esa-sceva/satcom-chunk-collection huggingface dataset:
# Digging Performance and Stress Characteristic of the Excavator Bucket
## 3 Analysis of ADAMS-EDEM Coupling Simulation Results
### ADAMS-EDEM Co-Simulation of Mining Process
The force accounts for a small proportion of the total digging resistance, suggesting that the cutting resistance is an important part of the digging resistance. Figure 6: Bucket digging resistance. Figure 7: Bucket area division. The calculation formula of the bucket filling rate is:  
\[\eta=\frac{V_{1}}{V} \tag{1}\]  
As shown in Formula (1), \(\eta\) is the filling rate (%), \(V_{1}\) is the actual load volume of the bucket (m/s), and \(V\) is the bucket capacity (m/s). The calculation formula of energy consumption per unit mass material in bucket mining is:  
\[E=\frac{\int_{0}^{t}Fvdt}{m} \tag{2}\]  
As shown in Formula (2), \(E\) is the energy consumed by mining unit mass materials (J/kg); \(F\) is the digging resistance generated during the excavation process; \(v\) is the speed during the bucket digging process (m/s); and \(m\) is the effective material mass in the bucket after a single excavation (kg). The bucket capacity of the XB120R700 hydraulic excavator is 7 m\({}^{3}\), the excavation mass of the original bucket model is 6426.06 kg through discrete element post-processing, and the stacking density of the material pile through geological investigation is 1016.7 kg/m\({}^{3}\), so the filling rate of the original bucket is 90.3%. The energy consumption per unit mass material is 413.72 J/kg calculated using ADAMS.
# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 3 Methodology
### Backbone Network  
Recent developments in CNNs for image denoising demonstrate that unsupervised learning networks can achieve good denoising results without requiring datasets with a considerable amount of target images for training [33]. For this purpose, we designed a backbone network. As shown in Figure 4, the backbone network is based on an unsupervised network with an encoder-decoder structure, which is similar to that of the typical U-Net [34], except for the input and output layers. As a result, the main operators in our network are a combination of convolution, batch normalization and ReLu. The left and right sides of our network are the encoder and decoder, respectively. The encoder utilizes a successive convolution layer to extract precise information, and the decoder uses a successive deconvolution layer to recover the information. The advantage of the typical U-Net encoder-decoder network structure is that it uses a multi-scale processing method  
Figure 3: Pipeline of the proposed UWMGN. \(D_{1},D_{2},\dots,D_{K}\) are the image denoisers, \(\hat{x}_{1},\hat{x}_{2},\dots,\hat{x}_{K}\) refer to the initial denoised images, \(W_{1},W_{2},\dots,W_{K}\) are the weight maps.  
to expand the processing field of view, which greatly reduces the memory requirements for storing parameters. Simultaneously, the connection between the same level features in the encoder-decoder is established through skip connections to minimize information loss due to upsampling and downsampling. It is represented by a dashed arrow in the figure. Compared to the standard U-Net network, for the input of the network structure, our input is not an image but random noise of the same size as the initial denoised images. Since our desired output is \(K\) weight maps corresponding to the denoised images, we redesigned the loss function of the U-Net network. In this way, the output weight maps can retain the best part of the corresponding denoised images. This function was derived from the preliminary corrupted image and the combined image, ensuring that our output evolves in a reasonable direction--that is, the noise of the image continuously decreases while the image remains close to the input image without distortion. In the following section, we will analyze the loss function. Lastly, due to the great extensibility of our network, it can generate weight maps for colored images after expanding it by the number of channels.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 4 Results and Discussion
### Solution  
We assume that the main server of APPIFY has a pair of public verification and private signing keys (\(VK_{main}\), \(SK_{main}\)). The main server also has a (potentially self-signed root) certificate for public verification key \(cert(VK_{main})\). The main server of APPIFY provides this certificate to the APPIFY in MEC.  
The main server of APPIFY issues a certificate \(cert(APPIFY)\) for APPIFY in the MEC host. This certificate is given to APPIFY when deployed in the MEC host and will be used to authenticate APPIFY to Alice.  
We also assume that MNO has public and private verification and signature key pair (\(VK_{MNO}\), \(SK_{MNO}\)). The MNO has a (potentially self-signed root) certificate for public verification key \(cert(VK_{MNO})\). The MNO sends \(cert(VK_{MNO})\) to its subscribers, including [PERSON].  
The MNO issues a certificate \(cert(MEC)\) for MEC when MEC is deployed to the network of MNO. This certificate is signed by \(SK_{MNO}\), and it will be used later for authenticating MEC to [PERSON].
## 5 Other Therapeutic Approaches  
### Phage Therapy and CRISPR-Cas 9  
Bacteriophage therapy has been used for over 50 years to treat bacterial infections. Recent experimental and clinical studies have demonstrated a remarkable impact on treating both wound biofilm infections as well as device-related infections [127]. The existence of huge numbers of bacteria in the biofilms enables the bacteriophages to rapidly and efficiently infect their host and subsequent multiplication of the bacteriophage. Bacteriophages have many qualities that could make biofilms vulnerable towards them. They are knownto produce or induce enzymes that degrade the ECM. They are also able to infect persister cells. [PERSON] et al. have shown that bacteriophages such as Sb-1 enhanced antibiotic activity against biofilm (Table 6) [128]. Moreover, it also degraded the EPS, mainly the polysaccharide content in the biofilms, and targeted the persister cell of _S. aureus_[128]. It is perhaps predictable that bacteriophages can also target the bacteria in biofilms since they prey on bacteria naturally [129]. One of the merits of phage therapy over antibiotic therapy is that the treatment is much more specific. A phage attaches to one specific bacterial strain leaving others unharmed in contrast to the use of antibiotics, which may kill not only harmful bacteria but also helpful bacteria that live in our gut. At the same time, the specificity of phage therapy could also turn into a disadvantage as the matured and naturally produced biofilms could be phage resistant [130].  
Clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR-Cas9 system) is a novel technology that has been applied for genomic editing in some prokaryotes and eukaryotes [131]. Most recently [PERSON] et al. have knockdown the _lux5_ gene and fimbie-associated gene (_fimH_) using the CRISPR technology for regulating biofilm-associated infections [132; 133]. Similarly, [PERSON] et al. employed the CRISPR/Cas9 system to make an _ompA_ (outer membrane protein A) gene knockout in a dominant mosquito microbiota _Cedeca neteri_[134]. The _ompA_ mutant displayed a reduction in biofilm formation capability, significantly decreased infection in adults, and was also found to be less prevalent in adults [134]. Thus, CRISPR seems to be another promising technology for controlling biofilms.
# Named Entity Recognition Method Based on Multi-Feature Fusion
## 4 Experimental Results
### Named Entity Recognition  
#### 4.3.1 Experimental Data  
This experimental dataset contains 5000 pieces of data, which is divided into training set, test set and verification set according to the ratio of 6:2:2, including 16,415 entities. The distribution of the data set is shown in Table 2. This paper uses the BIO annotation strategy, where B indicates the beginning of the entity, I indicates the middle position and the end position of the entity, and O indicates non-entity.
# The Vicious Cycle of Obesity and Low Back Pain: A Comprehensive Review
## 4 Spinal Changes Related to Obesity
### Epidural Fat  
Epidural fat is adipose tissue within the spinal canal that surrounds the dura mater, cushioning the spinal cord and nerve roots. In certain cases, excessive epidural fat accumulation (particularly in obesity) can lead to spinal stenosis by compressing neural structures, resulting in pain, numbness, and weakness in the lower extremities, a condition known as epidural lipomatosis [86]. A recent longitudinal study demonstrated a linear relationship between BMI and epidural fat volume, with each 1 kg/m\({}^{2}\) increase in BMI corresponding to an additional 45 mm\({}^{3}\) of epidural fat [87]. Similarly, [PERSON] et al. reported greater epidural fat volume in obese patients undergoing urgent decompression for cauda equina syndrome, suggesting that epidural lipomatosis may increase the risk of this condition in obese individuals [88]. This association was further supported by a recent meta-analysis, which identified increased BMI as the primary risk factor for epidural lipomatosis and suggested that weight loss could mitigate symptom severity and surgical complications [89]. Beyond its mechanical effects, epidural fat accumulation has been recognized as a marker of metabolic syndrome (Figure 2). Studies have shown that its volume correlates not only with BMI but also with abdominal circumference, visceral fat area, and hypertension. From this perspective, epidural lipomatosis may represent a form of ectopic fat deposition, similar to that seen in the pancreas, heart, and liver in patients with metabolic syndrome [90]. Adipocytes from overweight patients undergoing decompression surgery for epidural lipomatosis were found to be hypertrophic and exhibited increased expression of pro-inflammatory cytokines such as TNF-\(\alpha\) and IL-1B [101]. However, further mechanistic studies are needed to validate these findings.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 2 Background and Related Works
### Use of CV in Navigation for People with VI  
[PERSON] et al. [52] reported that CV-based navigation apps on smartphones are a cost-effective solution. Researchers have proposed several CV-based positioning and navigation systems by recognizing landmarks (e.g., storefronts [26]) or processing tags (e.g., barcodes [29; 53], QR codes [54; 55], color markers [56], and RFID [57]). CV techniques have also been applied to obstacle avoidance [58; 59], which ensures that users can move safely during the navigation without running into objects. However, [PERSON] et al. [26], who studied the last-few-meters wayfinding challenge for PVI, concluded that, for a deployable level of accuracy, using CV techniques alone is not sufficient yet.  
Another line of work is to develop autonomous location-aware pedestrian navigation systems. These systems combine CV with specialized hardware (e.g., wearable CV device [60] and suitcase [61]) and support collision avoidance. While these systems have expanded opportunities to receive navigation and wayfinding information, their real-world adaptability is still questionable, as [PERSON] et al. [62] commented that navigation environments in the real world are dynamic and ever-changing.  
Lately, researchers are exploring the feasibility of an augmented reality (AR) toolkit in indoor navigation, which is built into modern smartphones (e.g., ARKit [63] in iOS devices, ARCore [64] in Android devices). [PERSON] et al. [65] demonstrated the potential of constructing indoor 3D maps using ARKit and localizing users with VI on 3D maps with acceptable accuracy. [PERSON] et al. [66] proposed an ARKit-based mobile application to help PVI recognize and localize objects. Researchers found that AR-based navigation systems have the advantage of (i) having widespread deployment [67], (ii) providing better user experience than traditional 2D maps [68], and (iii) freeing users' hands without the need to point the camera towards an object or a sign for recognition [69].  
More recently, we explored the opportunity of utilizing CV technologies to assist sighted agents instead of users with VI [70]. We designed several use scenarios and low-fidelity prototypes and had them reviewed by professional RSA agents. Our findings suggest that a CV-mediated RSA service can augment and extend agents' vision in different dimensions, enabling them to see farther spatially and predictably and keeping them ahead of users to manage possible risks. This paper complements those findings by identifying situations where leveraging CV alone is not feasible to assist sighted assistants.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 11 Discussion on Private Campus Networks  
### Service Providers for the Customers  
When 3 GPP designed the services and defined the areas to be covered by 5G, three basic theoretical directions and objectives were identified [127]. As part of 3 GPP, which has been entrusted with the development of 5G service level systems, IMT-2020 [2, 128] has set out its goals as follows.  
* Innovation--Deploy innovative software-based services for both the enterprises and the individual subscribers. Innovation must mean software based services, where innovation serves not only individual subscribers but also corporate customers. Therefore, a particular function or service must be freely transferable between the physical elements of the network and hardware.
* Digitalization--Service Provider (SP) must recognize that 5G has a shift towards cloud services. SPs need to be clear that the next step in digitalization is moving towards cloud services. Therefore, it is not preferred to bond services to a specific hardware. They must be deployed on various cloud infrastructures. Therefore, these services can be customized freely and flexible.
* Simplification--Want to Virtualize (Network Function), automate, and simplify. When designing the system architecture [129], it must be initially taken into consideration that elements can be developed (I), designed (II), and operated (III) by machines. The system must be built of simple and interconnected elements which form a loosely coupled digital system. The base elements of the services should have minimal and loose connections; also, the associated services should be modular.  
Furthermore, support for legacy systems is a practical addition: customers with less needs and efforts must be served as well as new customers. Service Providers shall pay attention to legacy customers' needs [130]. In case some customers require merely voice services, providers shall respect this and fulfill their needs. This also means that new services shall be at least as simple as existing ones.  
The following subsections provide various hints for motivating private campus networks, where enterprises can utilize complete 5G network segments as they would own it (depending on certain regulations they could even own it) or experience network and service quality as they were the only users of the network. This motivation is built up by a short recap on the evolution of mobile cellular communication and services, and the expected business impact of 5G.
# JLMS25 and Jiao-Liao Mandarin Speech Recognition Based on Multi-Dialect Knowledge Transfer
## 5 Experiment  
### Datasets and Metrics  
The datasets utilized in this study consist of the publicly available KeSpeech dataset [5] and the self-constructed JLMS25 dataset. In the first stage of the experiment, data from seven dialects within the KeSpeech dataset, excluding Jiao-Liao Mandarin, were employed. These dialects include Beijing Mandarin, Southwestern Mandarin, Zhongyuan Mandarin, Northeastern Mandarin, Lan-Yin Mandarin, Jiang-Huai Mandarin, and Ji-Lu Mandarin. To mitigate data imbalance in KeSpeech, 30,000 Mandarin speech samples were randomly selected from its training set and incorporated into the training process. During the fine-tuning stage for Jiao-Liao Mandarin, the second and third experimental stages were independently conducted using the Jiao-Liao Mandarin subset of the KeSpeech dataset and the JLMS25 dataset, respectively. For the experiments based on the KeSpeech dataset, only its training set and test data were utilized for model training and testing. For JLMS25, the data were divided into training and test sets with a 9:1 volume ratio, ensuring a balanced distribution for both model training and testing.  
We used Character Error Rate (CER) and Word Error Rate (WER) as the primary evaluation metrics for assessing speech recognition performance. Since the model generates a sequence of Chinese characters, using CER as an evaluation metric is both straightforward and widely accepted. For WER, both the model's output and the reference text were segmented into sequences of Chinese words. The CER and WER are calculated using the following formulas:  
\[\text{CER}=\frac{(S_{c}+D_{c}+I_{c})}{N_{c}}, \tag{3}\]  
\[\text{WER}=\frac{(S_{w}+D_{w}+I_{w})}{N_{w}}, \tag{4}\]  
where \(S_{c}\), \(D_{c}\) and \(I_{c}\) are the numbers of substituted, deleted, and inserted characters, respectively, and \(Nc\) represents the total number of characters in the reference text. \(S_{w}\), \(D_{w}\), and \(I_{w}\) are the numbers of substituted, deleted, and inserted words, respectively, and \(N_{w}\) represents the total number of words in the reference text.
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 2 Materials and Methods  
To construct the recognition model, we first collected a database in real urban scenarios. Then, we used two techniques to extract time and frequency information from audio signals in a given time window that was used as input data to convolutional neural networks to conduct the classification task. Following, we provide a detailed explanation of each one of these steps.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
### QKD Overview  
A field of cryptography called QKD is thought to be unconditionally secure, even with the commercialization of quantum computers [2; 4]. QKD describes a family of secure communication protocols that use quantum states to generate a cryptographic key between two parties, [PERSON] and [PERSON], which can then be used for encryption [5]. While traditional secure communication protocols may be subject to eavesdroppers, QKD ensures eavesdropper detection, making it more secure than other methods [5; 6].  
Security Based on Quantum Mechanics  
QKD relies on quantum mechanical properties to ensure security; for instance, quantum state collapse during measurement, irreversibility of measurement, the Heisenberg uncertainty principle, the no cloning theorem, and entanglement [5; 7]. When a quantum state is measured, the state collapses into one of the possible states and cannot be reversed. For security purposes, this can mean that the act of measuring results in permanent changes that can be used to detect changes that an eavesdropper may make on the QKD system. Additionally, when two properties are related by the Heisenberg uncertainty principle, certainty in one of the properties results in uncertainty in the other, and vice versa. For example, knowing a photon's polarization in the rectilinear basis results in uncertainty of the photon's diagonal polarization state [8; 9; 10]. For security, this can mean that random, uncontrollable changes may be introduced into the QKD system by eavesdropper presence. For quantum entanglement, when two particles are entangled, they are linked in such a way that measuring one particle will instantaneously affect the state of the other particle, regardless of distance. Furthermore, the state of either particle cannot be predicted prior to measurement [7]. Entangled particles follow the property that if the measurement of each particle is performed in the same basis, then their results will be anti-correlated. For different bases, there is no correlation between the result. Eavesdropper detection can be performed using the Bell Inequality [11]. Such properties are the foundations of QKD security.
# Evaluation and Improvement of Internet Printing Protocol Based on HCPN Model Detection Method
## 5 Improved Scheme and Results
### Improved Scheme
#### 5.1.2 Server Side  
As shown in Figure 14, after receiving MSG6, the server began the session resumption process and decrypted the early data and signature data. Then, it used the client public key and signature data to verify whether the random number rand1 was signed by the client. It continued the execution after the verification was affirmed. The server cached the random number rand2, received in the last session recovery request every time, and compared the two. Under normal circumstances, the two would not be equal, because the client would have generated a new random number every time it sent a recovery request. In the transition \"compare\", the Boolean variable ret was used to record the comparison result. If the ret value was \"TRUE\", it indicated that the two numbers were not equal, and the session could be established normally. If the ret value was \"FALSE\", it indicated that the same random number had been used twice, and an intermediary had replayed the session recovery request. At this time, the server would terminate the handshake.
# Research on Lazy Theta+
## 2 Octree Meshing Modeling
### Encoding Method  
As illustrated in Figure 4, the cubic grid structure includes grid cells, grid points, grid edges, and grid faces, representing the cube's eight vertices, 12 edges, and six faces, respectively [26].  
The grid coding method is divided into cell coding, grid edge coding, and grid point coding according to the grid structure. A subspace of the same size is further divided into subspaces until the target level, or the highest level of the grid, is obtained. The grid coding is coded in a Z-curve manner, and the grid coordinates are coded in octal numbers in the order presented in Figure 1[26; 27; 28].  
The steps for converting grid coordinates \((X,Y,Z)\) at a level \(N\) to octal encoding are as follows.  
1. Convert \((X,Y,Z)\) to binary number \((Xt,Y,Zt)_{2}\).
2. According to the order \(Zt\to Yt\to Xt\), the bits are crossed to obtain the code \(H_{2}=Zt\otimes Yt\otimes Xt\).
3. Shift \(H_{2}\) to the left by three bits to obtain the encoded \(H_{2}t=H_{2}<<3\).
4. Convert the binary code \(H_{2}t\) to the octal code \(I_{8}\).  
The above steps constitute the octal coding method of the grid cells, and on this basis, the grid points are encoded, with the relevant steps being presented below.  
Figure 4: Schematic illustration of the cubic mesh structure.  
Figure 3: Comparison of octree grid and standard grid. (**a**) Standard grid subdivision model, and (**b**) Octree grid partition model.  
1. Reverse the cross-bit of the binary cell code \(H_{2}\) to obtain the binary coordinate \((X_{2},Y_{2},Z_{2})\).
2. The calculation formula of the first hexagram limit grid point code \(Hd_{1}\) is presented in Equation (1). \[\left\{\begin{array}{l}X_{2}t=X_{2}+1\\ Y_{2}t=Y_{2}+1\\ Z_{2}t=Z_{2}+1\\ Hd_{1}=Z_{2}t\otimes Y_{2}t\otimes X_{2}t\end{array}\right.\] (1)  
The above coding method encodes according to the Z-shaped curve based on each level [29].