esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study: Hurricane Irma
### Case Study Description
Following this rule and the windspeed observation from Hurricane Irma, the uncertainty factor value \(\beta\) was set to 0.017 and 0.0147, depending on the wind speed. The resilience curves for daily traffic condition \(R_{D1},R_{D2},\ldots,R_{Dn}\) in a segment were measured with predicted data obtained from the LSTM and the segment resilience \(R_{s1},R_{s2},\ldots,R_{sm}\) during the case study (period from 4-15 September 2017), was calculated from Equation (6). The route resilience value was further quantified from the average segment resilience, as shown in Equation (7).
# Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher
## 1 Introduction  
The Internet of Things (IoT) is a new network industry based on Internet, mobile communication networks and other technologies, which has wide applications in industrial production, intelligent transportation, environmental monitoring and smart homes. It uses the sensors and actuators with different types of perception, communication and computing capabilities on objects to automatically acquire the information of the physical world [1; 2; 3]. As a kind of new media technology, IoT has a very complicated network environment, including home network, office local area network, wired network and wireless network. All kinds of networks interlace and coexist with each other, which may inevitably lead to congestion. The congestion problem occurs when the remaining resource space of the network does not meet the requirements of the user and the performance indicators in the network are greatly deteriorated [4; 5]. In IoT, routers need to forward and transmit large amounts of data, especially in sensor networks and wireless access networks, where network bandwidth and router capacity are limited [6; 7; 8; 9]. Therefore, congestion control plays an important role in IoT that provides satisfactory service.  
IoT is a hybrid network that includes both wired and wireless network environments. Thus, IoT includes not only the congestion caused by the traditional wired network, but also the congestion generated by the wireless network. Sensors and actuators in wireless sensor networks are usually deployed in edge environments where wireless channels are subject to burst packet loss due to multipath fading or high bit error rate [10; 11; 12; 13]. In the hybrid network, traditional TCP protocols can no longer properly coordinate network performance, it only considers the situation of wired networks, attributing the loss of packets to network congestion and rapidly adjusting the transmission rate to control the network load. As a result, the congestion window is frequently reduced and the network performance is seriously deteriorated [14; 15]. Therefore, in an IoT hybrid network, the research oncorrectly distinguishing packet loss categories and implementing reasonable control strategies to improve network performance has attracted wide attention from many scholars, but so far there is no recognized satisfactory solution.  
In this paper, we present our scheme and measure its ability to distinguish between congestion loss and wireless random loss. First, we use simulation to study this discriminatory ability. Then, we modify NewReno to integrate our scheme which is called NB-TCP and study the induced throughput enhancement. Finally, we compare the performance of our scheme with TCP-Casablanca [16] and NewReno [17].  
The rest of the paper is organized as follows. Section 2 reviews related works. Section 3 describes the system model and analyze different features of packet loss. We present the principle and implementation process of NB-TCP algorithm in Section 4. Section 5 presents simulation results and Section 6 concludes this paper.
# Low-Cost and Easily Fabricated Ultrasound-Guided Breast Phantom for Breast Biopsy Training
## 3 Results
### Breast Phantom Validation Workshop
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Survey**} & \multicolumn{4}{c}{**Number of Respondents (Total \(n=6\))**} \\ \cline{2-6}  & **(Single-Layered Phantom)** & **Excellent** & **Good** & **Neutral** & **Poor** & **Very Poor** \\ \hline
1. & Overall how satisfied would you rate the developed phantom? & 2 & 3 & 1 & - & - \\
2. & How was the ultrasound image quality produced by the phantom? & (33\%) & (50\%) & (17\%) & & \\
3. & Was the haptic feedback you received during the placement of core needle into the phantom was a realistic feel? & 1 & 2 & 2 & 1 \\
2. & How was the ultrasound image quality produced by the phantom? & 1 & 4 & 1 & - & - \\
3. & Was the haptic feedback you received during the placement of core needle into the phantom was a realistic feel? & 1 & 2 & 2 & 1 \\
2. & How was the ultrasound image quality produced by the phantom? & (17\%) & (33\%) & (33\%) & (17\%) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Survey on single-layered gelatin-based breast phantom. Figure 6: Survey results for pre-training and post-training confidence level of the participants (\(n=6\)) who participated in the US-guided core needle biopsy. Figure 7: Ultrasound images for embedded tumors and artefact in multi-layered gelatin-based phantom. (**a**) Malignant tumor (Shallot); (**b**) Cyst (Rubber glove finger filled with colored solution); (**c**) Benign tumor (Dried agar konjac); (**d**) Benign tumor (Dried sweet potato); (**e**) Artefact caused by the boundary between the fat and glandular tissues is indicated by the white arrow. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Survey** & \multicolumn{2}{c}{**Number of Respondents (Total \(n=6\))**} \\ \cline{3-4}  & **(Multilayered Phantom)** & **Yes** & **No** \\ \hline
1. & Was the ultrasound image produced by the multilayered phantom & 6 & - \\  & similar to the fat tissue for real breast? & (100\%) & - \\
2. & Was the haptic feedback of multilayered phantom you received during & 6 & - \\  & the placement of core needle into it was more realistic than the & (100\%) & - \\  & single-layered gelatin based phantom? & (100\%) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Survey on multilayered gelatin-based breast phantom.
## 2 Influence of MAI on Acquisition Module  
### Mathematical System Model of Synchronous DS-CDMA Channel  
In this section, we take a more detailed look at the influence of MAI on the DS-CDMA system, particularly focusing on the acquisition module. It is imperative to define the mathematical model. We assume that the system contains a total of \(K\) users as shown in Figure 2. Initially, the unprocessed data from each user are converted by pairwise polarization. Subsequently, the polarized data are subject to spread spectrum coding, utilizing a local PRN code generated by a circular shift register. Following this, modulation into the Radio Frequency (RF) band is employed. Ultimately, the spread spectrum signals of all users are transmitted to the receiver via a Gaussian channel after aliasing.  
The aliased multi-user signal, which arrives at the on-board receiver, is denoted as  
\[y_{in}(iT_{s})=\sum_{n=1}^{K_{user}}A_{n}b_{n}[i]c_{n}(iT_{s}-\tau_{n})e^{i2 \pi(f_{r}+f_{a}^{\alpha})iT_{s}}+n_{o}(iT_{s}), \tag{1}\]  
where  
\(K_{user}=1,4,...,16\) is the number of user signals arriving at the on-board receiver.  
\(A_{n}>0\) is the signal amplitude of user \(n\).  
\(b_{k}()\in[1,-1]\) is the unprocessed data sent by user \(n\).  
\(c_{k}()\) is the PRN spreading sequence code used by user \(n\).  
\(T_{S}=\frac{1}{f_{s}}\) is the system sampling period.  
\(f_{s}\) is the system sampling rate.  
\(\tau_{n}\) is the initial phase of the PRN code for user \(n\).  
\(f_{r}\) is the carrier RF point, every user in the system share the same \(f_{r}\).  
\(f_{d}^{m}\) is the carrier Doppler frequency offset for user \(n\).  
\(n_{0}(iT_{s})\) is the additive Gaussian white noise with mean 0 and variance \(\sigma_{n}^{2}\).  
Here, we need to point out that data modulation might be inconvenient for the coherent accumulation algorithm. So, we assume that each transmitter sends a non-data-modulated section before its information-bearing segment in every burst frame [27], which is abbreviated as  
\[y_{in}(iT_{s})=\sum_{n=1}^{K_{user}}A_{n}c_{n}(iT_{s}-\tau_{n})e^{j2n\pi f_{d} ^{*}iT_{s}}+n_{o}(iT_{s}). \tag{2}\]  
This study aims to explore the up-link receiver acquisition problem in a multi-user system, utilizing the aforementioned signal models as the foundation. Equation (1) is employed in the analysis of the transmission outcome to simulate the bit error rate (BER). Equation (2) is employed in the analysis of the acquisition module to simulate the correct detection probability (\(P_{cd}\)).
## 4 Digit Image Processing
### Digit Change Detection  
The inherent property of the utility metering scenario is that the least significant digit on the meter can be updated with an extremely variable rate. In extreme cases, update rates can vary between seconds and months. Our strategy is to preserve energy by avoiding unnecessary transmissions as much as possible, as data transmission is a much more costly operation compared to image collection and processing. Therefore, our next goal was to establish the criterion that would allow us to determine whether the state of the counter has changed since the last capture and subsequently transmit the data only if such a criterion was met.  
Table 1 depicts a matrix representing median differences between two images representing the same or different digits. It is calculated, as displayed in Figure 8, by comparing each pair of images from the dataset and filling a three-dimensional matrix, in which dimensions represent the first digit, second digit, and number of pixels that differ. Initially, a 3D matrix is set to zero matrix. If the result of comparison between the pair of images (\(D_{i}^{m}\), \(D_{i}^{n}\)) is \(k\), where \(i\) is the \(i\)-th image in set of images of digit \(n\) and \(j\) is the \(j\)-th image in a set of images of digit \(m\), then the 3D matrix is incremented on position (\(m\), \(n\), and \(k\)). Finally, after all pairs of digits have been compared, the mean value per pair (\(m\), \(n\)) is calculated.  
Figure 8: Scheme used for detecting differences between two digit images.  
The results align with our expectations as shown in Table 1. For instance, digit 0 differs significantly from digits such as 1, 4, and 7 due to its distinct shape, as reflected by the larger pixel difference between them. Conversely, digits with more rounded shapes, such as 6 and 9, exhibit fewer pixel differences when compared to digit 0, suggesting a greater similarity. This pattern is consistent across other digits as well. For instance, digits 8 and 3, which share similar structural features, show a smaller pixel difference. Finally, as expected, the values along the main diagonal are minimal, since they represent the comparison of each digit with itself.  
The next step involves determining the optimal threshold for distinguishing whether a digit has been changed. In our paper, we relied on the empirical rule to calculate the threshold value. It follows the steps given in Algorithm 2.  
```
0: Digit pixel differences table
```
0: Optimal value of the threshold  Extract diagonal (same digits) and non-diagonal values (different digits) of the Digit pixel differences table.  Calculate the mean (\(\mu\)) and standard deviation (\(\sigma\)) values for both diagonal and non-diagonal values.  Apply the Empirical Rule: \(\bullet\) 68-68% of values lie within 1 standard deviation from the mean. \(\bullet\) 95-95% of values lie within 2 standard deviations from the
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 3 Preservation Technologies and Optimization
### Future Preservation Techniques and Development Trend  
Future research and industrial practice in fish preservation will hinge on three intertwined trends, each accompanied by distinct challenges. Nanotechnology-enhanced coatings and films--such as nanoemulsion-loaded biopolymer layers--offer highly targeted delivery of antimicrobial and antioxidant agents, significantly prolonging shelf life in high-fat species; however, unresolved questions regarding nanoparticle migration, long-term toxicity, and environmental persistence necessitate the development of standardized safety assessment protocols and GRAS-certified nanocarriers [104]. Synergistic multi-hurdle systems that combine non-thermal processes (PEF, HPP), controlled atmospheres (MAP), and active coatings can achieve additive or even synergistic inhibition of enzymatic, oxidative, and microbial spoilage--often doubling shelf life without compromising sensory quality--but their widespread adoption is limited by the high capital and energy costs of batch-mode reactors and the technical complexity of integrating multiple processes, underscoring the need for continuous-flow designs and modular processing platforms [105]. Finally, intelligent and active packaging solutions, including colorimetric freshness indicators, RFID-based sensors, and oxygen-scavenging films or sachets, promise real-time quality monitoring and controlled-release preservation; yet, ensuring sensor accuracy under variable storage conditions, obtaining food-contact regulatory approval, and building consumer trust in technology-embedded packaging remain critical barriers [106].  
To bridge these gaps, coordinated efforts are required to establish robust toxicological and migration testing for nanomaterials, engineer cost-effective continuous-flow PEF/HPP equipment, define clear regulatory pathways for active and smart packaging, and leverage predictive modeling and machine-learning tools to tailor multi-hurdle preservation protocols to the specific spoilage profiles of different fish species.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 2 Materials and Methods
### Traditional Image Processing Algorithms Description
#### 2.5.1 [PERSON]'s Algorithm  
[PERSON]'s method is based on the computation of a threshold value \(T\) which is a function of the energy mean value and standard deviation computed over a rectangular window. Through its sliding over the whole image, it is possible to then identify which are the foreground and background pixels by comparing the window's pixels energy values with the local threshold.  
\[T(x,y)=m(x,y)+k\times\sigma(x,y) \tag{3}\]  
where \(k\) is a parameter which varies between \(-0.2\) and \(-0.1\), while the other parameter is the dimension of the window (supposed squared in our paper with side \(d_{Nb}\)).
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
Section 4 includes an overview for the simulation system model for simulation measurements. Section 5 presents the simulation results for optimized design point of polar codes followed by the comparison between LDPC and optimized Polar codes. Section 6 presents an overview for DVB standards describing the proposed functional block that are changed to enhance the performance of DVB systems. The system complexity comparison is discussed in Section 7. Finally, the paper is concluded in Section 8.
## 3 Methods
### EmoSDS
#### 3.1.1 Speech Input Processing and Residual Extraction  
The speech input was processed using the WavLM model to extract feature representations from its 6 th transformer layer, denoted as \(S\in\mathbb{R}^{t\times d}\), where \(t\) represents the number of frames and \(d\) denotes the speech feature dimension. The 6 th layer of WavLM encoded both linguistic and paralinguistic features, incorporating rich acoustic information [1,20]. However, as these features were inherently continuous, while LLMs operate in a discrete feature space, a discretization process was necessary to bridge this gap. To achieve this, we applied k-means clustering, pretrained on the 6 th layer of WavLM with 1000 clusters, to quantize the extracted speech representations. This quantization process captured essential linguistic content information, leveraging the structural properties of SSL features while transforming them into a form compatible with the LLM.  
This process produced two outputs: a sequence of cluster embeddings \(U\in\mathbb{R}^{t\times d}\), representing the quantized feature representations in the continuous space, and a corresponding  
Figure 2: Overall architecture of EmoSDS.  
sequence of cluster IDs, where each ID encoded the discrete cluster index assigned to each frame based on the clustering process.  
To eliminate redundancy, consecutive duplicate cluster IDs were removed, resulting in a downsampled discrete unit sequence \(C\in\mathbb{R}^{t^{\prime}}\), formally defined as  
\[C=(c_{1},c_{2},\ldots,c_{t^{\prime}}),\quad c_{i}\in\{0,1,\ldots,N-1\},\quad \forall i\in\{1,2,\ldots,t^{\prime}\}, \tag{1}\]  
where \(N\) denotes the total number of clusters and \(t^{\prime}\) represents the number of downsampled frames.  
To extract emotional features, we computed a residual embedding sequence \(R\in\mathbb{R}^{t\times d}\) by performing element-wise subtraction between the quantized embedding sequence \(U\) and pre-quantized embedding sequence \(S\):  
\[R=U-S \tag{2}\]
## 3 Dual-Branch SSC Receiver  
In this section, we consider a dual-branch SSC diversity receiver operating over both non-independent (correlated) and non-identically distributed (n.i.n.i.d.) Fisher-Snedecor \(\mathcal{F}\) composite fading channels. The equivalent baseband received signal at the \(i\)th (\(i=1,2\)) antenna can be expressed as \(r_{i}=sl_{i}+n_{i}\), where \(s\) is the transmitted complex symbol with energy \(E_{s}=\mathbb{E}[\left|s\right|^{2}]\), \(n_{i}\) is the complex AWGN with \(N_{0}\) assumed to be identical and uncorrelated to both branches, and \(h_{i}\) is the complex channel gain with its magnitude \(R_{i}=\left|h_{i}\right|\) being modeled as a Fisher-Snedecor \(\mathcal{F}\) distributed random variable. Furthermore, the general assumptions are made that only the channel fading magnitude affects the received signal and that the phase can be accurately estimated.  
Let \(\gamma_{\text{SCC}}\) represent the instantaneous SNR per symbol at the output of the SSC and \(\gamma_{\tau}\) denote the predetermined switching threshold. In the non-identical distribution scenarios, two branches of the SSC can have different average SNRs or different fading parameters in the same fading distributions, even if they follow different fading distributions.
## 5 Experimental Results
### Simulation Studies
#### 5.3.2 Test Case 2: The IEEE 162-Bus System  
The 162-bus system consists of 17 generators, 162 nodes and 284 edges and has a capacity of 15,056.7 MW. All time-domain simulations for IEEE 162-bus system are performed using PSD-BPA, which is a component of the software package developed by the Power System Department of China Electric Power Research Institute [39]. We assume that at \(t=0\) s, a three phase-to-ground solid fault happens at the middle of Line 3-50, and this fault is cleared after 0.325 s. If the system splitting measures are not taken, the figures of the generator angle curves, bus voltage curves and generator frequency curves can be observed from Figure 19a-d.  
If no control actions are taken, it can be seen from Figure 19b that the generator rotor angle curves become three clusters {3}, {99, 15, 27, 101, 108, 124, 125, 126} and {130, 6, 73, 76, 114, 118, 121, 131} at about 0.4912 s. Taking the angle of generator bus Node 3 as the base, the relative angles of the remaining generators have experienced the change from positive to negative 180 degrees at around 2.5 s, which indicates that the system has lost its transient stability. After the fault occurs at 0 s, the voltages of generator buses suffer from drastic fluctuations with amplitudes from 0.1673-1.0178 p.u., as shown in Figure 19c. The voltages of the generators that have lost synchronization are likely to reach [0 p.u., 0.2 p.u.], and therefore, the voltage collapse is triggered. It can be seen from Figure 19 dthat the generator frequencies of the third cluster have been rising continuously, which means the rotor speed is increased, widens the angle difference between itself and the other generator clusters gradually and eventually leads to instability at 0.4917 s.  
After the fault happens, we can trip the boundary transmission lines obtained from our tabu search algorithm. The emergency control actions after the system splitting are taken at 0.5 s: generation reduction at Bus 73 with 400 MW and load shedding at Bus 3 with 300 MW. The islands after the system splitting are shown in Figure 19e, where we mark the disconnected transmission lines in red. The system generator angles, voltages and frequency curves are shown in Figure 19f-h. Our algorithm obtained a solution with a power imbalance of 902.9 MW using approximately 40 ms. This solution gives an islanding strategy, which disconnects the set of edges {1-3, 3-14, 3-50, 3-103, 3-123, 3-124, 3-125, 8-15, 10-15, 10-60, 11-15, 13-15, 15-58, 15-62, 15-63, 22-40, 23-24, 25-27, 27-62, 27-65, 28-29, 32-33, 34-104, 40-81, 42-109, 48-50, 50-51, 60-61, 60-62, 60-65, 62-126, 65-126, 82-83, 88-96, 89-90, 91-94, 94-103, 109-124, 109-125, 122-123}, and the 17 generators are assigned to three islands. We can also see from Figure 19h that these three islands are successfully created with frequencies between 1.00 and 1.02 p.u. after 4 s. Similar to the simulations of the IEEE 39-bus system, the angles, voltages and frequencies of generators in both islands have reached a steady state.  
Figure 19: _Cont._
## 2 Related Work
Related to other studies on PD detection, in the machine learning approach, there is a study that used embedding extracted from a deep neural network named x vectors and classified it using cosine distance, cosine distance preceded by Latent Dirichlet Allocation (LDA), and Polylingual Latent Dirichlet Allocation (PLDA) [21]. Additionally, in [22], four machine learning methods (k-nearest neighbor, multi-layer perceptron, optimum-path forest, and support vector machine) were used with 18 feature extraction techniques for the detection of PD. In the deep learning approach, in [23], multiple artificial neural networks (ANNs) were used with 26 speech features for PD detection, and principal component analysis (PCA) and self-organizing map (SOM) were applied for feature selection. While in [24], a deep neural network (DNN) was applied for PD severity prediction using 16 biomedical voice measures. In this research, the calculation costs of voice preprocessing are relatively high, and multiple voice features are required. Compared with this research, in our study, we propose to use an RP which only calculates the percentile and absolute distance so as to reduce the calculation cost and make it easy for implementation. By using an RP generated from simple vowel voice, our approach uniquely detects PD with the model combining the CNN and RNN. To the best of our knowledge, detecting PD with the model combining the CNN and RNN using an RP generated from simple vowel voice has not been referred before.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 2 Materials and Methods
### UAV Data
#### 2.2.2 Data Processing  
Images from each flight were processed using the 'Ag Multispectral' template in thePix4D software (v4.4.12, Pix4D S.A., Prilly, Switzerland). First, key-points in the images were matched based on the programmed flight path. This model was then geo-referenced based on the known GCP coordinates and manual image verification before radiometric calibration was conducted. The resulting output was a separate reflectance map for each of the four bands, with an average GSD of 2.8 \(\pm\) 0.1 cm (mean \(\pm\) standard deviation). Map geolocation accuracy was assessed by calculating the Root Mean Square Error (RMSE) of the five CPs in the imagery. RMSE values were 1.25 cm, and 1.37 cm in the x- and y-direction, respectively. Due to a fault with the sunshine sensor, processed imagery for the period June to September 2018 was omitted from further analysis, leaving a total of 26 image sets.