: LoRa package format.  
LoRa includes a forward error correcting code. The code rate (\(CR\)) is given by \(CR=\frac{4}{4+n}\), with \(n\in\{1,2,3,4\}\). Taking into account that the bits of information are transmitted by symbol. The useful bit modulation rate \(R_{b}\)[32], is defined by Equation (2):  
\[R_{b}=SF\times\left(\frac{BW}{2^{SF}}\right)\times\left(\frac{4}{4+n}\right) \tag{2}\]  
To calculate the time on air for the transmission of a LoRa packet [27; 28; 29], Equation (3) is presented:  
\[ToA=\frac{2^{SF}}{BW}(NP+4.25+SW+max(H,0)) \tag{3}\]  
\[H=\left[\frac{8 PL-4 SF+28+16 CRC-20 IH}{4(SF-2 DE)}\right](n+4)\]  
where \(n\) is the value of the parameter belonging to \(CR=4/(4+n)\). \(NP\) is the Number of programmable symbols of the preamble. \(SW\) is the length of the sync word. \(PL\) is the number of PHY useful payload bytes. \(CRC\) is \(CRC\) presence (1 = yes; 0 = no). IH is the presence of the PHY header (1 = no; 0 = yes) and \(DE\) is the use of data rate optimization (1 = enabled; 0 = disabled).  
The received signal strength indicator (\(RSSI\)) is an important measurement parameter that reflects the quality of LoRa links [28]. With the noise factor (\(NF\)) of the receiver and the transmitted power P_TX, it is possible to estimate the \(SNR\) of the measured \(RSSI\) values, according to [28; 32]:  
\[SNR=RSSI+174-10(BW)-NF \tag{4}\]  
where \(NF\) = 6 dB, which is the noise and \(RSSI\) is the receiver sensitivity \(-137\) dBm [27], \(BW\) = 125 Klz, \(SF\) = 12 and where \(R_{b}\) = 293 bps.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
### Contributions  
In this manuscript, we make the following contributions:  
* Derive a novel, closed-form, tractable CRLB on position estimation in TWR systems;
* Derive a novel, closed-form, tractable CRLB on orientation estimation in TWR systems;
* Implement the proposed CRLBs in a simple MATLAB simulation platform;
* Benchmark several popular estimators against the proposed CRLBs;
* Discuss how these results can directly inform aircraft design decisions.
## 3 A Proposed Online Detection Algorithm
The frame error rate decreases accordingly. Conversely, if the first alarm is caused by the selfish carrier sensing problem, a frame transmission following successful RTS/CTS exchanges will be interfered with and, hence, even with RTS/CTS, the magnitude of \(c_{k}\) would continue to increase, even beyond \(\theta_{S}\). Recall that, under a normal condition, the PHY-layer link quality is stable and has a certain upper bound \(\mathcal{T}_{FER}\), namely, the target FER, which is guaranteed by the underlying rate-adaptation scheme that adjusts the modulation schemes to meet the target FER (e.g., \(\mathcal{T}_{FER}\) = 0.05 is used in the evaluation). For example, practical rate adaptations [21] adjust the modulation schemes to meet the target FER (frame error rate) and, thus, guarantee the average FER performance to be maintained around the target value. Consequently, the AP issues a detection alarm to the central controller in the network (see Figure 1) if \(c_{k}>\theta_{S}\), which may trigger a follow-up action at the controller level to solve the selfish carrier sensing problem within the managed network. **Remark. ** Although the focus of this paper is to propose an AP-level detection mechanism, it is important to cope with the selfish misbehavior detected within the network at the system level. Here, we briefly discuss how the controller determines when to take follow-up actions to cope with the selfish misbehavior detected within the network. In typical managed Wi-Fi networks, the information that is obtained at APs is integrated on the controller. Subsequently, the controller utilizes the information to improve the detection accuracy. When the selfish tethering is present, the majority of nearby APs will be likely to experience the similar severe packet errors simultaneously. Consequently, the victim APs send the detection alarm to the controller at the same time. By exploiting the spatial and temporal correlation in those alarms, the controller identifies the root of the problem more effectively and accurately. For example, if a certain condition is satisfied, then the controller can take various follow-up actions, which can be (i) localizing the rogue interfering node [22], and (ii) remedying victim APs (e.g., interference-aware channel reassignment), etc. However, the detailed follow-up actions are beyond the scope of this paper.
## 2 Theory and Implementation
### NDIR Mathematical Model Based on \(\Delta\)-\(\Sigma\) Modulation with Temperature Drift
#### 2.3.2 Sensor  
The \(\Delta\)-\(\Sigma\) architecture is based on the net radiation signal received by the sensor, which is used to modulate the heating duty of the infrared light source. According to [PERSON]'s law, the revived infrared radiation depends on the temperature of the light source as Equation (7) and it also is described by the Beer-Lambert's L which is related to the gas concentration, shown in Equation (8).  
The infrared radiation from the sensor to the environment is also considered to be derived for the net radiation of sensor, which is related to the temperature of sensor described in Equation (9). The temperature of sensor is easily affected by the heat from the environment, PCB (Printed circuit board) and the heat of the infrared light source. This is the major factor to be evaluated for the drift of gas concentration based on \(\Delta\)-\(\Sigma\) architecture.  
\[I_{O}=A\varepsilon_{b}\sigma T_{b}{}^{4}(t) \tag{7}\]  
\[I_{INPUT}=I_{O}(t)\times e^{-\lambda\cdot\text{C}\cdot d} \tag{8}\]  
\[I_{OUTPUT}=B\varepsilon_{a}\sigma T_{a}{}^{4}(t) \tag{9}\]  
where \(I_{O}\) is the intensity of the infrared light source before being absorbed by gas concentrations, \(A\) and \(B\) are geometric parameters.  
In order to describe the physical phenomenon more completely and realistically, we considered the emitting angle of light source, shown in Figure 4.  
The net radiation of sensor can be described as:  
\[I_{NET}=\int I_{O}(t)\times e^{-\lambda\cdot\text{C}\cdot d}d\theta-B \varepsilon_{a}\sigma T_{a}{}^{4}(t). \tag{10}\]  
By substituting Equation (6) into Equation (10), we will obtain an equation for the description of the threshold voltage (\(V_{th}\)), which is also the key parameter in our proposed \(\Delta\)-\(\Sigma\) architecture; the equation can be written as:  
\[V_{th}=aS\bigg{(}A\frac{m}{N}\frac{P_{e}(t)}{A_{b}}\times e^{-\lambda\text{C }d}-B\varepsilon_{a}\sigma T_{a}{}^{4}(t)\bigg{)}+b. \tag{11}\]  
where \(a\) and \(b\) are the electronic circuit gain and related system parameters, \(S\) is the sensitivity of the sensor.  
Finally, the equation for description of the gas concentration (C) has been derived to rewrite Equation (11); the equation can be written as follows:  
\[C=\frac{1}{\lambda d}\Bigg{\{}In\Big{[}\frac{m}{N}\Big{]}+In\frac{AP_{e}(t)}{ A_{b}\Big{[}(\frac{V_{th}-b}{aS})+B\varepsilon_{a}\sigma T_{a}^{4}(t)\Big{]} \Bigg{\}}. \tag{12}\]  
These expressions reveal several important factors regarding the effects of operating time of system (\(t\)) and temperature \(T_{a}\), which is supposed to be investigated carefully in this research.
## 9 Energy Control Strategies
### Fuzzy Model for Energy Control
#### 9.1.3. Fuzzy Rules  
The fuzzy rules establish the decision-making framework of the system. Each rule outlines conditions based on the input variables and determines the corresponding output decision. Our system adheres to the following rules:  
1. If _Wb_ is _Low_, and \(Traffic\) is _Low_, then strategy is \(LowEfficiency\). 2. If _Wb_ is _Low_, and \(Traffic\) is \(Strong\), then \(Strategy\) is \(MediumEfficiency\). 3. If _Wb_ is \(Medium\), and \(Traffic\) is \(Medium\), then \(Strategy\) is \(MediumEfficiency\). 4. If _Wb_ is \(Medium\), and \(Traffic\) is \(Strong\), then \(Strategy\) is \(StrongEfficiency\). 5. If _Wb_ is \(Strong\), and \(Traffic\) is \(Low\), then \(Strategy\) is \(MediumEfficiency\). 6. If _Wb_ is \(Strong\), and \(Traffic\) is \(Strong\), then \(Strategy\) is \(StrongEfficiency\).
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 3 Results and Discussion
### Discussion
#### 3.2.4 Prediction on Testing Images  
An example of segmenting the kidney and a kidney tumor utilizing the Seresnet50, Seresnet101, and Seresnet152 models is shown in Figure 9 using test images. The annotation images annotate the kidney and tumour regions in green and brown. The performance of the segmentation model is assessed using these annotations as the source of truth. On the other hand, the segmentation predictions for the kidney and tumor in the outcome images are visible. These photos clearly show the segmentation outputs of the Seresnet50, Seresnet101, and Seresnet152 models, demonstrating their effectiveness in locating and defining kidney and tumor regions inside CT scans.  
Table 6 includes various architectures, such as U-Net, FPN, and others. Each architecture is associated with its respective kidney and tumor detection scores. Based on the results, our approach utilizing the FPN (Feature Pyramid Network) with different versions of the SE-ResNet model (Seresnet18, Seresnet34, Seresnet50, Seresnet101, and Seresnet152) achieves high scores for both kidney and tumor detection. FPN-Seresnet50 demonstrates the highest performance, with a kidney score of 0.980 and a tumor score of 0.984. As a result, it can be concluded that our kidney and tumor detection method utilizing FPN-Seresnet50 is exact and efficient. Furthermore, as an additional enhancement, we incorporated ResNet50 into our approach. The outcomes yielded by this addition were marginally lower than the results attained with SeresNet50. It is important to note that different architectures show varying performance across the tasks. The scores in the table provide a comparative analysis of the different approaches used in kidney and tumor detection. Figure 10 compares different segmentation approaches.  
Figure 8: (**A**) Seresnet18 image normal and (**B**) Seresnet18 image with tumor (**C**) seresnet34 image normal and (**D**) seresnet34 image with tumor.  
Figure 9: (**A**) sersenet50 image normal and (**B**) sersenet50 image with tumor. (**C**) sersenet101 image normal and (**D**) sersenet101 image with tumor. (**E**) sersenet152 image normal and (**F**) sersenet152 image with tumor.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Architecture** & **Kidney Dice** & **Tumor Dice** & **Kidney IoU Scores** & **Tumor IoU Scores** \\ \hline
[87] & nnU-Net & 0.969 & 0.919 & - & - \\
[87] & Hybrid V-Net & 0.962 & 0.913 & - & - \\
[87] & U-Net3+ & 0.959 & 0.909 & - & - \\
[87] & SRE Net 2D-CNN & 0.979 & 0.925 & - & - \\
[88] & FR2 PathU-Net & 0.948 & 0.911 & - & - \\
[89] & U-Net EfficientNet-B0 & 0.984 & 0.980 & 0.969 & 0.960 \\
[89] & U-Net EfficientNet-B1 & 0.981 & 0.980 & 0.963 & 0.961 \\
[89] & U-Net EfficientNet-B2 & 0.983 & 0.982 & 0.966 & 0.965 \\
[89] & U-Net EfficientNet-B3 & 0.985 & 0.980 & 0.970 & 0.960 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluating Pretrained Architecture Approaches Against Prior Methods for KiTS19 Dataset Segmentation.
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
## 1 Introduction  
Ultrasonic imaging with characteristics of high transmission capacity and low harm to the human body has become one of the major medical diagnostic technologies nowadays. Imaging algorithms are the key technology of ultrasonic imaging system [1], while delay-and-sum (DAS) is one of the most widely used non-adaptive beamforming methods. However, DAS suffers from low lateral resolution and low signal-noise-ratio (SNR) [2]. In order to improve the imaging quality of the ultrasonic system, different kinds of adaptive beamformings have been introduced to process the ultrasonic echo data [3; 4; 5; 6; 7]. The calculated data-dependent apodization weights are equivalent to a spatial filter, which can maintain the desired signal and suppress the interference and noise signal of the ultrasonic echo data.  
Almost all previous work within this field is characterized by the fact that narrowband methods have been directly applied on broadband ultrasonic echo data [8; 9]. Therefore, [PERSON] proposed a broadband minimum variance beamforming implemented in the frequency domain [10; 11]. [PERSON] introduces the sub-discrete Fourier transform (sub-DFT) to divide the broadband echo signal into a set of narrowband signals, and provides a set of adapted, weights for each frequencysubband. However, how to appropriately divide the broadband signal into a set of narrowband signals based on sub-DFT needs more exploration. Appropriate division determines the quality of the system, thus sub-DFT beamforming is faced with the problem of decreased robustness [12; 13].  
This paper proposes an approach implemented in the wavelet domain. First, broadband echo signals are divided into a set of narrowband signals with discrete wavelet transform. Then, each wavelet subband is processed independently by a narrowband beamforming, generalized sidelobe canceler. Later, the processed subband responses are coalesced to provide the broadband beamforming output.  
The outline of this paper is as follows: Section 2 focuses on the traditional methodologies and its application to ultrasonic imaging. Section 3 looks into the principle and realization of the broadband generalized sidelobe canceler in detail. Section 4 presents the experiment results based on simulated data. Finally, the advantages of the proposed beamforming and its comparison with early proposed methodologies are discussed and concluded in Section 5.
# 3D Photoacoustic Tomography System Based on Full-View Illumination and Ultrasound Detection
## 3 Results
### Performance Test of the 3D-PACT
#### 3.1.2 Resolution Test of the System  
In the resolution test experiment, a black sphere with a diameter of 0.3 mm in 2% agar was used as an imaging target and photoacoustic signal source, and was placed in the center of the circular illumination zone, as shown in Figure 5a. Because of its all-black color, the sphere was a good absorber at the laser wavelength of 532 nm used in the system. In the process of data acquisition, the sampling rate was 50 MHz, the number of sampling points in a B-scan was 180 and the total laser energy reaching the surface of the phantom was about 3.2 mJ (the optical fluence of each bundle was 0.7 mJ/mm\({}^{2}\)). During the experiment, the imaging target, quartz bowl, and the ultrasonic transducer were all immersed in distilled water to achieve acoustic coupling. The reconstructed photoacoustic image is shown in Figure 5b. It should be noted that the imaging resolution in this research was defined as the full width at half maximum of the normalized absorption intensity at the centerline of photoacoustic image Figure 5b. As shown in Figure 5c, the imaging resolution of the system was 0.31 \(\pm\) 0.02 mm based on quantitative calculations. In addition, according to the raw data analysis of the photoacoustic signal shown in Figure 5d, it can be seen that the intensity of the photoacoustic signal of the imaging target was relatively uniform at the cross section, and there was no phenomenon showing that the photoacoustic signal was submerged. These also reflected the ability of the system to maintain the photoacoustic coplanar property.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Areas** & **E (μ)** &
\begin{tabular}{c} **Mean of Signal** \\ **Amplitude P (V)** \\ \end{tabular} & \(\mathbf{G_{r\!f}}\) = P/E (V/μ)** & \(\mathbf{H=G_{r\!f}/G_{t\!r\!f}}\) \\ \hline Q\({}_{1}\) & 88.7614 & 2.236 & 0.02519 & 204.46\% \\ Q\({}_{2}\) & 88.3206 & 2.214 & 0.02507 & 203.49\% \\ Q\({}_{3}\) & 88.0611 & 2.183 & 0.02480 & 201.30\% \\ Q\({}_{4}\) & 88.1574 & 2.195 & 0.02490 & 202.11\% \\ Q\({}_{5}\) & 88.2163 & 2.222 & 0.02520 & 204.54\% \\ Q\({}_{6}\) & 88.0742 & 2.156 & 0.02448 & 198.70\% \\ Q\({}_{7}\) & 88.0688 & 2.174 & 0.02469 & 200.41\% \\ Q\({}_{8}\) & 88.3672 & 2.229 & 0.02522 & 204.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ultrasonic reflection efficiency of the quartz bowl.  
Figure 5: (**a**) Phantom of resolution test, (**b**) reconstructed PA image of target, (**c**) the profile along the dotted line in (**b**), and (**d**) raw data of a B-scan.