esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 3 Results
### Correlation of Data Processing
#### 3.1.2 Correlation of Spacecraft Scan  
S/C scan raw data of each station is processed step by step following the flow scheme in Figure 3. The number points of FFT is 1024 with overlapped 128 points, and polynomial order is set as 6, which could fit Tianwen-1 trajectory within the scan. Taking one scan of the data sets as an example, the reconstructed delay polynomial with constant value of CEB is listed as:  
\[\begin{split}\tau_{CEB}(t)=1.41783e^{-24}t^{6}-2.62806e^{-21}t^{ 5}+1.80669e^{-18}t^{4}-1.24437e^{-15}t^{3}\\ +3.73636e^{-11}t^{2}+1.232370417t+\tau_{0-CEB}\end{split} \tag{20}\]  
A local signal model with a delay polynomial is reconstructed, after the correlation residual phase could be computed.  
A priority ephemeris from orbit determination of CLTC is used to assist with resolving phase ambiguity. Since the narrowest spanned bandwidth of tones signal is about 3.83 MHz  
Figure 8: Iteration statistics of the test data: (**a**) location index of maximum magnitude of evaluation vector for each iteration; (**b**) maximum magnitude of evaluation vector for each iteration. The iteration which satisfies both the maximum magnitude and index limit corresponds to the clock information, where clock offset is about 2.7 microseconds and rate is about 0.16 picoseconds per second.  
Figure 9: Comparison of fringes: (**a**) original fringe of cross spectrum of integration time, without proper clock information contained in aprioriory delay model, and no fringe could be identified; (**b**) fringe of cross spectrum of integration time, with proper clock information estimated.  
(the spanned frequency between the carrier and +1 DOR, see Figure 8), the maximum tolerance of the priori knowledge of the differential group delay model is:  
\[\tau_{Apriori}(t)=\pm\frac{\pi}{2\pi\tau_{Span}}\approx\pm 130 ns \tag{21}\]  
In most cases, the priority model is good enough to rule out a false slope of the narrowest spanned bandwidth corresponding to a delay, and then it helps to resolve phase ambiguity of the largest possible spanned bandwidth. Finally, the desired measurement accuracy can be achieved with the unblurred phases of the widest spanned DOR tones, the results are shown in Figure 10.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 8 Limitation, Challenges, and Opportunities of Existing Solutions
### Open Issues and Challenges for Intelligent Energy Management Systems in Buildings
Devices can conserve more network traffic since they are aware of the status of the primary server and their immediate neighbors. Additionally, because most big data processing occurs locally, adopting localized algorithms makes it possible to handle the enormous amount of big data [130]. * Interoperability and standardization issues: A significant challenge for IEMS in buildings is the absence of standardization and interoperability among diverse building automation systems and communication protocols. The lack of standards obstructs seamless integration, establishing technical and operational impediments for ML models that depend on cohesive, continuous data streams from many sources, including HVAC systems, lighting, security systems, and IoT devices. Data fragmentation arises when systems utilize disparate protocols or exhibit incompatibility, resulting in inconsistencies and possible deficiencies in data quality, which leads to compromising the quality and performance of ML models; errors of fragmented data can lead to errors in addition to decreasing the training efficacy, and restrict the model's capacity to generalize across diverse systems or environments. In addition, the absence of established protocols complicates the integration of ML-driven EMS across various building infrastructures, frequently necessitating bespoke solutions for each system. This problem escalates implementation expenses and slows the widespread adoption of ML-enhanced energy management solutions. Thus, universal standards should be established, and open communication protocols should be advocated within the building automation sector. This would facilitate improved interoperability among systems, guarantee uniform data flow, and augment the robustness and scalability of ML-based solutions for intelligent energy management [131].
## 2 Materials and Methods
### Datasets  
The dataset of CT images for creating models for classification was divided in 10 classes for brain, neck, chest, abdomen, and pelvis with contrast-enhanced (CE) and non-contrast-enhanced examination, defined as plain (P). The number of images in each class was 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, and 5000 images from the earliest date that they were acquired from the 700 patients; the names of the corresponding datasets were defined as 5 K, 10 K, 15 K, 20 K, 25 K, 30 K, 35 K, 40 K, 45 K, and 50 K, respectively, where the letter K represents one thousand; e.g., the 5 K dataset includes a total of 5000 images, 500 images each of the 10 classes in that dataset. For the validation dataset, another three different datasets (A, B, and C) of 1000 images for each class were prepared (a total of 30,000 images), which were exclusive from the above datasets. The names and details of each dataset are listed in Table 1.  
The image range of each class was defined as follows. Brain: slice from the anterior tip of the parietal bone to the foramen magnum; neck: slice from the foramen magnum to the pulmonary apex; chest: slice from the pulmonary apex to the diaphragm; abdomen: slice from the diaphragm to the top of an iliac crest; pelvis: slice from the top of an iliac crest to the distal end of the ischium (Figure 1). The range of each class was the same as that of a previous report [13] for the classification of CT images.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Class Name** & \multicolumn{8}{c}{**Dataset**} & \multicolumn{8}{c}{**Validation Dataset**} \\ \hline  & 5 K & 10 K & 15 K & 20 K & 25 K & 30 K & 35 K & 40 K & 45 K & 50 K & A & B & C \\ \hline Brain (P) & 500 & 1000 & 1500 & 2000 & 2500 & 3000 & 3500 & 4000 & 4500 & 5000 & 1000 & 1000 & 1000 \\ Brain (CE) & 500 & 1000 & 1500 & 2000 & 25 CE examination involved the intravascular injection of contrast media before examination. The timing of the scan from injection was not considered. Exclusion criteria of CT images for the datasets were images with excessive magnification, images with the reconstruction kernel of bone or lung, images with nothing above the anterior tip of the parietal bone, and images with only arms or legs.
## 4 Results  
### Signal Conditioning Characterization  
Potential differences with negative and positive polarities will ensue for alkaline and acidic solutions, respectively. Furthermore, in order to optimize performance, the full dynamic range of the ADC should be used. This condition implies that the minimum bias voltage should ideally match the potential difference for the most alkaline solution possible. Additionally, optimally, the maximum offset value would match the potential difference for the most acidic sample.  
The minimum offset voltage is obtained by selecting the greatest resistance value of the potentiometer, RV1, in Figure 3. Recalling that a 2.5 V stable source feeds the voltage divider network, by simple inspection, the minimum bias voltage is determined to be 840 mV. The maximum offset, however, depends not only on the divider network (with a theoretical value of 2.5 V), but also on the amplifier and acquisition setup. From Figure 3, it is trivial to anticipate a three-fold gain for the amplification stage. Furthermore, assuming a conventional ADC configuration with an analog reference voltage \(V_{AREF}\) of 5 V, the maximum input signal will be given by \(V_{AREF}/GAIN\), or quantitatively 1.67 V.  
A device exhibiting Nernstian response would have a full detection range of \(\pm\)414 mV, centered ideally at 0 V for a neutral pH. In global VF applications, the aforementioned range denotes theoretical extrema of practically all available sensors.  
Combining the effects of bias and electrode response, the signal conditioning characterization is obtained before and after the amplification stage. The minimum theoretical voltage should be 426 mV before amplification and 1.278 V after. Meanwhile, as stated above, the maxima have an additional restriction imposed by the ADC. Thus, the maximum voltage should be 1.67 V before amplification and 5 V after. This analysis serves to demarcate the working bias range of the signal conditioning stage. Assuming the Nernstian response, end users can select any offset voltage in the 840-1250 mV range. From this analysis, we detect an area of improvement; namely, the optimization of the ADC dynamic range. Such improvement is addressed in the discussion section.
# From NFT 1.0 to NFT 2.0: A Review of the Evolution of Non-Fungible Tokens
## 3 Blockchain and Solutions for NFT
### Other Solutions
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Platform** & **Transaction Speed** & **Confirm Time** & **Transaction Cost** & **Smart Contact** & **Consensus Algorithm** & **NFT Standards** & **Application Fields** \\ \hline _Eleventure_ & 15-25 tps & \(-\)12 s & \(\sim\)USD +-USD 5.5 & Solidity & PoS & ERC-721, ERC-1155, ERC-988 & General purpose \\ _Flow_ & 1000 tps & \(-\)2.5 s & \(\sim\)USD 0.03 & Solidity & PoS & ERC-721, ERC-1155 & Arts and sports collectibles \\ _Carkamo_ & 250 tps & 5-60 s & \(\sim\)USD 0.15-USD 0.3 & Plutus & PoS & CIF-721 & Collectibles and videogames \\ _EOS_ & 4000 tps & 1.5 s & No fees & C++ & DPoS & dGoods SimpleAssets, AtomicAs-sets & Videogames \\ \hline Texas & 40 tps & 30 s & USD 0.001 & Michelson & LPsG & FA2 & Arts and collectibles \\ \hline _BSC_ & 300 tps & 3 s & \(\sim\)USD 0.1–USD 0.2 & Solidity and Vyper & PoSGA & BEP-721 & Collectibles \\ _Algorant_ & 600 tps & 3.7 s & USD 0.0002 & TEAL & Pure PoS & Algorand Standard Assets (ASAs) & Collectibles \\ _Informable_ & \(\sim\) 900 tps & - & No fees & Solidity & PoW & ERC-721 & Videogames \\ \hline _WAX_ & 8000 tps & 30 s & No fees & C++ & DPoS & SimpleAssets, AtomicAssets & Videogames \\ \hline Polygon & 7200 tps & 2 s & \(\sim\)USD 0.01–USD 0.05 & Solidity & PoS & ERC-721, ERC-1155, ERC-988 & Collectibles \\ \hline _Solum_ & 8500 & 0.4 s & USD 0.0002 & Rust, C, and C++ of stake & Proof of history and proof of stake & Metaplex & Arts and collectibles \\ \hline _Ronit_ & 200 & 3 s & USD 0.0016 & Solidity & Byzantine fault-tolerant proof of authority & ERC-721, ERC-1155, ERC-988 & Videogames \\ \hline _NFTSSL_ [63] & 15-25 tps & \(-\)5 min & - & Solidity & PoW & ERC-721, ERC-1155, ERC-988 & General purpose \\ \hline \end{tabular}
\end{table} 
Table 4: Blockchain for NFTs—A comparison.
## 2 Results
### Quorum Sensing Inhibitors
#### 2.2.1 RIP  
RNA III inhibiting peptide (RIP) is a seven-amino acid molecule capable of inhibiting the synthesis of RNAIII, a transcriptional unit of the staphylococcal accessory gene regulator (Agr) system responsible for QS and biofilm formation [108; 109].  
This peptide has been proven to be effective in treating device-associated infections due to MRSA and MSSA [110; 111; 112; 113], and its role in skin wound healing in animal models has also been evaluated. [PERSON] et al. [39] showed in a mouse model that _S. aureus_ and _S. epidermidis_ biofilm reduced the rate of wound healing significantly compared with an uninfected wound, with a histologically lower degree of re-epithelialisation. Infected mice treated with topical RIP 100 mcg for 7 days had a histologically comparable degree of healing to uninfected mice. This underlines the possible topical role of RIP in preventing biofilm and ensuring better wound healing.  
The role of topical RIP (20 mcg) in the healing of MRSA-infected wounds in combination with daily teicoplanin (7 mg/kg) for 7 days compared with teicoplanin alone has also been demonstrated in a mouse model, resulting in a lower bacterial load and histological degree of healing (collagen, re-epithelialisation, microvascular density and expression of VEGF comparable to non-infected wounds [40]. It is also hypothesised that RIP may induce VEGF by improving the quality and speed of wound healing [39; 40; 112; 113; 114].  
Topical daily RIP was also used in two cases [115] of patients with chronic diabetic ulcers after failure of conventional antibiotic therapy, in systemic combination with systemic daptomycin, avoiding amputation of the lower limb.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 1 Introduction
### Organization  
The rest of this paper is organized as follows. Section 2 presents the model of the NOMA-based cognitive UAV short-packet communication system and formulates the problem under some constraints. Section 3 describes the method for the specific solution. Section 4 provides a simulation to verify the proposed method. Finally, Section 5 concludes the paper.
# AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement
## 3 AE-DTI Model Design
### AE-FS Feature Selection Algorithm Design
#### 3.1.1 AE-FS Algorithm Principle  
Data dimensionality reduction is a crucial task in reducing the dimensions of data while preserving essential information. Autoencoder, a powerful method for dimensionality reduction, utilizes neural networks to learn the mapping relationship between the high-dimensional representation of data and its compressed representation. In comparison to linear methods, autoencoder excels in capturing the nonlinear structure of complex data. Furthermore, it is capable of adaptively learning the optimal feature representation without the need for a predefined feature extraction method. The conventional autoencoder operates as an unsupervised algorithm. Initially, it compresses the input image, denoted as \(A\), through a neural network to extract critical features from the data. Subsequently, it reconstructs the data as \(A^{\prime}\) by decoding these important features. By comparing the errors between \(A\) and the reconstructed data \(A^{\prime}\), the reverse gradient is updated iteratively to enhance the accuracy of the autoencoder. Following model training, the first half of the network can be selected to compress the dataset effectively, achieving the objective of dimensionality reduction. The specific network structure is illustrated in Figure 2. Building upon the traditional autoencoder dimensionality reduction method, AU-FS has been devised as outlined below.  
First, AE-FS uses the original dataset to train the autoencoder and calculates the reconstruction error \(l\). Then, the AE-FS systematically eliminates each feature individually and re-trains the autoencoder model to obtain a new reconstruction error \(l^{\prime}\). Finally, AE-FS calculates the score of each removed feature by comparing the difference value between  
Figure 1: Flow chart of AE-DTI.  
Figure 2: Autoencoder network structure.  
\(l\) and \(l^{\prime}\), thereby selecting those features whose scores are higher than a set threshold. In short, AE-FS mainly evaluates the importance of each feature globally by training a feature scoring network and then selects key features to reshape one-dimensional data according to the set scoring threshold. The network structure diagram is shown in Figure 3.
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 6 Performance Evaluation
### Network Service Delay  
The service delay is determined by the total provisioning time required to allocate cached NFs on the nodes, time required to provide a copy of a cached NF, and the time required to map any uncached NFs, i.e., instantiation delay. This delay also includes the hopping time over all intermediate nodes and link propagation delay in the path from the _src_ to the nodes that host the NFs. Given the stringent delay, it is vital here to provide services at the least delays. Figure 3 shows the service provisioning delay for the proposed caching methods for various numbers of incoming MS requests versus the conventional uncached methods. At low incoming requests, the network is still underutilized and most of the nodes are unoccupied. Therefore, the delay here is relatively low for the two methods, e.g., ranges of 2.5-3 ms and 3-3.5 ms for the clustered and distributed methods for the first 80 requests, respectively. In the clustered approach, incoming requests are routed to the secondary nodes that host all the cached NFs. This routing leads to hopping over primary nodes, which causes a slight delay (e.g., 3 ms at 80 requests) without providing NFs to them.  
In the distributed method, the cached NFs are hosted at the primary nodes in Layer II, which requires each node to provide service to the request, thus yielding in increased delay, e.g., 4.4 ms at 80 requests. At high traffic volumes, some NFs are uncached, which results in mapping requirements of new NFs. Along with this, the distributed method maps these additional new NFs on the established caching path, which aggregates the delay further. Meanwhile, the clustered approach maps the new NFs on the direct neighbors of the secondary node, which yields in shorter paths, thus yielding shorter delays that approach 5.5 ms at 200 requests.  
Meanwhile, the uncached methods suffer from noticeable delays attributed to the provisioning delays of nodes after the terminals initiate service requests. Here, both the distributed and clustered uncached methods search for the best node that yields in the least aggregate path delays to host the network functions. This is opposed to the proposing caching methods that allocate the NFs in advance, thus reducing the instantiation time of the functions running on the nodes. Here the distributed uncached method suffers the highest delay approaching 9.5 ms at 200 requests, i.e., attributed to the high number of nodes in the request path hosting the NFs. This delay is reduced in the clustered uncached method by aggregating the NFs on a single node, i.e., conditioned by available resources, thus reducing the total nodes in the path, which minimizes the link delays between the nodes.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Enhancing the EPR Effect by Targeting the Tumor microenvironment (TME)  
The EPR effect is a cornerstone of nanodrug delivery, leveraging the leaky vasculature and impaired lymphatic drainage of tumors to facilitate the accumulation of therapeutic agents. However, the variability of the EPR effect across different tumor types and individuals poses a significant challenge to the consistent efficacy of nanodrugs. To address this limitation, innovative strategies targeting the tumor microenvironment (TME) have been developed. These strategies include the molecular targeting of specific TME markers, the use of external physical methods, and physiological modifications of the TME to enhance drug delivery and therapeutic outcomes [167].
## 2 Methodology  
### Elastic Wave Equation  
The two-dimensional elastic wave equation can be expressed as:  
\[\left\{\begin{array}{l}\rho\frac{\partial^{2}u_{x}}{\partial t^{2}}=(\lambda+2 \mu)\frac{\partial^{2}u_{x}}{\partial x^{2}}+(\lambda+\mu)\frac{\partial^{2}u_{ x}}{\partial x\partial z}+\mu\frac{\partial^{2}u_{x}}{\partial x^{2}}+f_{x}\\ \rho\frac{\partial^{2}u_{x}}{\partial t^{2}}=\mu\frac{\partial^{2}u_{x}}{ \partial x^{2}}+(\lambda+\mu)\frac{\partial^{2}u_{x}}{\partial x\partial z}+( \lambda+2\mu)\frac{\partial^{2}u_{x}}{\partial z^{2}}+f_{z}\end{array}\right. \tag{1}\]  
where \(t\) is the time, \(\rho\) is the density of the medium, \(\lambda\) and \(\mu\) are Lame constants, \(\lambda=\rho\left(v_{p}^{2}-2v_{s}^{2}\right)\), \(\mu=\rho v_{s}^{2}\), \(v_{p}\), and \(v_{s}\) are the velocities of compressional and shear waves, \(u_{x}\) and \(u_{z}\) represent displacement in the \(x\) and \(z\) directions, and \(f_{x}\) and \(f_{z}\) represent source terms in the \(x\) and \(z\) directions.
# Neural Graph Similarity Computation with Contrastive Learning
## 3 Methodology
### Contrastive Learning  
To obtain the labels for learning graph similarity, existing works adopt traditional exact algorithms to compute the GED distance between two graphs [7]. Nevertheless, as mentioned above, these algorithms tend to require extremely longer running time given larger graphs. Consequently, the ground-truth labels are actually lacking and difficult to obtain.  
To mitigate this issue, we propose an unsupervised contrastive loss to learn expressive graph representations and help predict more accurate similarity scores. Contrastive learning aims to make graph representations agree with each other under proper transformations [47]. Hence, by maximizing the agreement between two augmented views of the same graph via a contrastive loss in the latent space, more expressive and generalizable graph embeddings can be learned in the absence of labeled data. The learned embeddings can further facilitate downstream applications, such as the graph similarity computation task in this work. Next, we first introduce the graph augmentation strategy and then present the unsupervised contrastive loss.
# Transitioning Broadcast to Cloud
## 2 Processing Chains in Broadcast and Cloud-Based Online Video Systems
### Contribution and Ingest  
In broadcast, live input streams (or \"feeds\") originate from remote or field production. They are normally encoded by a contribution encoder, and delivered to broadcast center over a certain physical link (dedicated IP, satellite, 4G, etc.). The encoding is always done using one of the standard TV formats (e.g., 480i SD or 1080i HD), and with a number of codec- and TS-level constraints applied, making such streams compatible with broadcast  
Figure 2: Conceptual diagram of processing operations in cloud-based online video platform.  
systems [24; 25; 26; 27; 28; 29; 30; 31; 32]. When streams are sent over IP, real-time UDP (User Datagram Protocol)-based delivery protocols are normally used. Examples of such protocols include RTP [33], SMPTE 2022-1 [34], SMPTE 2022-2 [35], Zixi [36], etc.  
Pre-recorded content usually comes in form of files, produced by studio encoders. Again, only standard TV/broadcast video formats are used, and specific codec- and container-level restrictions are applied (see e.g., [37]). Moreover, in most cases, the contribution (or so-called \"mezzanine\") encodings are done at rates that are considerably higher than rates used for final distribution. This allows broadcast systems to start with \"cleaner\" versions of the content.  
In case of online video platforms, input content generally comes from a much broader and more diverse set of sources--from professional production studios and broadcast workflows to user-generated content. Consequently, the bitrates, formats, and quality of such streams can also vary greatly. This forces OVPs to be highly versatile, robust, and tolerant on the ingest end.  
The quality of links used to deliver content to OVPs may also vary greatly. From dedicated connections to datacenters, to public Internet over some local ISPs. UDP may or may not be available.  
In such a context, the live ingest protocol that become most commonly used, remarkably enough, is RTMP (Real-Time Messaging Protocol) [38]. This is an old, Flash-era protocol, with many known limitations, but it works over TCP (Transmission Control Protocol), and remains a popular choice for live to cloud ingest. Other protocols for live ingest include SRT (Secure, Reliable Transport) [39] and RIST (Reliable Internet Stream Transport) [40].