esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### What Are the Main Technological Uses of Audio Features in Educational Research? (RQ1)
#### 3.1.3 Classroom Dynamics  
Classroom dynamics captures the evolving nature of classroom interactions by focusing on how teacher strategies, student responses, and the structure of classroom activities unfold over time. Unlike studies that examine static emotional states or isolated events, these works investigate how multiple signals (e.g., verbal, acoustic, or behavioral) interact across temporal sequences to shape the overall learning environment.  
A significant focus within this category is the assessment of classroom climate. For example, [PERSON] et al. [32] combines audio and video features to model the overall atmosphere of a class session. This line of work continues in [33; 34], where the authors use the CLASS (classroom assessment scoring system) framework to automatically classify sessions as exhibiting either positive or negative climate characteristics.  
Other studies emphasize the temporal structure of lessons and how teaching unfolds in real time. [PERSON] et al. [35], for instance, uses student feedback to evaluate the perceived quality of lectures, implicitly assessing the classroom dynamic from the learner's perspective. In more technical approaches, [PERSON] et al. [36] explores the detection of classroom events under noisy conditions, while [PERSON] and [PERSON] [37] applies audio diarization techniques to classify different teaching methodologies based on the structure of interactions observed.  
These studies reveal that classroom dynamics are not reducible to isolated actions or emotions but rather emerge from the ongoing interplay of multiple pedagogical and communicative signals. By capturing this temporal complexity, the works in this category provide a crucial lens for understanding teaching effectiveness and learning climate, factors that can guide the design of more adaptive and responsive educational practices.
# AVSS: Airborne Video Surveillance System
## 5 AVSS Features
### Video Management (UAV)  
The video management is essential because of the limited resource: storage, computing power, and the wireless network bandwidth. When there is enough space, there is no downside of storing a video. However, with a limited resource, we remove less important videos when more space is needed. First, 20% of the storage is cleared whenever the storage reaches over 90% of its maximum capacity, from the oldest. If the video's event score is more than three times higher than the average score of removal candidates, the video is marked as important and untouched. The marked videos are omitted from removal candidates. If it is impossible to remove 20% of the remaining videos when the maximum storage is used, the UAV comes back to base. However, it is extremely unlikely, because average SD cards can store tens of hours of videos.  
Because the computing power is also limited, inter-frame encoding schemes are not feasible. Yet, surveillance capability of the UAV requires real-time streaming of the video to users. Therefore, we have employed frame level compression schemes, a web-based implementation with motion JPEG (MJPEG). Each frame is compressed using the event detection scores. As the video capture can be only accessed with one process at a time with MJPEG server, sub-streams of the capture are created. The sub-streams can be sent to the users accessing the stream or saved in the storage. Optical flow-based event detection is implemented here. Each stored video's duration is 60 s.
## 4 Design Consideration and Simulation
### Inductor Value Calculation  
From Table 4, substitution of \(V_{o(min)}\) = 13 V, \(V_{in(max)}\) = 16 V,\(f_{s}\) = 400 kHz, \(\beta\) = 20%, and \(I_{o}\) = 1.2 A into (2) can obtain \(L_{bl}\) = 9.1 \(\upmu\)H. In addition, substitution of \(V_{o(max)}\) = 16 V, \(V_{in(min)}\) = 9 V,\(f_{s}\) = 400 kHz, \(I_{o}\) = 1.2 A, and \(\beta\) = 20% into (4) can obtain \(L_{bt}\) = 20.5 \(\upmu\)H.
## 3 Detailed Problem Formulation
### Playback Delay  
The playback delay is the time the CN takes to resume playing the video after a VCR operation, as illustrated in Figure 2. A smaller playback delay indicates a better viewing experience.  
When the anchor duration is fixed, if the anchor interval increases, the anchors that are perfected with identical residual download bandwidths will have a wider distribution in the video. When the user randomly jumps to a certain point using a VCR operation, the probability that the anchor near the point that has been downloaded increases, and the probability that the user experiences a delay decreases. Therefore, the average playback delay decreases.  
We simulated the VCR behavior of a VoD system. The fast-forward and -backward operations compose a notably small percentage (approximately 1%) of the user's actual operations. As mentioned in [15; 16], most of the user interactions are forward search (48%) and pause (51%). Most VCR operations can be achieved through the jump process: jump to a new point from the current play point and resume playing the video. One search or pause must only jump once, whereas fast forwards or backwards usually consist of a series of jump processes [17]. Therefore, to study the playback delay of the VCR operation, this paper mainly pays attention to the forward-search jump behavior when the users are watching long videos.  
Figure 2: Playback delay.  
The length of each jump in the VCR operation complies with Weibull distribution [8], and the probability density function is as follows:  
\[f(x,\alpha,\beta)=\frac{\alpha}{\beta}(\frac{x}{\beta})^{\alpha-1}e^{-(\frac{x}{ \beta})^{\alpha}} \tag{1}\]  
Table 1 shows the parameters of the distribution.  
After the video was played for 1 min, the user randomly performed 20 VCR operations; then, the average playback delay was recorded. As shown in Figure 3, with the increase in anchor interval, the playback delay gradually decreases. When the users watch a video on demand, the delay should be as short as possible. From this perspective, a larger anchor interval corresponds to a better user experience.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Pre-Registered Image Fusion Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & **EN** & **SD** & **MI** & **VIF** & \(Q^{abf}\) \\ \cline{3-6}  & & **+** & **+** & **+** & **+** & **+** & **+** \\ \hline \multirow{6}{*}{RoadScene} & ReCoNet & 7.03 \(\pm\) 0.25 & 41.89 \(\pm\) 7.70 & 2.19 \(\pm\) 0.31 & 0.59 \(\pm\) 0.10 & 0.42 \(\pm\) 0.04 \\  & CDDFuse & **7.48 \(\pm\) 0.19** & 56.66 \(\pm\) 8.24 & 2.35 \(\pm\) 0.35 & 0.73 \(\pm\) 0.13 & 0.52 \(\pm\) 0.07 \\ \cline{1-1} \cline{2-6}  & SuperFusion & 6.96 \(\pm\) 0.24 & 41.85 \(\pm\) 7.85 & 2.66 \(\pm\) 0.32 & 0.66 \(\pm\) 0.08 & 0.49 \(\pm\) 0.04 \\ \cline{1-1} \cline{2-6}  & SeAFusion & 7.38 \(\pm\) 0.20 & 52.64 \(\pm\) 9.43 & 2.32 \(\pm\) 0.36 & 0.72 \(\pm\) 0.14 & 0.53 \(\pm\) 0.05 \\ \cline{1-1} \cline{2-6}  & PSFusion & 7.38 \(\pm\) 0.13 & 48.25 \(\pm\) 5.17 & 1.84 \(\pm\) 0.39 & 0.66 \(\pm\) 0.12 & 0.54 \(\pm\) 0.04 \\ \cline{1-1} \cline{2-6}  & Ours & 7.42 \(\pm\) 0.20 & **59.84 \(\pm\) 11.87** & **3.41 \(\pm\) 0.47** & **0.89 \(\pm\) 0.14** & **0.55 \(\pm\) 0.06** \\ \hline \end{tabular}
\end{table} 
Table 1: Quantitative results of pre-registered image fusion experiments between the proposed algorithm and five mainstream alternative algorithms on RoadScene and TNO datasets. Here, a “+” indicates that larger metric values are better. **Bold** and underlined values represent the best and second-best results, respectively. For each metric, the value preceding “\(\pm\)” denotes the average value of the algorithm on the corresponding dataset, whereas the value following “\(\pm\)” indicates the average error. Figure 7: Qualitative comparison results of pre-registered image fusion experiments between the proposed algorithm and five mainstream alternative algorithms on RoadScene and TNO datasets.
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
## 2 Network Steganography
### Covert Channels  
\"Any communication channel that a process can exploit to transfer information in a manner that violates the system's security policy\" [22] is how the US Department of Defense defines the term \"covert channel.\" In other words, a secret channel is a method of information hiding whereby a user transfers confidential data between two or more entities using a regular communication channel, all while keeping the information hidden from outside parties. The general idea of the covert channels in information and communication technology (ICT) networks is illustrated in Figure 3.  
A covert channel in ICT networks can be established by utilizing network steganography between network devices. While both end devices are aware of the secret message, the other devices within the network remain unaware of its existence. There are two fundamental types of covert channels [23]:  
* Storage Covert Channel--it uses the fields of the network packet as the message carrier to transmit the steganography message. Headers are crucial because they include the data needed to start and continue a communication. While some fields are necessary for proper flow, others are optional and can be modified to hold covert data.
* Timing Covert Channel--it implements communication by modifying the behavior of the transmission system by intentionally changing the time dependencies between transmitted packets. One drawback of approaches employing this functionality is the requirement for a synchronization mechanism [24].  
Figure 3: Covert channels in ICT networks.  
Other than the mentioned hidden channels, hybrid solutions combine the above two cases.  
The transmission of information using the storage covert channel mechanism may be performed using one or more protocols. When using one protocol, the data is hidden in the packet structure. On the other hand, when using different protocols, the data need not be encoded in the packet, but the packet itself can be interpreted as a message. The use of multiple channels is more effective when sending short messages, as the channels created in this way are not efficient in terms of bandwidth compared to the storage channel, which uses fewer network protocols.  
In our novel approach, a storage covert channel has been used.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 5 SRv6-Based Lossless Migration
### Migration Procedure
#### 5.1.1 Enhanced Mode  
The Enhanced Mode, characterized by comprehensive SRv6 encapsulation, requires the configuration of routing rules only on gNB2, SR Endpoint4, and SR Endpoint2. gNB2 is in charge of encapsulating the packets in an IPv6-SRv6 header. During the migration interval, gNB2 must include in the segment list the SID identifying the End.Buffer behavior for SR Endpoint2.  
Figure 8 shows the control plane procedure for path migration. The MEO configures gNB2 to apply the H.Encaps.Red. gNB2 includes within the SRH, the SR Endpoint4's End SID and sets the SR Endpoint2's End.Buffer SID as the destination address of the IPv6 outer header. Firstly, the MEO asks the SMF to activate the SDN controller for SR Endpoint4 configuration. The latter requires a rule to forward the packets to the designed container once decapsulated. Subsequently, the MEO prompts the AMF to configure the H.Encaps.Red encapsulation rule on gNB2, as just described. At the same time, the MEO asks the SMF to trigger the SDN controller for configuring the H.Encaps.Red rule on SR Endpoint2 to redirect the on-the-fly packets. The latter rule must include the SR Endpoint2's End.Buffer SID.  
Once the container is restored on MEC Host2, a new network configuration is required. The buffer is not required anymore, and the packets can be forwarded directly to the service instance. The MEO, in this phase, can trigger all the following actions concurrently. The H.Encaps.Red rule must be updated on gNB2, to encapsulate the packet. At the same time, the MEO prompts the SMF and the SDN controller to update the backup rule on SR Endpoint3. Concurrently, the H.Encaps.Red rule on gNB1 can be removed. Finally, the MEO triggers the buffer flush.  
Figure 7: The proposed migration timeline.  
Figure 9 delineates a data plane example during service migration, which has been triggered by the MEO after the client handover. It illustrates the data plane downtime interval. Figure 9 shows the UE sending a packet to its service instance, whose transfer is in progress. When it reaches gNB2, the latter applies the H.Encaps.Red behavior and forwards the packet. gNB2 inserts the SRH; Figure 9 shows, in the SRH, the list of the segments (B:0:0:4:E: :) and the index of the active segment (Segment Left field). The packet reaches SR Endpoint2, which implements the End.Buffer behavior. After that, the packet with the updated outer header is forwarded to SR Endpoint3. SR Endpoint3 applies the End behavior with the USD, removing the outer IPv6-SRv6 header. Then, it forwards the packet to the destination container.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 4 Toward an MPC-Based Solution for the CFDP
### Deployment Considerations  
A key question to be answered when developing an MPC-based platform is who would host the computing nodes in a practical deployment.  
One option could consist of each participating hualier hosting one node, making sure that each hualier can fully trust at least the node under their control. However, this is arguably unrealistic from multiple points of view. Firstly, from an efficiency point of view, the complexity of secret sharing-based MPC protocols generally scales quadratically in the number of nodes, so that a high number of nodes is unfavorable; while significant, this drawback might be overcome, e.g., by randomly selecting a subset of hualiers to host the computing nodes for each execution of the protocol.  
However, secondly, this option would require each hualier to possess the necessary knowledge and IT infrastructure to host such a node, which might be especially unrealistic for micro to small companies employing up to 5 or 25 drivers, respectively [38]. Considering the discussion in Section 3.3, such companies might be the ones with the highest relative benefit from a collaborative platform, since they might not have sufficiently many orders for extensive in-house optimization.  
We thus consider a more realistic scenario where a small number of MPC nodes (e.g., 3) is hosted by independent service providers. These parties should not be active members of the transport ecosystem, to avoid their own interests and conflicts of interest. Possible candidates might, e.g., be notaries, who already enjoy a high level of trust. This approach is also aligned with other related initiatives such as the _PhysICAL_ ([[https://physical-project.at/](https://physical-project.at/)]([https://physical-project.at/](https://physical-project.at/)) (accessed on 30 June 2025)) project, which is developing a trusted broker for optimizing multi-modal transport challenges (although the _PhysICAL_ approach relies on a single fully trusted entity collecting, processing, and storing sensitive business information from logistics companies, which might pose a significant risk for large-scale adoption [4]).  
Overall, we thus believe that this approach constitutes a good balance between trust challenges and efficiency, and at the same time allows parties to reduce costs and increase usability compared to a fully decentralized deployment due to economies of scale.  
Finally, given the established trust in notaries--especially in contrast to a setting where nodes are controlled by competing hualiers--we consider instantiations with passive security to be a suitable choice. In this model, computing nodes may attempt to infer information from the data they process but otherwise adhere to the protocol specifications. However, as we discuss below, this is not a fundamental constraint of our heuristic but rather of the choice of the implementation framework, and could easily be adjusted by adopting alternative implementation frameworks at the cost of higher computational complexity.
## 3 Ground Loss Effect Factors
### Displacement Solution
\begin{array}{l}S_{x0}=-\frac{v_{\text{loss}}}{2\pi}\frac{x}{x^{2}+H ^{2}}\bigg{[}1+\frac{y}{\left(x^{2}+y^{2}+H^{2}\right)^{\frac{1}{2}}}\bigg{]} \\ S_{y0}=\frac{v_{\text{loss}}}{2\pi}\frac{1}{\left(x^{2}+y^{2}+H^{2}\right)^{ \frac{1}{2}}}\\ S_{z0}=\frac{v_{\text{loss}}}{2\pi}\frac{H}{x^{2}+H^{2}}\bigg{[}1+\frac{y}{ \left(x^{2}+y^{2}+H^{2}\right)^{\frac{1}{2}}}\bigg{]}\\ S_{x0}(y\rightarrow\infty)=-\frac{v_{\text{loss}}}{\pi}\frac{x}{x^{2}+H^{2}}\\ S_{z0}(y\rightarrow\infty)=\frac{v_{\text{loss}}}{\pi}\frac{x}{x^{2}+H^{2}} \end{array} \right. \tag{4}\]  
where \(V_{\text{loss}}\) is ground loss. From Equation (4) it can be found:  
\[\left\{ \begin{array}{l}S_{x0}(y)=S_{x0}(y\rightarrow\infty)\times\frac{1}{ 2}\times\left[1+\frac{y}{\left(x^{2}+y^{2}+H^{2}\right)^{\frac{1}{2}}}\right] \\ S_{z0}(y)=S_{z0}(y\rightarrow\infty)\times\frac{1}{2}\times\left[1+\frac{y}{ \left(x^{2}+y^{2}+H^{2}\right)^{\frac{1}{2}}}\right]\end{array} \right. \tag{5}\]  
Equation (5) shows that the distribution of stratum displacement caused by shield construction (ground loss) along the Y axis satisfies the law of \(\frac{1}{2}\times\left[1+y/(x^{2}+y^{2}+z^{2})^{\frac{1}{2}}\right]\). Therefore, based on this law, the [PERSON]'s solution is extended to the three-dimensional. It is noted that the coordinate system adopted in this paper is different from that used in the [PERSON]'s solution, so the distribution law should be revised, and its expression is as Equation (6):  
\[\varepsilon_{y}=\frac{1}{2}\times\left[1-\frac{y}{\left(x^{2}+y^{2}+H^{2} \right)^{\frac{1}{2}}}\right] \tag{6}\]  
The plane ground loss parameter \(\varepsilon_{x,z}\) is extended to three-dimensional parameter \(\varepsilon_{x,z,y}\) which can be seen as Equation (7). Then, three-dimensional stratum displacement solution, Equation (8) can be obtained by replacing \(\varepsilon_{x,z}\) in Equation (2). \[\varepsilon_{x,y,x}=\varepsilon_{y}\times\varepsilon_{x,z}=\left[1-\frac{y}{ \left(x^{2}+y^{2}+H^{2}\right)^{\frac{1}{2}}}\right]\times\frac{4 gR+g^{2}}{8R^ {2}}\exp\left\{-\left|\frac{1.38x^{2}}{\left(H+R\right)^{2}}+\frac{0.69z^{2}} {H^{2}}\right|\right\} \tag{7}\]  
\[\left\{ \begin{array}{l}S_{z}=R^{2}\bigg{\{}-\frac{z-H}{x^{2}+(z-H)^{2}}+ \left(3-4\mu\right)\frac{z+H}{x^{2}+(z+H)^{2}}-\frac{2z\left[x^{2}-(z+H)^{2} \right]}{\left[x^{2}+(z+H)^{2}\right]^{2}}\bigg{\}}\times\varepsilon_{x,y,z}\\ S_{x}=-R^{2}x\bigg{\{}\frac{1}{x^{2}+(z-H)^{2}}+\frac{\left(3-4\mu\right)}{x^{2 }+(z+H)^{2}}-\frac{4z(z+H)}{\left[x^{2}+(z+H)^{2}\right]^{2}}\bigg{\}}\times \varepsilon_{x,y,z}\end{array} \right. \tag{8}\]
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
###### Abstract  
Palladium supported on nanocrystalline ZSM-5 (n-ZSM-5, Si/Al = 32) and hierarchical ZSM-5 (h-ZSM-5) with different acidity (Si/Al = 33, 51, 122) were tested in the liquid-phase conversion of stearic acid under nitrogen atmosphere (6 bar). The incorporation of Pd into ZSM-5 zeolite increased significantly the share of gasoline in the reaction products due to the promotion by this metal of both decarboxylation and hydrogen transfer reactions. Likewise, the Pd nanoparticles dispersed over the zeolite support favored the conversion of light olefins formed by end-chain cracking reactions into gasoline-range hydrocarbons according to an oligomerization/cyclization/aromatization pathway. Additionally, Pd/h-ZSM-5 gave rise to higher conversion and selectivity towards gasoline than Pd/n-ZSM-5, due mainly to the enhanced accessibility and improved Pd dispersion achieved when using the hierarchical zeolite. The decrease in the Si/Al atomic ratio in Pd/h-ZSM-5 samples resulted in a rise in the stearic acid conversion, although it was lower than expected. This finding denotes that, for supports with high acid sites concentration, the Pd availability became the limiting factor as the metal was loaded in similar amounts in all catalysts. Finally, the increase of the reaction temperature with the Pd/n-ZSM-5 (122) catalyst augmented both stearic acid conversion and gasoline selectivity, since it enhanced the conversion of the light olefins, formed as primary cracking products, into liquid hydrocarbons. Therefore, it can be concluded that Pd supported on hierarchical ZSM-5 zeolite is a convenient catalyst for obtaining bio-gasoline from oleaginous feedstock.  
bio-gasoline; stearic acid; Pd; ZSM-5; hierarchical zeolite
## 3 Research Methodology
### Edge Continuity Distortion-Aware Block (ECDAB)
To further alleviate the influence of distortion, features extracted by EDAB are sent to the distortion-aware block (DAB) for further processing. [PERSON] et al. [37] pointed out that the distortion of ERP images is related to the latitude of the 360\({}^{\circ}\) omnidirectional images, while the distortion of the 360\({}^{\circ}\) omnidirectional images after ERP is related to the height. So, in the DAB, the ERP image is divided into N blocks along the row direction, and then these blocks are stacked on the channel dimension (the shape of features changes from B C H W to B C\(\times\)N H/N W). Here, B, C, H, and W represent batch-size, channels, height and width for input images or features, respectively, and the symbol // indicates an integer division symbol. Then the segmented images are grouped for the convolution operation. The purpose of the above operation is to extract features with different convolution kernels for different blocks. Note: the number of blocks needs to be set when implementing a DAB; simply speaking, the segmentation here is equivalent to a reshape operation in the deep learning framework. Finally, the results obtained by grouping convolution are readjusted to the original shape (B C\(\times\)N H//N W to B C H W) and then a convolution operation is carried out to compensate for the influence of segmentation. DAB's detailed structure is depicted in Figure 4. ECAB and DAB are connected in series to form the edge continuity distortion-aware block (ECDAB).