esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 3 Receiver for Short-Reach Transmission Systems  
It is well-known that short-reach transmission systems are mainly used in data center optical interconnects, access network, and metro network, etc. In such scenarios, the component costs need to be take into consideration as a variety of servers, switchers, and users are involved. Applying only one single-end PD in the system may greatly reduce cost and energy consumption, but it may not be able to meet ultra-high speed transmission systems because single-end PD only detects signal intensity. At present, some advance direct detection schemes, e.g., Kramers-Kronig receiver and Stokes vector receiver, have been proposed to improve the rate-distance product by combining impairments compensation algorithms with advanced modulation technology. Furthermore, monolithic integrated receivers with small footprint size, low power consumption, and low-cost have also been widely concerned and already demonstrated in various short-reach transmission systems. Next, we would review the recent applications of the four detection technologies.
# Non-Compliance Distalization Appliances Supported by Mini-Implants: A Systematic Review
## 4 Discussion
### Anchorage Loss  
Non-compliance appliances are associated with some undesirable side effects that decrease their clinical success, such as anchorage loss of the anterior dental unit indicated by forwarding movement and proclination of the anterior teeth, distal tipping and rotations of the maxillary molars, and posterior anchorage loss in terms of mesial movement of the molars, during the anterior tooth retraction [16; 21; 26; 28]. All the mentioned non-compliance appliances that were placed palatally or buccally showed no anchorage loss among the Pendulum and TADs subgroup. However, [PERSON] and [PERSON] confirmed that the MGBM appliance produced some anchorage loss, with the mesial tipping of the first premolars by \(2.5^{\circ}\) and \(4.3^{\circ}\) along with proclination of the maxillary incisor with \(1.4^{\circ}\) and \(1.8^{\circ}\), respectively [30].
# Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network
## 1 Introduction  
Middle ear diseases are diagnosed using patient's history and otoscopic findings in the tympanic membrane (TM). Guidelines on otitis media highlight the usefulness of TM findings in diagnosing various types of otitis media [1]. The development of video endoscopes has enabled more accurate detection of diagnostic lesions. However, clinical diagnosis of otitis media is mainly subjective. In addition, it shows limited reproducibility due to the high reliance on the clinician experience and cooperation of the patients [2].  
Artificial intelligence is often employed to perform high-level image analysis such as image classification, segmentation and matching. The success of such an analysis highly depends on feature extraction, for which deep learning is widely used [3]. In particular, convolutional neural networks (CNNs) have demonstrated good performance in automatic classification of medical images (e.g., skin lesions [4; 5], eye lesions [6; 7; 8], radiologic and MRI images [9; 10; 11]). Several studies have applied deep learning for making diagnoses based on otoscopic images. Accuracies from 73.11% to 91.41% have been reported when applying deep learning to distinguish acute otitis media (AOM) and otitis media with effusion (OME) [12; 13; 14]. At the same time, chronic otitis media (COM) is challenging due to variability of the images and difficulty of locating lesions. [PERSON] et al. proposed a machine-learning algorithm to classify normal middle ear, AOM, OME and COM with an accuracy of 88.06% [15].  
In this study, we propose a machine-learning method to detect the TM findings accurately. The aim of the study is to build and evaluate a CNN model for TM classification (i.e., detecting TM side and presence or absence of perforation). The usefulness and reliability of the CNN in classifying TMs is also investigated.
## 6 Prototyping and Testing of the Designed TFPMDEIG  
A prototype is fabricated to assess the validity of the proposed TFPMDEIG. The experimental results are in good agreement with the 3-D FEM analyses. The fabricated TFPMDEIG is shown in Figure 15a. The manufacture of the proposed generator can be described in the following three steps:  
1. [label=1-]
2. Rotor disk: The rotor disk is made of paper sheet with phenolic resin (Figure 15b). Permanent magnets have been fixed by glue on it.
3. The stator body: the stator body is made of compact plastic with a thickness of 25 mm. The stator body holds the stator cores (E and I core) (Figure 15c,d). The location of the stator core is carved by CNC (computer numerical control) milling machine.
4. E and I-shaped cores: E and I-shaped cores are made of silicon steel. In order to reduce core losses, E and I-shaped cores are laminated.  
Figure 16 shows the experimental platform of the TFPMDEIG, consisting of an inverter drive (VFD (variable-frequency drive)), an asynchronous motor, the TFPMDEIG, a resistive load and an oscilloscope. The prototype is driven by the asynchronous motor, which is fed by the frequency converter. Experiments on the TFPMDEIG are of two types; no load, and load tests. In the latter case, a resistive load is used.  
Figure 14: Finite element results: (**a**) no load voltage; (**b**) full load voltage; (**c**) full load current; (**d**) output power in full load.  
The no-load voltage curve, output voltage curve, output current curve, and output power curves at full load, are plotted in Figure 17.  
Figure 16: (**1**) Oscilloscope; (**2**) resistive load; (**3**) TFPMDEIG; (**4**) asynchronous motor; (**5**) inverter drive (VFD).  
Figure 15: (**a**) Fabricated TFPMDEIG; (**b**) rotor disk; (**c**) stator body (E core); (**d**) stator body (I core).
# Machine Vision-Based Chinese Walnut Shell-Kernel Recognition and Separation
## 4 Discussions  
Table 2 demonstrates the superiority of the YOLOX detection model over YOLOV3, Faster-RCNN, and SSD. To achieve this, YOLOX uses the Mosaic data enhancement strategy for input and replaces the traditional YOLO baseline header with a decoupling baseline, significantly improving the convergence speed. In addition, SimOTA is used, reducing training time and avoiding extra solver hyperparameters in the Sinkhorn Knopp algorithm, thereby improving detection accuracy and efficiency. In this paper, YOLOX obtained good recognition accuracy for walnut shell kernel detection in different scenarios, including complex cases such as occluded shell kernels. This performance meets the requirements for online real-time detection of walnut shell kernels. [PERSON] et al. [34] used an improved YOLOv5 series algorithm to detect mixed materials in walnut kernels, achieving an mAP value of 88.9%. Meanwhile, [PERSON] et al. [35] used the YOLOv7 series algorithm to detect and identify the good, bad, or incomplete cashews on a packaging production line, with an average identification accuracy (mAP) of about 90%, demonstrating good results. While near-infrared spectroscopy is also an important tool in nut detection [36], it demands higher hardware requirements. YOLO-based detection technology has higher detection efficiency, more affordable detection equipment, and a wider range of application objects. Moreover, it has certain advantages in detection accuracy, making it suitable for current online detection and identification requirements for walnut shell kernels.  
However, it is important to note that the YOLOX model does have limitations. As shown in Figure 10, after the walnut shell breaks, the shell kernels exhibit different shapes, and their appearance closely resembles that of walnut shells. When shell kernels densely block each other and have similar appearances, the detection performance of walnut shells and kernels is slightly poor, resulting in lower confidence scores. Sometimes, the model may mistakenly predict two closely contacted and shielded walnut shells or kernels within a box, resulting in target positioning errors or reduced confidence. In addition, when exposed to strong light, the detection accuracy is limited due to overexposure in the central region of images caused by manual photo acquisition, resulting in increased similarity between shell kernels and unclear details [37]. The YOLOX detection model struggles to extract features effectively when processing walnut shell kernels with similar appearances, resulting in reduced confidence in some images during detection.  
In future research work, the utilisation of scientific image acquisition can be employed to further improve the photo accuracy of walnut shells and kernels [38; 39]. In addition, the future should aim to create a more lightweight detection model, exploring the replacement of the backbone network with other lightweight alternatives and reducing the number of model parameters.  
Figure 9: Detection effect with and without shielding conditions. (a) With shielding. (b) Without shielding.
## 2 Improved Ensemble Empirical Mode Decomposition
### Testing and Verification  
In this section, a synthetic signal will be used to test and verify the advantage of the IEEMD method when compared with the EEMD method. The noisy signal from the bridge structures is simulated as follows.  
\[\mathrm{s}(t)=15 cos\Big{(}2\pi t+\frac{\pi}{3}\Big{)}+12 cos\Big{(}10\pi t+\frac {\pi}{8}\Big{)}+10 rand \tag{15}\]  
where two cosine signals with center frequency 1 Hz and 5 Hz stand for the \"true\" signals, which contain the structural dynamical information. \(10 rand\) models the monitoring noise with a signal-to-noise ration [32] 2.66, and a peak signal to noise ration [32] 15.95. The sampling frequency is 256 Hz and the sampling period is 10 s.  
The decomposition results are shown in Figure 2.  
Figure 2 has clearly shown that the number of IMFs decomposed by EEMD is \(11\), which is two more than the IEEMD. By using EEMD, the frequency of IMF4 is 1 Hz. However, the IMF5 and IMF6 both have 5 Hz frequency components, which indicates that different IMFs contain the same frequency components, and the mode mixing problem still exists. By contrast, by using IEEMD presented in this paper, it can be seen clearly that IMF5 and IMF6 are the main frequency components. The frequency of IMF5 is 1 Hz, and the frequency of IMF6 is 5 Hz. The IEEMD method effectively alleviates the mode-mixing issues.  
The decomposition error is compared by superimposing all the decomposed IMFs and comparing them with the original signal. The reconstruction error of the two methods is shown in Figure 3. From Figure 3, it can be seen clearly that the error of IEEMD is in the order of \(10^{-13}\) and that EEMD is in  
Figure 2: Intrinsic mode functions (IMFs) decomposed by EEMD and IEEMD. (**a**) EEMD. (**b**) IEEMD.  
the order of 1. Clearly, the decomposition accuracy of IEEMD is much higher than that of the original EEMD method.
# Ultrafast Mid-IR Laser Pulses Generation via Chirp Manipulated Optical Parametric Amplification
## 4 Experiments on Broadband Pumped DC-OPA
### BBP-OPA in NIR Region
Using identical pump pulse energy (180 \(\upmu\)), the conversion efficiencies in BBP-OPA and NBP-OPA are both \(\sim\)3%, and the energy stability of \(<\)1% RMS is achieved for signal from BBP-OPA over 3 h. The beam quality is measured by loosely focusing the output signal into a 5-megapixel CCD camera and the beam profile is shown as the inset in Figure 12. The cross-sections of the beam on \(x\) and \(y\) axes both show an excellent Gaussian profile, which makes it a suitable laser source for successive amplification stages or further applications. The temporal properties of the signal pulse are measured with a SHG-FROG and the results are shown in Figure 13. The uncompressed pulse duration of 34 fs is obtained with a GDD of 240 fs\({}^{2}\). The obtained signal spectrum supports a sub\(-\)2-cycle TL pulse duration as short as 7.5 fs, which is plotted in Figure 13a. According to the near-quadratic spectral phase in Figure 13b, the pulse is linear-temporal-chirp dominant, which can be compressed to a near TL duration using various approaches such as prisms, gratings, chirped mirror pairs, or acoustic-optic programmable dispersive filter (AOPDF) accompanied by a bulk material.
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 4 Experimentation and Results
### Case Study of Using SbMBR Tree in Data Analysis
#### 4.6.2 Validation of Error Estimation Method  
To further validate our error estimation method, we used the ConvLSTM [35] model to predict the precipitation in April by the precipitation after compression in January, February, and March. The parameters of the ConvLSTM model are shown in Table 7.  
Firstly, we used the SbMBR tree algorithm and JPEG algorithm, respectively, to compress the data of the precipitation in January, February, March, and April in 2017, 2018, and 2019.  
Secondly, we spitted the rasterized data into many 216 \(\times\) 216 submatrices. Then, we used the precipitation in January, February, and March as the input and used the compressed precipitation in April as the output to train with the ConvLSTM model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Param Name** & **Value** \\ \hline Convolution kernel & \(3\times 3\) \\ Number of Convolutional layers & 4 \\ Number of nodes per layer & \{1,16,32,64\} \\ Optimizer & AdamW \\ Loss Function & L1 Loss \\ \hline \hline \end{tabular}
\end{table}
Table 7: Params used in ConvLSTM.  
Figure 10: Comparison of SbMBR tree and JPEG algorithm through normalized mutual information.  
We simply defined \(1-error\) as follows:  
\[1-error=avg\Bigg{(}\frac{|x-x_{org}|}{x_{org}+p}\Bigg{)} \tag{15}\]  
where \(x_{org}\) is the original data, \(x\) is the compressed data, \(p\) is a small constant number used to avoid \(x_{org}+p=0\), and \(avg\) is the average value function.  
After training, we obtained the predicted data from the compressed input. Then, we calculated the \(1-error\) mentioned above for all the inputs. The result is shown in Figure 11 in the form of the \(1-error\)-compression ratio curve.  
From Figure 11, it can be concluded that with the compression ratio growing, the \(1\)-error between compressed data and real data increases, which is consistent with the result in Section 4.6.1. In addition, the curve of the SbMBR tree is overall above the curve of JPEG, which is also consistent with the result in Section 4.6.1.  
To sum up, our error estimation method is effective on these meteorological data, and our compression method performs better than the JPEG algorithm on these meteorological data.
# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 5 Classification
### K-Nearest Neighbor (KNN)  
K-Nearest Neighbor (KNN) is a widely used reliable classification approach [49]. It is based on the Euclidean distance between a test sample and the training samples. Let \(x_{i}\) be an input sample with \(p\) features \(\big{(}x_{i1}x_{i2},\cdot x_{ip}\big{)}\), \(n\) be the number of input samples \(i=1,2,\ldots,n\), and \(p\) is the number of features \(j=1,2,\ldots,p\). The Euclidean distance between sample \(x_{i}\) and \(x_{l}\) (\(l=1,2,\ldots,n\)) can be calculated as  
\[d(x_{i},x_{l})=\sqrt{(x_{i1}-x_{i1})^{2}+(x_{i2}-x_{i2})^{2}+\cdots+(x_{ip}-x_{ ip})^{2}}. \tag{41}\]  
A cell that covers all neighboring points that are the nearest to each sample can be expressed as  
\[R_{i}=\big{\{}x\in\mathbf{R}^{p}:d(x,x_{i})\leq d(x,x_{m}),\forall i\
eq m \big{\}}. \tag{42}\]  
In the context above, \(R_{i}\) represents the region or cell associated with sample \(x_{i}\), while \(x\) refers to any point within that cell. This framework embodies two fundamental aspects of the coordinate system [49]: every point inside a cell is considered among the nearest neighbors of the sample defining that cell, and the closest sample to any point is determined by the nearest boundary of these cells. Leveraging this property, the K-Nearest Neighbor (KNN) algorithm classifies a test sample by assigning it the most common category among its \(k\) nearest training samples. To avoid ties in voting, \(k\) is typically chosen as an odd number. When \(k\) equals 1, the method is known as the nearest neighbor classifier.
# A Channel Allocation Mechanism for Cellular Networks
## 4 Numerical Analysis  
This section investigates the communication behaviors of handovers and call arrivals by proposed model through numerical analysis. The input parameters are considered as follows:  
* The variable \(l_{i}\) is the distance of the road segment covered by the cell \(i\),
* the variable \(V_{i}\) which is the average speed of car,
* the variable \(F_{i}\) which is the car flow,
* the call inter-arrival time \(t\) (h) has exponential distribution with mean \(1/\lambda\),
* the call holding time \(\tau\)(h) has exponential distribution with mean \(1/\mu\).  
The variable \(A_{i}\) is the count of call arrival per hour. The variable \(O_{i}\) is the count of Handover Out per hour. The variable \(I_{i}\) is the amount of Handover In. The variable \(D_{i}\) is the amount of call department per hour. This study investigates the effects of traffic information and communication behaviors on the \(A_{i}\) and \(O_{i}\).  
Effects of \(F_{i}\) on the \(A_{i}\) and \(O_{i}\): Figure 13 shows that the numbers of \(A_{i}\) and \(O_{i}\) are increased in accordance with \(F_{i}\). Several MSs are carried in Cell\({}_{i}\) to generate the signals of call arrivals and handovers with traffic congestion. Therefore, the factor of car flow (\(F_{i}\)), which is important.  
Effects of \(V_{i}\) on the \(A_{i}\) and \(O_{i}\): Figure 14 shows that the number of \(A_{i}\) is decreased in accordance with \(V_{i}\), but the number of \(O_{i}\) is not affected by \(V_{i}\). This study focuses on the channel allocation for handover, so the factor of the average speed of car (\(V_{i}\)) is not important.  
Effects of \(\lambda\) on the \(A_{i}\) and \(O_{i}\): Figure 15 plots \(A_{i}\) and \(O_{i}\) against \(\lambda\), which indicates that \(A_{i}\) and \(O_{i}\) are unchanged as \(\lambda\) increases. This phenomenon is explained as follows. When the call inter-arrival  
Figure 12: The timing diagram for call departure in the specific Cell\({}_{i}\) derived from Handover In [27; 28; 29; 30; 31; 32; 33].  
Figure 13: Effect of \(F_{i}\) on the \(A_{i}\) and \(O_{i}\).  
time (\(\lambda\)) increases, the influence of call inter-arrival time (\(\lambda\)) on \(A_{i}\) and \(\mathrm{HO_{C}}\) are invalid. Though Figure 15, the factor of call inter-arrival time (\(\lambda\)) is not important for channel allocations.  
Effects of \(\mu\) on the \(A_{i}\) and \(O_{i}\): Figure 16 plots \(A_{i}\) and \(O_{i}\) against \(\mu\), which indicates that \(A_{i}\) is unchanged and \(O_{i}\) increases as call holding time (\(\mu\)) increases. This phenomenon is explained as follows. When the call holding time (\(\mu\)) increases, the amounts of handover become higher. In the other words, no matter how the call holding time (\(\mu\)) changes, the influence of the call holding time (\(\mu\)) on \(O_{i}\) is invalid. In Figure 16, the factor of the call holding time (\(\mu\)) is important for channel allocations.
## 4 Results
### Main Analysis (Mixed-Effects Model)
#### Key Insights from the Model  
Figure 4 illustrates the fixed effects as point estimates with 95% confidence intervals. The intercept (0.619) represents the fine-tuned LLM under baseline conditions (multi-step, application-based, application-oriented, and mean explanation quality). Single-step design significantly increases error by +0.937. VectorGraphRAG (+0.503) and VectorRAG (+0.215) both raise baseline error, but they also display large negative interactions with single-step (\(-1.271\) and \(-1.134\), respectively), indicating they can partially or fully offset the disadvantage of simpler tasks. Explanation-quality subgrades are strongly significant (e.g., specificity_relevance \(=-0.431\)), suggesting that stronger justifications reduce alignment errors by up to 0.4 or 0.5 points.  
Overall, the fine-tuned LLM often achieves relatively low error in multi-step contexts, but VectorRAG or VectorGraphRAG can be more competitive if questions are single-step or if they provide higher-quality explanations in key sub-dimensions such as specificity/relevance.
# A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges
## 2 Train-Track-Bridge Model
### Train-Track-Bridge Interaction  
The complete train-track-bridge model is obtained by coupling the bridge and the train models with the expression of the contact forces between the wheelsets and the rails. The complete model is shown in the scheme of Figure 7. The adoption of track irregularity and wheel tread irregularity is optional in the model.  
Figure 4: Train model.  
Figure 5: CSA train data.  
Figure 6: TSR train data.  
The equations of the motion of the bridge, track, and the superstructure, and of the vehicle are integrated in time, in order to solve the vectors \(\underline{x}_{\text{s}}\) and \(\underline{x}_{\text{v}}\) representing the degrees of freedom of the structure and of the vehicle, respectively. The numerical integration process is described in the flowchart of Figure 8, where \(\xi_{\text{i}}\) represents the position of a wheel along the \(i\)-th finite element, \(N_{k}\), \(F_{lk}\), and \(F_{lk}\) are the normal, tangential, and longitudinal components, respectively, of the contact force on the \(k\)-th wheel.  
At the beginning of each time step (starting from the blue dashed square on the left in Figure 8) approximations of the displacements of the contact points are calculated, based on the previous step values. A first estimation of the contact forces can then be calculated through the contact force model [12]. Based on these tentative contact force values, the generalized forces on the structure and on the vehicle can be calculated, and the equations of motion can be integrated (red dashed-line square on the right). Once the vectors containing the solution of all the degrees of freedom of the structure and of the vehicle are obtained, the motion of the contact points on the rail and on the wheel, and consequently the contact forces, can be calculated. An iteration loop is begun within the same time step,  
Figure 8: Flowchart representing the numerical integration process [8].  
Figure 7: Complete train–track–bridge interaction model.  
by integrating the equations of motion again with the new calculated contact forces, until convergence for contact forces is reached. Afterwards, the time step is increased, new approximations are made, and the numerical integration cycle starts again.