esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 New Data Structure
### Coding Schemes  
#### 3.1.1 Canonical Code  
This coding scheme corresponds to the idea of [PERSON] and [PERSON] (2010), where it is also shown that the coding achieves empirical entropy of order \(k\). It works as follows. We order all possible bit strings by their length and, as secondary criterion, by their binary value. We get the following sequence: \(\epsilon,0,1,00,01,10,11,000,001\ldots\). Note that we can calculate the position \(j\) of a bit string \(s\) in this sequence: \(j=1s-1\), where \(1s\) is the bit string \(s\) with a preceding \(1\), interpreted as a binary number. We use the first element of this sequence to encode the most frequent block, the second one for the second most frequent block, _etc._ This is obviously optimal in terms of memory consumption as long as we assume the beginnings and endings of code words to be known.  
As long as each block is encoded in a code of fixed length \(b\) the code of a specific block \(i\) can be found by simple arithmetic. However, the Canonical Code produces variable length code words and thus we require a technique to find the desired block code. We next describe two approaches to achieve that.  
One option is to maintain a two-level pointer structure [PERSON] and [PERSON] (2010), similar to the top and mid-level of the rank directory described in Section 2.3. For each large block we store the beginning of its first encoded small block directly in \(\log n\) bits using \(O(\frac{n\log n}{b_{l}})=o(n)\) bits in total. We call it the _top-level pointers_. The _mid-level pointers_ defined analogously and use \(\frac{n\log(b_{l})}{b_{s}}=o(n)\) bits. As for rank structures this only requires constant time to access, but the space consumption is much more significant than the \(o(n)\) indicates.  
An alternative approach is to store a bit vector parallel to the encoded bit vector, where we mark the beginning of each code word with a \(1\) and prepare it for select queries (as previously done in Saddakane and Okanohara (2010); Saddakane and Okanohara (2010), for example). To find the \(i\)th code word we can use \(\mathsf{select}_{1}(V,i)\) on this _beginnings vector_\(V\). If the original text is highly compressible, the encoded bit vector and thus the beginnings vector is rather short compared to other data structures used. Otherwise, it is sparsely populated and can be compressed by choosing an appropriate select dictionary, for example the sparse array by [PERSON] and [PERSON] Sadakane and Okanohara (2010). On the downside, using select to find the proper code word comes with constant but significant run-time costs in practice.
# Cryogenic System for FTIR Analysis of Hydrocarbon Fuels at Low Temperature and Atmospheric Pressure
## 4 Discussion  
The obtained results open up new application areas for low-temperature FTIR spectroscopy of hydrocarbon fuels:  
Identification of frozen fuels and lubricants: the spectral \"signature\" of hydrocarbons at 80 K can be used to identify the fuel type in samples stored or found in a frozen state (e.g., when investigating accidents in the Far North). The method allows one to determine without heating whether a frozen hydrocarbon liquid is kerosene, diesel fuel, jet fuel, or gasoline based on the position and intensity of characteristic bands.  
Cryostorages and space research: the cryogenic FTIR analysis technique is applicable in the design of long-term storage systems for hydrocarbon energy carriers at ultra-low temperatures (e.g., fuel reserves in polar regions or at near-Earth orbital stations). It also holds promise for simulating extraterrestrial conditions, such as studying the interaction of hydrocarbons with the surface at the temperature of Titan's moon (-94 K) and for testing fuel tank materials in the cold of space.  
The practical value is confirmed by the possibility of using the method to identify fuels. In the future, it is planned to automate the process of cryo-spectroscopic analysis, create an autonomous cell capable of cooling and scanning samples without operator participation, as well as to extend the technique to other types of fuel. This work contains results only for hydrocarbons such as methanol, kerosene and ethanol; future works could involve other hydrocarbon compounds, like gasoline, diesel, natural gas, aviation kerosene, their mixtures, etc. That would help with identifying the presence of these fuels in different ices (polar, extraterrestrial) at low temperatures and their properties and behavior under those conditions. Future studies using this experimental setup will also allow for a more detailed study of the physicochemical behavior and phase transitions of various substances under cryogenic conditions at atmospheric pressure. In the long term, this may contribute to the development of analytical tools for planetary science, astrochemistry, and environmental monitoring of fuel pollutants in frozen environments.
## 2 Basic Framework of the System  
The gas-electric IES mainly consists of a photovoltaic electricity generation device, a carbon capture device, a gas-fired unit, an electric-to-gas converter, an energy storage battery, a gas storage tank, and other devices. The basic framework of the system is shown in Figure 1. In this system, if there is still surplus after photovoltaic power and other output units meet the load demand, the electric-to-gas converter can use this surplus electricity to synthesize methane. Part of the carbon dioxide required for the synthesis of methane can be captured and provided by the carbon capture system. The methane obtained can be directly used for the supply of gas load or stored. On the one hand, the completion of electric-to-gas conversion and the coupling relationship between various devices in the system realizes the conversion from electric energy to gas energy and optimizes the utilization of energy; on the other hand, the CO\({}_{2}\) captured by the carbon capture system can be fully utilized to realize carbon-to-methane conversion, which improves the overall economic benefits of the system.  
The low-carbon energy sharing framework of multiple regional gas-electric integrated energy systems built in this paper is shown in Figure 2. In the traditional single energy system, it is difficult to keep a balance between energy supply and energy demand. Outside the system, energy can be shared with other subsystems, or energy can be traded with the electricity grid and the gas grid; within the system, devices of energy conversion are usually configured to realize the conversion among multiple energy sources such as electricity and gas, so as to solve the problem of energy mismatch in the time and space of the system. However, in the process of energy trading, the system also needs to face the challenges brought about by multiple uncertainties such as load prediction and new energy output.  
It can be seen from Figure 2 that electricity and gas are the two main types of energy involved in each IES, and all subsystems are connected to the electricity tie line through gas pipelines, so that multiple types of energy can be shared. Moreover, each subsystem is equipped with a communication device, through which information can be exchanged with adjacent systems. Noticeably, since the research target is the regional IES and multi-IESs are close to each other in the scenarios under consideration, more attention is paid only to the constraints from power interaction between subsystems rather than other constraints from grid power flow security.  
Figure 1: Framework of the regional gas–electric integrated energy system.
# Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering
## 2 Background and Related Work
### Page Stream Segmentation  
Regarding page stream segmentation, the field has developed from using statistical approaches based on page similarity to using machine learning approaches. In 2002, [PERSON] and [PERSON] [PERSON] and [PERSON] (2002) used bottom-up hierarchical clustering in combination with a classifier working on rule-based page representations which yields page similarities to segment streams into documents. The main difference between their approach and the approach used in this paper is that we do not rely on handcrafted feature vectors containing layout information, but that we instead use vectors obtained from a neural vision model, which is expected to encode many of these features implicitly. Additionally, we limit our studies to clustering pages on their visual features, instead of incorporating textual information.  
In 2009, [PERSON] and [PERSON] [13] extracted specific fields from the images of pages as features for a left-to-right (Bakis) hidden Markov model. The first study that viewed PSS only as a classification problem was that of [PERSON] et al. [14], who used a Bag of Visual Words (BoVW) representation and three different classifiers: Support Vector Machines (SVM), Random Forest and Multilayer Perceptron (MLP). In 2014, [PERSON] et al. [15] used both image and text as input for their SVM and were the first to use multimodal machine learning for PSS. In 2021, with the use of transfer learning, [PERSON] and [PERSON] [2] achieved state-of-the-art PSS performance by creating a multimodal classification model using the VGG16 architecture [6] for images and a pretrained FastText [16] model for word embeddings. [PERSON] et al. [3] experimented with using domain specific BERT [17] models and found that this only yielded a minor improvement. Although these neural binary classification methods have proven successful, there is still room for improvement, as these models learn to rely solely on the features of individual pages for classification, instead of leveraging interpage relationships. By using agglomerative clustering, we explicitly model these interpage dependencies, allowing the model to utilize the information from surrounding pages. Our work is related to that of [PERSON] et al. [1], where they included the interpage context by adding page dependencies using 33 semantic classes, which increased the performance substantially. The main difference in our method is that we do not add explicit relationship information to the data, but rather model these relationships by using agglomerative clustering, which makes these possible relationships explicit.
# Requirements for Crafting Virtual Network Packet Captures
## 4 Use Cases
### Security  
Network security and protection is relevant in modern environments. Due to the increasing speed and diversity of network packets, the detection of network-based attacks is a complex task. Preventing adversarial attacks is typically done with techniques such as machine learning, but the deployed algorithms need realistic and correct network packets to train and improve the models. Incorrect, static or incongruuous information leads to wrong results and therefore useless models. So, training the models with appropriate information is crucial for a high level of security. As shown in [18], the shift from hardware-based to virtual networks impacts every involved network device that analyses the transferred network traffic. Whereas performance issues or a huge resource load might be detected in an easy way, the detection of misconfigured or incomplete rules for the protection of the network is critical but extremely difficult. Because of this, the process of implementing new network devices or new algorithms for the classification of network packets in virtual networks needs a valid baseline of well-known information. By using a known data-set of the traditional network and transferring this capture to a capture file with adapted information of the intended virtual network, the overall security benefits result in a clean and correct process of packet generation. The re-configuration of the firewall mentioned in Section 4.2 improved the security of a virtual network.
## 8 Future Research Directions  
In this review, some key limitations have centered around the reasonably new introduction of passkeys as a concept. Most surveys have focused on enterprise settings, university employees, and students, meaning the contextual and longitudinal data is relatively limited.  
With the rapid adoption and further development efforts for the passkey, FIDO2, and WebAuth specifications pushed by large influential organizations such as Apple, Microsoft, and Google, the landscape of passwordless authentication is evolving at an  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Improvement** & **Challenges** \\ \hline Technical & 3 & 2 \\ Usability & 3 & 1 \\ User Perception & 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impact rating of each category for the areas of improvements and challenges from a user perspective, with one being low and three being high impact.  
Figure 6: Overview of the identified impact of the Improvement and Challenge Categories.  
unprecedented pace. This growth in development and deployment creates a seedbed for future research that widens the demographics surveyed and checks for similarities and differences in this area based on characteristics such as age, gender, nationality, socioeconomic background, etc. In particular, surveys need to be broadened to cover passkey adoption across more diverse user groups in different contexts based on parameters such as socioeconomic factors, age, and job collar.  
Another focus area would be users' technical challenges today, such as passkey recovery practices, account sharing capabilities, and cross-device synchronization/transferability. The papers consider these concepts to be key hindrances to the widespread adoption of passkeys. Therefore, this focus area should not be limited to technical feasibility alone; it should also consider the usability implications and potential security impacts to ensure a smoother transition.  
Another perspective that has been found significant during this literature review is the perception of security and how the user feels secure. Some results suggested that passkeys and other FIDO2 authenticators, such as security keys, were adopted too quickly, which, together with the lack of knowledge of the under-the-hood workings of the specification and the technical application of the same, showed signs of users lacking the needed trustworthiness for the technology. A survey in this direction should aim to bridge the division between perceived and actual security, ultimately ending in a more favorable attitude towards passkey adoption.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 2 System Hypotheses and Signal Modeling
### Sounding Reference Signal in 5G NR
#### 2.2.2 Resource Mapping  
To transmit the SRS in the 5G NR frames, the generated Zadoff-Chu sequence \(\mathbf{r}_{\mathbf{zc}}(\mathbf{n},\mathbf{i}^{\prime})\) in Section 2.2.1 is mapped to the given physical resources (which include subcarriers and time slots). The mapping can be described by Equation (3), which is defined in Chapter 6.4.1.4.3 of the 3 GPP TS 38.211 specifications [16].  
\[\mathbf{S}_{\mathbf{K}_{TC}\mathbf{k}^{{}^{\prime}}+\mathbf{k}_{0},\,\mathbf{ i}^{\prime}+\mathbf{l}_{0}}=\begin{cases}\beta_{\mathbf{SRS}}\mathbf{r}  
reference element. We use an incident signal of a single source \(\mathbf{k}\) from direction \((\theta_{k},\phi_{k})\). The array manifold vector can be written as:  
\[\mathbf{a}(\theta_{k},\phi_{k})=\mathbf{a}_{z}(\theta_{k})\otimes\mathbf{a}_{c} (\phi_{k}) \tag{6}\]  
where the operator \(\otimes\) is the Kronecker product. \(\mathbf{a}_{z}(\theta_{k})\) and \(\mathbf{a}_{c}(\phi_{k})\) are the azimuth and elevation manifolds, respectively, defined as:  
\[\mathbf{a}_{z}(\theta_{k}) =e^{\frac{-2\pi d}{\lambda}}[1,e^{-j\pi\lambda cos\phi_{k}},...,e^{ -j(M-1)\pi\lambda cos\theta_{k}}]^{T} \tag{7a}\] \[\mathbf{a}_{c}(\phi_{k}) =e^{\frac{-2\pi d}{\lambda}}[1,e^{-j\pi\lambda cos\phi_{k}},...,e^{ -j(N-1)\pi\lambda cos\phi_{k}}]^{T} \tag{7b}\]  
where \(\lambda\) is the wavelength and \(d\) is the array element spacing, which is the half wavelength in this work. For a single snapshot (sample), the congregate receiving signal from \(\mathbf{K}\) sources can be written as:  
\[\mathbf{y}=\mathbf{A}(\theta,\phi)\mathbf{x}+\mathbf{n} \tag{8}\]  
where \(\mathbf{A}(\theta,\phi)=[\mathbf{a}(\theta_{1},\phi_{1});\mathbf{a}(\theta_{2 },\phi_{2});...,\mathbf{a}(\theta_{K},\phi_{K})]\in\mathbb{C}^{MN\times K}\), \(\mathbf{y}\in\mathbb{C}^{MN\times 1}\), \(\mathbf{x}\in\mathbb{C}^{K\times 1}\), and \(\mathbf{n}\in\mathbb{C}^{MN\times 1}\) are one snapshot (sample) of the array receiving signal, incident signal, and noise, respectively.
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 4 Conclusions  
We investigated link adaptation for a mobile user, when the wanted signal and the interfering signals undergo fading, and when there may be changes in the transmissions of the interferers. Here, we consider MISO transmissions, and the variations in interfering transmission are related to the change of precoders in a cell. Mobility-induced signal and interference power changes and precoder changes cause SINR variation. Robust link adaptation is considered to meet a URC target of outage probability \(10^{-5}\) with delay \(\tau=1\) ms. For this, we assume that at the time of SINR measurement, the instantaneous values of the wanted signal power, and the individual interferer powers are known, in addition to the average values, and the distribution of these quantities. A suitable backoff is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(S_{0}\)/\(S_{\mathrm{ave}}\) & \(I_{0}\)/\(I_{\mathrm{ave}}\) & Sig. Power & Intf. Power & Combined Effect \\ \hline
0 & 0 & 8.01 & 2.5 & 8.53 \\
0 & \(-10\) & 8.01 & 6.76 & 11.30 \\ \(-10\) & 0 & 44.17 & 2.5 & 44.82 \\ \(-10\) & \(-10\) & 44.17 & 6.76 & 46.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Required backoff values for mobility induced SINR variability.  
then chosen based on the statistical characteristics of the realized SINR to avoid outage due to CQI misprediction.  
Under Rayleigh fading assumption, we develop closed form expressions for the signal and interference power distributions, as well as the SINR distribution. Users experiencing a deep fade in either the interference or wanted signal power during the time of measurement require large backoffs to achieve ultra-reliable transmission with low outage. When the channel during the measurement is close to an average situation, small backoffs are required. This demonstrates that the accuracy of information of the current fading situations, as well as the fading statistics, are crucial for robust link adaptation. In comparison to the mobility-induced backoffs, backoffs related to variability of interference precoders are small, when the number of transmit antennas \(N_{T}<10\).  
This research work has been partially supported by EIT ICT Labs under the EXAM and ACTIVE -projects.  
[PERSON], [PERSON], and [PERSON] conceived and planned the work; [PERSON] played a leading role in the theoretical modeling; [PERSON], [PERSON], and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 6 Various Criteria for Assessing the Power Quality and Efficiency of the Studied System
### Power Quality
#### 6.1.3 Zero-Sequence Voltage (ZSV)
In [148], the solution involved integrating auxiliary switches, while, in [91], an approach was suggested where one side of the OEMM is fed by a three-level inverter, and the other side is fed by a two-level inverter. However, these solutions require additional hardware, which increases costs. Therefore, a more cost-effective solution would be to design appropriate control strategies to eliminate the ZSV. For instance, in [149], the authors proposed desynchronizing the control between the two inverters to create a 120\({}^{\circ}\) phase shift between the voltages of the first and second inverters. Similarly, the authors of [150; 151] employed a 180\({}^{\circ}\) phase shift and introduced the use of even or odd synchronous vectors. In [152], two approaches were suggested that allow for free selection of DTLI switching states. However, these approaches not only eliminate the ZSV but also restrict the inverter switching. Finally, the authors of [48; 153] developed a technique called Decoupled Sample-Averaged Zero-Sequence Elimination, which forces the ZSV to zero while minimizing switching losses. A new method tailored for low switching frequencies, called Synchronous Off-Line Optimum PWM, was presented in [154].
# Investigation of Open Air Stability of CsPbBr3 Thin-Film Growth on Different Substrates
## 3 Results and Discussion
### Surface Chemical Evolution
On the other hand, samples obtained on gold and ITO substrates resulted enriched in cesium and lead and depleted in bromine, probably as a consequence of the quick degradation of the perovskite structure and the formation of volatile bromine byproducts that, in the ultra-high vacuum conditions in which XPS experiments were carried out, tended to be removed from the surface, indicating, on the whole, higher instability of the deposits grown on these substrates.
# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 4 Experiment
### Different Community Discovery Algorithms  
In the above experiment, we use LFM as the community discovery algorithm. In addition to the LFM algorithm, there are many other efficient community discovery algorithms. This section studies the performance of MCEP on several commonly used community discovery algorithms. These community discovery algorithms are the LFM algorithm [25], the Cluster Percolation method [27], the greedy algorithm [28], and the Louvain algorithm [29]. Among these four algorithms, the first two are overlapping community discovery algorithms, and the latter two are non-overlapping community discovery algorithms. The experimental results are shown in Table 9. We conducted a comparative experiment on the DBLP dataset. For the greedy algorithm and the Louvain algorithm, because the number of positive samples of shrink events is too small, there is no way to predict its accuracy, so there is no result in that part. The experimental results are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Event** & **GL-B-MCEP** & **GL-MCEP** & **G-B-MCEP** & **G-MCEP** \\ \hline Survive & 94.36\% & 90.81\% & 94.71\% & 88.31\% \\ Dissolve & 80.74\% & 76.83\% & 83.30\% & 74.90\% \\ Grow & 75.64\% & 67.54\% & 75.18\% & 68.10\% \\ Merge & 97.82\% & 94.08\% & 97.79\% & 96.05\% \\ Shrink & 81.92\% & 73.49\% & 73.95\% & 69.64\% \\ Split & 77.85\% & 70.26\% & 74.92\% & 71.36\% \\ Aver & 84.72\% & 78.84\% & 83.31\% & 78.06\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of ablation experiments:ia-reality-call.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Event** & **GL-B-MCEP** & **GL-MCEP** & **G-B-MCEP** & **G-MCEP** \\ \hline Dissolve & 71.16\% & 72.49\% & 70.54\% & 72.02\% \\ Grow & 92.42\% & 84.02\% & 89.16\% & 87.69\% \\ Merge & 98.45\% & 97.12\% & 97.86\% & 96.24\% \\ Shrink & 96.11\% & 91.14\% & 92.77\% & 89.49\% \\ Split & 79.68\% & 73.98\% & 72.98\% & 71.76\% \\ Aver & 87.56\% & 83.75\% & 84.66\% & 83.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of ablation experiments: six-askubuntu-c2q.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Event** & **LFM** & **CPM** & **Greedy** & **Louvain** \\ \hline Survive & 97.99\% & 81.18\% & 96.32\% & 95.88\% \\ Dissolve & 76.02\% & 80.78\% & 80.12\% & 87.30\% \\ Grow & 84.81\% & 97.15\% & 95.10\% & 87.60\% \\ Merge & 90.31\% & 93.93\% & 94.53\% & 94.19\% \\ Shrink & 99.46\% & 96.90\% & - & - \\ Split & 88.93\% & 99.82\% & 90.80\% & 91.92\% \\ Aver & 89.59\% & 91.54\% & 91.38\% & 91.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: The results of MCEP under different community algorithms.