esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Two Detector Magnetic Systems for the Future Circular Hadron-Hadron Collider
## 2 Modeling the Magnetic Systems
### Minimal Steel Yoke Magnetic System  
To realize the minimal steel yoke conception [5], the main coil is assumed to be surrounded by five barrel wheels of 17.5 m outer diameter and 3.9 m width each, as shown in Figure 3. Each barrel wheel has two 0.75 m thick steel layers with a radial gap of 0.5 m between them.  
Figure 2: Three-dimensional model of the FCC-hh detector baseline magnetic system comprising the main superconducting coil with 10.9 m inner diameter, and two superconducting forward coils with 5.6 m inner diameter. The distance between the coils is 2.823 m each side.  
At the distance of 0.75 m off the extreme wheels, two endcap disks of 17.5 m outer diameter and 0.75 m thickness are located at each barrel end. Four smaller disks of 14 m outer diameter at each yoke end follow these disks. The thicknesses of the first small disks on both sides of the yoke are 0.5 m, while other disks are 0.75 m in thickness.  
The presence of 0.5 m air gaps between all the endcap disks allow room for installation of the muon detection chambers located in the same positions as in the baseline design of the FCC-hh detector [3] shown in Figure 1. As displayed in Figure 4, the inner parts of the endcap disks rely on the cylindrical radiation protection shields of 1 m radial thickness assumed to be made of a carbon steel. These shields have a length of 7 m and an outer diameter of 10 m each.  
Figure 4: The main superconducting coil with an outer diameter of 11.9, the five barrel wheels measuring 3.9 m in width, the two cylindrical radiation protection shields of 10 m outer diameter and 7 m long around each forward coil, the four endcap disks of 17.5 m diameter each, and the eight endcap disks of 14 m diameter each are shown. The main solenoid coil is visible between the barrel wheels in the air gaps of 0.125 m each. The length of the barrel part around the main coil is 20 m; the total length of the yoke is 35 m. The color scale of the magnetic flux density on the yoke surface is the same as in Figure 3.  
Figure 3: Distribution of the magnetic flux density on the minimal steel yoke surface of the FCC-hh detector. The color scale unit is 0.5 T. With the central magnetic flux density of 4.24 T, the minimum and maximum magnetic flux density values on the yoke surface are 0.2037 and 4.0357 T.  
In contrast, in the baseline design of the magnetic system shown in Figure 1, these shields are assumed to be made of nonmagnetic material. The total length of the minimal steel yoke is 35 m.  
The main superconducting coil and the forward solenoid coils have the same dimensions and the same total currents as in the baseline magnetic system design.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description
### High-Pass Filter  
A high-pass filter allows frequency components above a specific cutoff frequency while suppressing those below that. In addition to the basic mathematical formulas mentioned earlier, the design of IIR high-pass filters also incorporates other important mathematical concepts and formulas [15], such as the Frequency Conversion of Equation (3); a standard method uses frequency transformation formulas to convert low-pass filters into high-pass filters.  
\[s=\frac{\omega 0}{s^{\prime}} \tag{3}\]  
Alternatively, Equation (4) of group delay is used. Group delay measures how a filter's phase response changes with frequency, indicating the filter's delay for different frequency components.  
\[\tau(w)=-\frac{d\phi(\omega)}{d\omega} \tag{4}\]
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 3 Methodology
### Overall Structure  
The backbone network of MIHE-Net is structured into four sequential stages. The input and output of stage \(l\) are \(F_{l-1}\) and \(F_{l}\), respectively. The input \(F_{l-1}\) undergoes down sampling and is then split into two parts, \(X_{l-1}^{(1)}\) and \(X_{l-1}^{(2)}\), along the channel dimension, which are fed into two separate paths:  
\[\begin{split}\mathbf{X}_{l-1}=\text{Conv}_{3\times 3}(\text{DS}(\mathbf{F}_{l -1}))\\ \mathbf{X}_{l-1}^{(1)}=\mathbf{X}_{l-1}[:\frac{1}{2}C_{l},\ldots],\mathbf{X}_ {l-1}^{(2)}=\mathbf{X}_{l-1}[\frac{1}{2}C_{l}:,\ldots]\end{split} \tag{7}\]  
where \(DS\) denotes the downsampling operation and \(C_{l}\) denotes the number of channels. One path is a feed-forward network (FFN) that takes \(X_{l-1}^{(1)}\) as input, passes it through two 3 \(\times\) 3 convolutions, and outputs \(X_{l}^{(1)}\). The other path consists of the MCI module and the HEA module, taking \(X_{l-1}^{(2)}\) as input and producing \(X_{l}^{(2)}\). Specifically, \(X_{l-1,n}^{(2)}\) is processed by the MCI module to obtain the output feature \(P_{l-1,n}\), while the HEA module generates the attention weight \(A_{l-1,n}\). This attention weight \(A_{l-1,n}\) is then used to enhance the feature resulting from the MCI module, yielding the enhanced feature \(F_{l-1,n}^{\text{attn}}\). The formula is as follows:  
\[\mathbf{F}_{l-1,n}^{\text{attn}}=(\mathbf{A}_{l-1,n}\odot\mathbf{P}_{l-1,n})\oplus\mathbf{P}_ {l-1,n}. \tag{8}\]  
The enhanced feature \(F_{l-1,n}^{\text{attn}}\) is then passed through a 1 \(\times\) 1 convolution to produce \(X_{l}^{(2)}\):  
\[\mathbf{X}_{l,n}^{(2)}=\text{Conv}_{1\times 1}(\mathbf{F}_{l-1,n}^{\text{attn}}). \tag{9}\]  
The final output of the entire process is then  
\[\mathbf{F}_{l}=\text{Conv}_{1\times 1}(\text{Concat}(\mathbf{X}_{l}^{(1)},\mathbf{X}_{l}^{(2 )})), \tag{10}\]  
where _Concat_ denotes the concatenation operation.
# Prediction Method of Blast Wave Impact on Crew Module for Liquid Rocket Explosion on Launch Pad
## 6 Results and Analysis
### PredictionFormula of Peak Overpressure  
The peak overpressure data with different scaled distances are fitted to obtain the prediction formula of peak overpressure applicable to the explosion mode on launch pad. The first-order attenuation exponential function in the form of Equation (9) is used for fitting, and the fitting Equation (9) and the fitting curve in Figure 9 are obtained.  
Equation (9) describes the peak overpressure of blast wave acting on the crew modules as a function of the scaled distance and it can be used to predict the damage effect on crew modules of different stages explosions. As can be seen from Figure 9, when the scaled distance is small, the peak overpressure is very high. With the increase of the scaled distance, the peak overpressure decays rapidly and eventually tends to be flat. According to the calculated scaled distance, the peak overpressure of blast wave acting on the crew module can be obtained by consulting Figure 9, which greatly facilitates the prediction of the explosion hazard of the crew module.  
\[\Delta P=475.56*\exp(-\overline{r}/0.20)+2.26 \tag{9}\]
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 5 Real-World Evaluation  
In this section, we describe our real-world evaluation of the proposed method using a DJI Mavic 2 Pro drone. Due to local regulations, we had to limit the drone's flight altitude to 100 m; therefore the flights were performed at altitudes of 50 and 100 m. The first step was to capture the GPS locations and their corresponding video frames from the drone's video stream. To do so, we built an app that collects this information using DJI's Android mobile software development kit (SDK) [27]. The ability to downsample or oversample the video stream allowed us to simulate flight speeds that differ from the actual speed of the drone. We gathered the samples, which will serve as input to our model, and stored them on a PC.
## 4 Traffic Filtering-Based Techniques
### Filtering HSR Traffic (FHT)
#### 4.5.2 FHT Operations  
The FHT approach has two phases: learning phase and forwarding phase.  
* Learning phase: In this phase, the FHT learns and builds MAC tables using control messages.
* Building MAC1 table: The MAC1 table is built at access QuadBoxes. Each QuadBox periodically sends a Hello message over all its ports. When a DANH node receives a Hello message, it replies by sending an ACK message, and forwards the Hello message to other  
Figure 10: The process of filtering unicast traffic.  
DANH nodes. Each access QuadBox builds its MAC1 table based on ACK messages received from DANH nodes in its DANH ring. Based on the MAC1 table, an access QuadBox will not forward frames into its DANH ring if its MAC1 table does not contain the destination MAC address. * Building MAC2 table: The MAC2 table is built at trunk QuadBoxes. Once access QuadBoxes have built their MAC1 tables, they send MAC messages that contain all the MAC addresses of their MAC1 table to their QuadBox ring. Trunk QuadBoxes connected to the QuadBox ring receive the MAC messages and update their MAC2 tables. Based on the MAC2 table, a trunk QuadBox will not forward a unicast frame from its first QuadBox ring to its second QuadBox ring if the first QuadBox ring's MAC2 table contains the destination MAC address. * Forwarding phase: In the phase, QuadBoxes forward unicast frames based on MAC tables. The MAC1 table is used to prune the unicast frames for unused DANH rings, whereas the MAC2 table is used to filter the traffic for unused QuadBox rings. Additionally, based on the new traffic forwarding rule, the unicast frames are not doubled and circulated in the active rings.  
Figure 11 shows the process of forwarding a unicast frame from a source to a destination under FHT. The source node sends two copies of the frame simultaneously through each port in both directions in DANH ring 1. Access QuadBox 1 passes the frame to its QuadBox ring 1. When receiving the frame from QuadBox ring 1, access QuadBox 2 looks up its MAC1 tables and does not forward the frame to its DANH ring 2, since its MAC1 table does not contain the destination MAC  
Figure 11: Filtering unicast traffic under the FHT.  
address of the frame. Access QuadBox 2 sends the frame over another trunk port to trunk QuadBox 7. Trunk QuadBox 7 looks up its MAC2 table and then does not forward the frame from its QuadBox ring 1 to its QuadBox ring 2, since its QuadBox ring 1's MAC2 table contains the destination MAC address. Finally, access QuadBox 6 forwards the frame into its DANH ring 6 because its MAC1 table contains the destination MAC address of the frame. The destination node receives two copies of the frame from both directions of DANH ring 6, passes the first frame to its upper layers, and discards the duplicate. According to the new forwarding rule for QuadBox nodes, the frame is not duplicated and circulated in active QuadBox rings.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 4 Experimental Results
### Test Results
#### 4.4.2 Top 1 Accuracy Among Different Models  
In addition, we compared the testing accuracies of the three models on top 1 accuracy, as well as the average accuracy across all pieces for top 1 predictions. Figure 7 illustrates the testing accuracy for individual pieces and the average testing accuracy across all pieces. From the figure, it is evident that our proposed model ELP-Net achieves the highest prediction accuracy for all pieces and also outperforms others in terms of overall average accuracy. When comparing the two baseline models, CNN-GRU and CNN-MSA-GRU, the addition of the GNN module in ELP-Net leads to significantly improved performance. This highlights the critical role of the GNN in processing situational map data by effectively capturing spatial relationships. Similarly, the transformer module, particularly with the addition of the MSA mechanism, demonstrates its decisive role in handling time-series data, outperforming the CNN-GRU model. In summary, the combination of the transformer module and the GNN module results in superior performance, making ELP-Net the most effective among the evaluated methods.
## 5 Post-Evaluation Analysis
### Query-by-Example Spoken Term Detection
#### 5.2.2 System Analysis for In-Vocabulary and Out-of-Vocabulary Queries  
A similar analysis for the QBe STD systems was carried out for INV and OOV queries and results are presented in Tables 26-28 for MAVIR, RTVE and SPARL20 data, respectively. They show that INV query detection is _easier_ than OOV query detection, since better system performance is obtained for INV queries, regardless of threshold calibration issues. This  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **INL** & **OOL** \\ \hline
**System ID** & **MTWV** & **ATWV** & **MTWV** & **ATWV** \\ \hline E2E + LA & 0.0135 & \(-\)0.1105 & 0.0357 & \(-\)0.0429 \\ \hline E2E & 0.0000 & \(-\)402.2910 & 0.0000 & \(-\)270.0931 \\ \hline E2E + ZNORM & 0.0000 & \(-\)37.0409 & 0.0000 & \(-\)60.5879 \\ \hline \hline \end{tabular}
\end{table}
Table 23: System results of the QBe STD task for MAVIR test data for in-language (INL) and out-of-language (OOL) queries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **INL** & **OOL** \\ \hline
**System ID** & **MTWV** & **ATWV** & **MTWV** & **ATWV** \\ \hline E2E + LA & 0.0123 & 0.0123 & 0.0000 & 0.0000 \\ \hline E2E & 0.0237 & \(-\)33.7965 & 0.0769 & \(-\)36.9764 \\ \hline E2E + ZNORM & 0.0000 & \(-\)107.7061 & 0.0000 & \(-\)76.7394 \\ \hline \hline \end{tabular}
\end{table}
Table 25: System results of the QBe STD task for SPARL20 test data for in-language (INL) and out-of-language (OOL) queries.  
could be due to the fact that OOV queries exhibit more diverse properties in terms of occurrence rate and target language among others, which makes OOV query detection more complex inherently.
# Approximate Sensory Data Collection: A Survey
## 3 Compressive Sensing Based Approximate Data Collection Algorithms  
Compressed sensing methods have recently become one of the most popular techniques for approximate data collection. Generally, the algorithms belonging to this category regard the sensory data sampled by all the sensors in a given time interval as a matrix with \(n\times m\) size, where \(n\) is the size of the network, \(m\) is the number of sampling times of each sensor in the given time interval, and the algorithms assume all sensors are synchronous in the network and the sensory data matrix is sparse in certain subspaces.  
Based on the above assumption, the compressive sensing based approximate data collection algorithms mainly have the following steps.  
Firstly, a series of random vectors are generated to form the bases of a subspace.  
Secondly, the sensory data is compressed according to the above subspace by projecting the original data matrix into the above subspace.  
The main advantages of the algorithms in this category are that the compression rate, i.e., ratio of the dimension of the compressed data matrix to \(n\), can be controlled easily according to users' requirements, and the compression process can be implemented in a distributed manner after the bases are determined. On the other hand, the main challenge of the algorithms is how to generate the appropriate bases. According to the methodology for generating the random bases, the existing works can be classified into the following three groups.
## 3 Results and Discussion
### Hindcast Experiments to Improve the Forecasting Chain
This led to consider the hydrological forecast as highly reliable on a subregional warning scale rather than evaluating the results on individual basins; this technique is called the multi-catchment probabilistic approach [45], and the prediction is based on the elaboration of the flood forecast made on a number of small catchments that belong to the same predefined region, in order to issue a unique forecast that is valid for the entire region. In Italy, warning areas are defined by regional laws. From this perspective, even if the streamflow forecast is not quantitatively correct for all the basins, the magnitude of the event is very well captured in all the chain configurations and supported meteo/hydrological offices for warning purpose.
# PTA-Sync: Packet-Train-Aided Time Synchronization for Underwater Acoustic Applications
###### Abstract
Unlike the conventional OVTT-based synchronization method, the TS accuracy is increased by estimating the propagation delay change by applying three-dimensional vector values instead of scalar quantities to the node's moving speed. As a result of performing simulations according to various network condition changes, such as the synchronization elapsed time, node's average velocity, number of synchronization message exchanges, synchronization message transmission time interval, the degree of mobility randomness, and network range, it is observed that the time error synchronization performance of PTA-Synch is improved by 87.83% and 62.43% compared to that of TSMA-IGTS and E2 DTS, respectively. The following parts of the paper are organized as follows. Section 2 elaborates on the target network and the proposed PTA-Sync protocol. Section 3 comparatively analyzes the synchronization performance of the PTA-Sync protocol against TSMA-IGTS andE2 DTS. Finally, Section 4 presents the concluding points of the study.
### Experiments with VAEs  
To verify the performance of the proposed VAE trained with the log-likelihood-based reconstruction error function, we conducted a series of speaker recognition experiments on the TIDIGITS dataset. For performance comparison, we also applied various feature extraction approaches. The approaches compared with each other in these experiments were as follows:  
* _I-vector_: standard 200-dimensional i-vector;
* _Autoencode_: VAE trained to minimize the cross-entropy between the input Baum-Welch statistics and the reconstructed output Baum-Welch statistics;
* _Classify_: VAE trained to minimize the cross-entropy between the softmax output and the one-hot speaker label;  
Figure 3: Average differential entropy computed using the latent variable variance extracted from the proposed VAE on different durations.  
* _Proposed_: the proposed VAE trained to minimize the negative log-likelihood-based reconstruction error.  
_Autoencode_ is a standard VAE for reconstructing the input at the output, and was trained to minimize \(E_{VAE}\) (11) given the Baum-Welch statistics as input. On the other hand, _Classify_ is a VAE for estimating the speaker label, which was trained to minimize the following loss function:  
\[E_{Class}(\mathbf{x})= D_{KL}(q_{\phi}(\mathbf{z}|\mathbf{x})\|p_{\theta}( \mathbf{z}))-\mathbb{E}_{\mathbf{Y}}[\log\mathbf{\hat{Y}}], \tag{20}\]  
where \(\mathbf{Y}\) denotes the one-hot speaker label of utterance \(\mathbf{X}\) and \(\mathbf{\hat{Y}}\) is the softmax output of the decoder network. The network structure for _Classify_ is depicted in Figure 4. In this experiment, only the mean vectors of the latent variables were used for _Autoencode_, _Classify_, and _Proposed_.  
The results shown in Table 1 tell us that the VAEs trained with the conventional criteria (i.e., _Autoencode_ and _Classify_) performed poorly compared to the standard i-vector. On the other hand, the proposed VAE with likelihood-based reconstruction error was shown to provide better performance for speaker recognition than the other methods. The feature extracted using the VAE trained with the proposed criterion provided comparable verification performance (i.e., EER, DCF08, DCF10) to the conventional i-vector feature. Moreover, in terms of classification, the proposed framework outperformed the i-vector framework with a relative improvement of 5.8% in classification error. Figure 5 shows the detection error tradeoff (DET) curves obtained from the four tested approaches.  
Figure 4: Network structure of the baseline VAE _Classify_.
## 2 Spatial Audio Quality Assessment
We evaluate the common elements of these lists to identify four discriminatory attributes to be assessed within a listening test (see Table 1). Given that a small number of participants was used (those measured for the SADIE II database), the contribution from each subject was significant and an exhaustive list of attributes would have been an onerous task for each subject, potentially creating a detrimental effect on the results due to listening fatigue. To avoid this, a smaller selection of attributes was used and participants were encouraged to think more carefully about the ratings given to each stimuli. Four attributes were selected to be compatible with the interface used by the participants to rate the stimuli, discussed in Section 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Anchors** & **Definition** \\ \hline
**Brightness** & Dark \(\rightarrow\) Bright & The abundance of high (/low) frequencies. \\ \hline
**Richness** & Thin \(\rightarrow\) Rich & A full and well balanced mix. Inclusive of all frequencies \\  & & and with no obvious boosts or cuts. \\ \hline
**Externalisation** & In-Head \(\rightarrow\) External & The locatedness of sources to distant points in space. \\ \hline
**Preference** & Unfavoured \(\rightarrow\) Preferred & An overall plausibility of the sound field. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spatial audio attribute scales and definitions. [PERSON] [32], [PERSON] [33], [PERSON] [34], [PERSON] [35] and [PERSON] [36] all identify _brightness_ (/darkness) as the abundance of high (/low) frequencies. We use the same definition. A term to describe a sense of fullness is also included by each author. It is described as immersion by [PERSON] and presence by [PERSON]. In this paper, we use the term _richness_ (as in [32]). We describe it as the sense of a full and well balanced mix inclusive of all frequencies and with no obvious boosts or cuts. Regarding spatial attributes, _externalisation_ is identified by [PERSON], [PERSON], [PERSON] and [PERSON] [31]. We use this term and describe it specifically as the locatedness of sources to distant points in space. An overall feeling of realism or naturalness, is also identified by the same authors. We summarize these sensations with the term _preference_, implying an overall plausibility of the sound field.
# Research on Progress of Blockchain Consensus Algorithm:
## 4 Future Prospects of Consensus Algorithms
### Improvements Based on Traditional Consensus Algorithms
#### 4.1.1 Continuous Improvement of PoW Algorithm  
The PoW algorithm has problems such as low data throughput, high consumption of computing power, and too long time for consensus. So, the PoW algorithm can be improved from many aspects to make the PoW algorithm more perfect. Regarding the problem of low data throughput of the blockchain, expansion plans such as increasing the block size and reducing the block generation interval can be used to improve it. The Bitcoin-NG [16] protocol provides a new idea, namely introducing the concept of key blocks and micro-blocks. Key blocks are only used to elect leaders and do not store transaction information; micro-blocks are used to store transaction information. The generation of blocks does not need to consume computing power. After the node generates the key block, it is set as the leader, and the leader generates multiple micro blocks with less consumption and faster efficiency. By generating a large number of micro-blocks for storing data, the storage capacity of the blockchain can be greatly increased, and the transaction response time can be reduced. Aiming at the problem of too high computing power consumption, the Ethash algorithm proposed a solution, that is, to introduce I/O blocking and a directed acyclic graph to improve the solution of the _target_ value in PoW. It uses a directed acyclic graph to create a large dataset and a small one, and the small dataset is used to verify block information, the large dataset is generated by calculation of the small dataset, and miners can only save the large dataset in order to mine faster. Through I/O blocking, the overhead of system resources is reduced. The Ethash algorithm makes mining evaluation more suitable for general-purpose computers with large memory capacity without other special requirements. Through dynamic adjustment, the production speed of the data block is improved, and the transaction time is shortened.