esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Description and Basic Definition  
It is believed that in a complex traffic environment, the motion states of surrounding vehicles have a significant impact on the future motion trends of the target vehicle. The vehicles in the current and adjacent lanes that are closest to the target vehicle in front and behind are defined as the surrounding vehicles. As shown in the left of Figure 1, the blue car is the target vehicle \(V_{o}\) whose motion needs to be predicted. Yellow cars are the surrounding vehicles, expressed as \(V_{i},i\in\mathcal{N}(V_{o})=[F,R,LF,LR,RF,RR]\), where \(\mathcal{N}(V_{o})\) represents the set of the surrounding vehicles. The encoder proposed in this paper consists of two components, the vehicle-vehicle interaction module and trajectory encoding module. For the vehicle-vehicle interaction module, the motion states of vehicles are primarily focused. We define the motion states of the object vehicle and surrounding vehicles at time \(t\) as \(s_{o}^{t}=\left[u_{o}^{t},v_{o}^{t},a_{o}^{t}\right]\) and \(s_{i}^{t}=\left[u_{i}^{t},v_{i}^{t},a_{i}^{t}\right]\), where \(u_{o}^{t},v_{o}^{t},a_{o}^{t}\) are the longitudinal velocity, and longitudinal acceleration of the target vehicle and \(u_{i}^{t},v_{i}^{t},a_{i}^{t}\) are the longitudinal velocity, lateral velocity, and longitudinal acceleration of the \(i\)th surrounding vehicle, respectively. Similarly, for the trajectory encoding module, we define the trajectories of the target vehicle and surrounding vehicles at time \(t\) as \(\tau_{o}^{t}=\left[x_{o}^{t},y_{o}^{t}\right]\) and \(\tau_{i}^{t}=\left[x_{i}^{t},y_{i}^{t}\right]\), where \(x_{o}^{t},y_{o}^{t},x_{i}^{t},y_{i}^{t}\) represent the longitudinal and lateral positions of the target vehicle and \(i\)th surrounding vehicle. Based on these, the historical observation can be formulated as  
\[\begin{split} S_{o}^{t}&=\left[s_{o}^{t-t_{obs}},s_ {o}^{t-t_{obs}+1},\cdots,s_{o}^{t-1},s_{o}^{t}\right]\\ S_{i}^{t}&=\left[s_{i}^{t-t_{obs}},s_{i}^{t-t_{obs} +1},\cdots,s_{i}^{t-1},s_{i}^{t}\right]\\ \Gamma_{o}^{t}&=\left[\tau_{o}^{t-t_{obs}},\tau_{o} ^{t-t_{obs}+1},\cdots,\tau_{o}^{t-1},\tau_{o}^{t}\right]\\ \Gamma_{
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 2 Uplink and Downlink Channels in Satellite-Ground FSO Communications
### Channel Parameters  
To find the optimal FSO system parameters needed for near-Earth uplink/downlink transmissions between a satellite and a ground station, we vary several parameters highly related to the FSO channel environment. For this, important FSO design parameters such as wavelengths, satellite altitude, ground station altitude, and ground station aperture diameter are varied to observe how the FSO channel behaves. In addition, we illustrate how the varied channel parameter impacts other related parameters in the FSO link. Based on the obtained result, we present the recommended channel parameter values needed for a near-Earth FSO system design. The values used in the simulation for ground station altitude, satellite altitude, ground station aperture diameter, and the wavelengths are listed in Table 1. All the simulations are carried out in MATLAB R2022a (9.12) on a Windows 10 operating system.
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 4 Results
### Experimental Setup  
In order to evaluate the proposed method, 23 sequences with resolutions between 416\(\times\)240 and 2560\(\times\)1600 were used: _BlowingBubbles, BasketballPass, BQSquare, RaceHoses, BasketballDrill, BasketballDrillText, PartyScene, RaceHoses, BQMall, Johnny, FourPeople, KristenAndSara, SlideEditing, SlideShow, ChinaSpeed, BasketballDrive, BQTernace, Cactus, Kimono, ParkScene, ParkJoy, Traffic, PeopleOnStreet_[31]. Each sequence contains between 150 and 600 frames and has a frame rate between 20 and 60 frames per second.  
For encoding the sequences, the HEVC reference Model (HM) version 16.15 was used. For NS\({}_{\text{src}}\), a low-delay configuration was used in which the first frame is an intra-frame and all other frames are predicted frames that each take only a single preceding frame as reference. As discussed above, TMVP is turned off because inserting a keyframe disrupts  
Figure 3: Companion stream CS\({}_{\text{src}}\) is encoded from the source video and CS\({}_{\text{NS}}\) is constructed from the decoded normal stream.  
this coding tool. For CS\({}_{\text{Src}}\) and CS\({}_{\text{NS}}\), the same configuration is used as for NS\({}_{\text{Src}}\), yet only encoding intra-frames that do not depend on other frames.  
In general, it is most straightforward to use the same QP for both the normal stream and companion stream. However, for completeness and additional flexibility, we also provide results of using different QPs. Therefore, each source sequence is compressed with four Quantization Parameter (QP) values (denoted as QP\({}_{\text{NS}}\) for NS\({}_{\text{Src}}\), and QP\({}_{\text{CS}}\) for CS\({}_{\text{Src}}\) and CS\({}_{\text{NS}}\)): 22, 27, 32, and 37.  
In the experiments, we inserted a keyframe at frame \(f=9\), which is the 10 th frame and started decoding from there on as if a random access was performed or as if frame \(f=8\) had been lost. This is an arbitrary decision and could have been any other frame in the beginning of the sequence. Given the set of sequences we used, the 10 th frame is taken to avoid duplicated frames and black frames present in the first frames of some sequences. These duplicate and black frames distorted the results heavily because their rate would be very small.  
The keyframes from CS\({}_{\text{Src}}\) and CS\({}_{\text{NS}}\) are used to replace an inter-frame of NS\({}_{\text{Src}}\), while all other frames of NS\({}_{\text{Src}}\) are left unchanged. These videos with the keyframe inserted (i.e., KI) are denoted as KI\({}_{\text{Src}}\) and KI\({}_{\text{NS}}\), respectively. The effect of the keyframe insertion on the size and quality are evaluated in the next sections.
## 1 Introduction
### Materials for Metal AM in Aerospace  
The range of materials suitable for AM is truly broad, from ceramics to metals and up to advanced composites. The authors' interest revolves around the utilization of metallic alloys that have already been analyzed, as well as offering suggestions regarding the considerable implications for aeronautical equipment [48]. Stainless steels are often selected for many aerospace applications in which hardness and strength, especially at elevated temperatures, are required. Different classes of stainless steels, including austenitic (SS316L), precipitation hardened (PH), and martensite aged, exhibit good adaptability to the AM processes. Depending on the environmental conditions for which they are conceived, these metals can achieve high wear, corrosion, and oxidation resistances without specific surface treatments [49; 50; 51]. For this reason, they are used across components in heat exchangers, landing gear systems, hydraulic manifolds and actuators, engine and exhaust systems, and structural joints. Titanium-based alloys (Ti-6 Al-4V, Ti-8 Al-1 Mo-1V and Ti-10V-2 Fe-3 Al) are ideal candidates thanks to their stability at high-temperatures and specific strengths for use in critical parts, such as compressor blades, hydraulic valves, and structural brackets [52]. They are used in cryogenic applications for rockets' propellant tanks and are electrochemically compatible if coupled with PMCs (polymer matrix composites), which are extensively used in aerostructures [53]. Nickel-based alloys are, instead, preferred for working at elevated temperatures, such as in the case of gas turbines and engine parts (e.g., Inconel 625 and 718) and for precision applications (Invar) [54]. However, steels present the serious disadvantage of being inappropriate for weight-sensitive applications, while titanium alloys, although with a better resistance-to-weight ratio, are much more expensive and require longer lead times. The aerospace sector demands light-weight properties, with appreciable mechanical characteristics, making aluminum (Al) alloys highly sought after. Many Al alloys are not the best fit on their own due to solidification cracking and porosity formation. This can be enhanced thanks to AM techniques that offer the possibility of optimizing tailored materials for the proper end application. For example, AM-produced parts in Al alloys (7A77, 6061-RAM2, AlSi10 Mg and Scalmalloy(r)) can offer higher strength and successful builds.
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 3 IPFS (Inter-Planetary File System)
### Inter-Planetary Name System (IPNS)  
With IPFS, content is addressed by its hash, a mechanism that guarantees integrity but also limits content search. As the DHT-based search algorithm only takes hashes as input, nodes need a secure alternative for searching data in the network. The solution is IPNS: a system used to create and update mutable links. By using IPNS, each node can use its private and unique key to publish and sign data linked to its peer ID. This information is stored in the DHT and the node can update it as frequently as desired. As a result, any node that knows the peer ID of another node can make a request to the network and get the content of the latest information published by that node. Peer IDs are public and broadcasted to nearby nodes. The example described in Figure 5 shows how this system can be used to easily get data from a traffic camera:  
1. The camera is an IPFS peer with a unique peer ID. The peer ID is a public hash generated from the private key of the camera. The private key is never shared with other peers.
2. Every time that the camera captures an image, it generates its CID. CIDs are directly built using the content hash. The new CID is published and added to the distributed hash table (DHT).
3. At this point, the user would be able to get the new camera image using the DHT; the problem is that the user does not know which is the CID of the image.
4. To solve this problem, the camera regularly updates its IPNS address to point to the CID of the last captured image. This information is also published in the DHT and made available to all the nodes of the network.
5. If the user makes a DHT request to resolve the camera IPNS address, it will always get the CID of the last camera image. The camera IPNS address is /ipns/<PeerID>. This IPNS address never changes and no other peer can update it because it would need to know the associated private key.
6. The result of a DHT IPNS resolution is a CID, not the associated content. If the user wants to download the image, it can query the DHT for the list of peers that have a copy. The list will include the camera IP address and also other peers that already have retrieved the same image before.
# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 4 Experiments and Analysis
### Training Setup  
The Pytorch framework was employed for network setup. FIO-F and JSCIFI-F were both trained on a server with an NVIDIA 3090 GPU by using the Adam optimizer [39]. To achieve different compression ratios, the network was constructed by using different settings, i.e., by changing the MLP hidden layer, as well as the number and the contained convolutional layer settings of the NeRV blocks. The learning rate was set to 0.0005, and we selected an epoch number of 35.  
By referring to a previous method [25; 32; 34], six hyperspectral datasets were trained, validated, and tested for experiments. To validate the traditional deep learning methods, the mixed dataset was cropped into nonoverlapping 128 \(\times\) 128 patches and then cut with an overlap in the channel domain (spectral domain) according to the minimum spectral channel number of 48. The split images were divided into three sets in a ratio of 8:1:1. The largest set was used as the training set, while the two smallest sets were used as the validation and test sets, respectively. For the proposed framework, in order to utilize neural video representation, the data in the test set were further divided into frame-like collections. According to the experimental setup, the frame sets were categorized into single-channel and three-channel frame sets.
## 5 Q-Learning-Based CCM-SPS Resource Allocation Scheme Proposed for JCS Sidelink
### Q-Learning-Based CCM-SPS Scheme for JCS Sidelink
#### 5.2.2 Algorithm Flow and Pseudo-Code  
Based on previous analysis, we proposed the CCM-SPS scheme to reduce the times of consecutive collisions. Specifically, during transmission, the full-duplex (FD) echo detection capability is used to sense the channel state. If a collision is detected, an \(\Delta RC\) is selected based on the current state. This means that when a collision is detected, the scheme decreases the times of consecutive collision and restricts the number of vehicles entering the reselection process simultaneously, as shown in Figure 5.  
The pseudocode of its algorithm is shown below (Algorithm 1):  
Figure 5: CCM-SPS accelerate reselection.  
```
0: Vehicle density, Packets occupy bandwidth
0:\(Q\) table
1: Initialize the parameters such as learning rate \(\alpha\) and discount factor \(\eta\).
2: Initialize the vehicles' states, actions, and \(Q\) table
3:loop
4: Begin the new packet transmission
5: Observe transmitted vehicle \(s_{i}\)
6:if collision occurred, \(COL=1\)then
7: the number of consecutive resource collisions
8:\(N_{col}=N_{col}+1\)
9: Vehicle \(i\) obtain the negative reward: \((\exp(-N_{col})-1)/log(RC+1)\)
10:else
11: the number of consecutive resource collisions \(N_{col}=0\)
12: Vehicle \(i\) obtain the positive reward 1
13:endif
14: Vehicle update the Q(s,a)
15: Vehicle update the probability \(\epsilon\) according to simulation time
16:if exploration then
17: Vehicle randomly select an action \(\alpha\)
18:else
19: Vehicle select the optimal action \(\alpha_{opt}\) with \(Q_{max}\)
20:endif
21:endloop
```  
**Algorithm 1** Pseudo-code of the proposed CCM-SPS
## 3 Proposed Model
### Shuffle Attention Mechanism  
The Attention mechanism can focus on the feature of speech and suppress irrelevant information. We apply a lightweight Shuffle Attention to the final output of the encoder [25]. It is used to focus on detailed features of speech and suppress irrelevant information after down-sampling. The structure is shown in Figure 3.  
This attention mechanism divided the speech feature map into G groups along the channel dimension. Then the speech feature maps of each group are evenly divided into two sub-feature maps along the channel dimension. They serve as input of Channel Attention (C-Att) and Spatial Attention (S-Att), respectively. C-Att can focus on correlation between the various channels of speech sub-feature map, and S-Att can focus on detailed features of speech sub-feature map. In Figure 3, \(\mathbf{x}_{k1}\) and \(\mathbf{x}_{l2}\) are speech sub-feature maps after the k-th group of speech features (\(\mathbf{x}_{k}\)) are split along channels. \(\mathbf{x}_{k1}\) and \(\mathbf{x}_{k2}\) are fed to C-Att and S-Att, respectively, as shown in Equations (2) and (3):  
\[\hat{\mathbf{x}}_{k1}=\sigma(\mathbf{\omega}_{e}f_{GAP}(\mathbf{x}_{k1})+\mathbf{b}_{e})\cdot \mathbf{x}_{k1} \tag{2}\]  
\[\hat{\mathbf{x}}_{k2}=\sigma(\mathbf{\omega}_{s}g(\mathbf{x}_{k2})+\mathbf{b}_{s})\cdot\mathbf{x}_ {k2} \tag{3}\]  
Figure 2: Proposed model architecture, with 5 coding layers, 5 decoding layers, and 1 Shuffle Attention. IN represents the channel of input speech feature map in each layer, \(\text{C}_{\text{i}}\) and \(\text{H}_{\text{i}}\), respectively, represent the channel of speech feature map after down-sampling and up-sampling at the i-th layer.  
where \(\hat{x}_{k1}\) and \(\hat{x}_{k2}\) are sub-feature maps output by C-Att and S-Att, respectively. Concatenating them along channel axis to acquire attention output feature maps of the k-th group of speech features (i.e., \(\hat{x}_{k}\)). \(\mathbf{\omega}_{c}\), \(\mathbf{\omega}_{s}\), \(\mathbf{b}_{c}\) and \(\mathbf{b}_{s}\) are the weights and biases of FC in C-Att and S-Att, respectively. \(f_{GAP}(\cdot)\) is GAP, \(g(\cdot)\) is group norm, and \(\sigma(\cdot)\) is sigmoid. Finally, the output features of attention of all groups are aggregated. In addition, the final output is obtained after the channel shuffle operation. The final output is the same size as the original input. The channel shuffle operation enables information fusion between different speech sub-features.
## 4 Security and Performance Analyses
### Security Analyses  
#### 4.2.1 Mutual Authentication (Threat Model 1)  
In the proposed scheme, Medical-Center Server MCS authenticates Doctor \(Doc\) by checking \(V_{3}\) since only \(Doc\) and \(MCS\) have capability to compute \(Cer_{D}\), and thus \(V_{3}\), where \(Cer_{D}=h(T_{{}_{MCST_{D}}}(x)\text{mod }p\|TS_{2})\), \(PHI=D_{PHI}\oplus Cer_{D}\) and \(V_{3}=h(ID_{D}\|Cer_{D}\|PHI\|R_{D})\). Similarly, \(MCS\) authenticates Patient \(P\) by checking \(V_{1}\) since only \(Pat\) and \(MCS\) have capability to compute \(Cer_{P}\), and thus \(V_{1}\), where \(Cer_{P}=h(T_{r_{MCS}r_{P}}(x)\text{mod }p\|TS_{1})\) and \(V_{1}=h(X_{P}\|P_{P}\|Cer_{P}\|R_{P})\). Patient \(Pat\) authenticates \(MCS\) by checking \(V_{5}\), since only \(MCS\) can compute \(Cer_{P}\), and thus \(R_{P}\), where \(R_{P}=C_{P}\oplus Cer_{P}\) and \(V_{5}=h(NID_{P}^{new}\|ID_{P}\|SK_{MCSP}\|R_{P}\|X_{P}^{new})\). Patient \(Pat\) implicitly authenticates \(Doc\) through \(MCS\) by checking \(V_{1}\) and \(V_{3}\) to make sure that the doctor \(Doc\) is authorized by using \(P_{P}\), where \(P_{P}=h(W_{P}\|ID_{P}\|ID_{P}\|T_{a}(x))\). Doctor \(Doc\) authenticates \(MCS\) by checking \(V_{4}\), since only \(MCS\) can compute \(SK_{DMCS}\) and derive \(R_{D}\) from \(NID_{D}\), where \(SK_{DMCS}=h(T_{r_{MCS}}(x)\text{mod }p)\)\(NID_{D}=E_{SK_{DMCS}}(ID_{D}\|R_{D})\), and \(V_{4}=h(ID_{D}\|R_{D})\). Doctor \(Doc\) implicitly authenticates \(Pat\) through \(MCS\) by checking \(V_{4}\) since \(MCS\) will send \(V_{4}\) after \(P\) passes the authentication of \(MCS\).
## 2 Results and Discussion
### Statistical Evaluation and Discrimination of VOC Profiles  
Calculated Pearson's coefficients were all above 0.9 for samples belonging to the same individual, demonstrating high consistency of obtained profiles. Kruskal-Wallis test indicated that **39** VOCs displayed discriminating features when comparing the integrated peak area of compounds from the three studied groups, being the most responsible for obtaining distinct and characteristic HE, AB, and HA profiles. In Figure 3, a heatmap is presented associated with hierarchical cluster analysis using [PERSON]'s method, which displays the mentioned relevant VOCs, exhibiting the changes in their levels according to analyzed profiles. From the heatmap plot, seven clusters of VOCs were outlined, which were distributed between four clusters formed for the three investigated groups. The abscess group presented two distinct clustering patterns.  
The heatmap presented in Figure 3 showed that there were strong links between the chemical structure of VOCs and clusters' formation. For example, cluster **1** was formed just from hydrocarbons, cluster **3** and **6** were abundant in VSCs, and, in clusters **5** and **7**, esters and methyl-esters, respectively, were predominant.  
In the second part of discrimination assessment, the full dataset profiles of detected VOCs were used as input in Principal Components Analysis (Figure 4). The three groups of samples (HA, HE, and AB) formed well-defined clusters, indicating the existence of characteristic VOC profiles associated with each investigated group. PCA plot demonstrated the discrimination based on volatile patterns, where 65.37% of the variance was described for the first two principal components.  
Figure 2: Correlation networks of VOCs emitted from saliva samples from all three group of patients, where: healthy patients (HE), subjects with submandibular abscesses (AB) and halitosis (HA). VOCs are shown as nodes. Numbers in circles denote the number of volatiles from each group.  
Figure 4: PCA (Principal Component Analysis) plot depicting the differentiation of group of individuals by VOC profiles; Assigned clusters: healthy controls (HE), patients with submandibular abscess (AB) and halitosis (HA).  
Figure 3: Hierarchical cluster analysis and heatmap of VOC profiles based on peak areas from chromatograms. Columns represent three groups of patients (total of 30 individuals): healthy (15), segregated from an abscess (10) and halitosis (5). Rows represent 39 discriminating VOCs (orange, low abundance; red, high abundance).
# Compaction Characteristics and Minimum Void Ratio Prediction Model for Gap-Graded Soil-Rock Mixture
## 4 Conclusions  
A series of vibrating compaction tests were conducted on SRMs with different basic gradations, gap intervals and mass contents of fine-grained group. It was found that gap-graded SRM may reach a smaller void ratio than the SRM with a continuous gradation under some circumstances, for example, the gap-graded SRMs without 5-20 mm particles have a smaller void ratio than the SRMs with basic gradation, in the case of \(D=2.3,2.5\) and \(2.7\). If the particles in a gap interval play the role of filling components, the absence of them will increase the void ratio of the SRM. Among gap intervals for a certain basic gradation, there is a vital grain size group that dominates the compaction of the SRM. The particle breakage of gap-graded SRMs is more prominent than the SRM with continuous gradation on the whole, especially at the gap interval of 5-20 mm among the selected gap intervals.  
A new model is proposed for better predicting the minimum void ratio of gap-graded SRMs. The model requires three parameters, \(c_{c}\), \(e_{f}\) and \(D\), for prediction of gap-graded SRMs with different mass contents of fine-grained particles. Comparison between the predicted minimum void ratios measured void ratios proves the validation of this model. It should be noted that the new model is only valid for the lithology and the shape of the particles studied in this work.  
Conceptualization, T.W. and S.L.; Methodology, Y.F.; Software, J.Y.  
This research was funded by National Natural Science Foundation of China (U1765205; 2017 YFC0404805). The APC was funded by Hohai University.  
This work was supported by the \"National Natural Science Foundation of China\" (Grant No. U1765205) and \"National Key R&D Program of China\" (Grant No. 2017 YFC0404805).  
The authors declare no conflict of interest.