esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Applications of SER in Education  
Recent studies have explored the application of SER in remote educational settings. For instance, [PERSON] and [PERSON] [10] detailed a new approach to recognizing emotions in speech in an effort to address emotion deficiencies in e-learning systems. Their study focused on developing an SER system that uses neural networks to extract prosodic features from emotional utterances. Similarly, [PERSON] et al. [11] proposed an automated model for speech emotion polarization capable of evaluating lecturers on distance education platforms with orbital local binary patterns and multi-level wavelet transforms employed for feature extraction and classification. [PERSON] et al. [12] analyzed the emotional deficiency in current e-learning systems and proposed a model that incorporates SER to enhance teacher-student connections by tracking changes in learners' emotional states through their speech cues, allowing for teaching strategies to be adjusted accordingly. Using a similar approach, [PERSON] et al. [13] discussed various techniques for transferring speech emotion classifiers trained on acted emotional data to naturalistic elicited data by using online learning. This involved adopting a boosting algorithm that incrementally retrained the models on unlabeled real-world data. [PERSON] et al. [4] developed and evaluated a real-time SER system that used microphones to provide feedback in e-learning environments. This system's classification of basic emotional states significantly resembled that of human expert raters. [PERSON] et al. [14] constructed an e-learning model that incorporates SER and neural networks to track learners' emotional states, with the resulting system providing encouragement and advice tailored to the emotions it recognizes. In addition, the method developed by [PERSON] et al. [5] automatically detects presenters' emotions from course materials, resulting in the creation of a lecture transcript database to study speaker personalities. The data were halved every 5 s, yielding 9541 observations. This study also produced a Shoelace Pattern, which is a shoelace-inspired local feature generator for feature extraction. Shoelace Pattern subbands were created using a tunable-Q wavelet transform to ensure enhanced performance. To create the final feature vector, the feature extraction model combined the top four feature vectors. After selecting the 512 most useful attributes using neighborhood component analysis, a support vector machine (SVM) with a 10-fold cross-validation scheme was used to categorize the data. The classifiers were determined to have accuracy rates of 94.97% and 96.41%. Finally, the SER method for educational settings introduced by [PERSON] and [PERSON] [15] features kernel canonical correlation analysis and SVMs with an accuracy of over 90%. Therefore, the literature indicates that SER techniques can significantly enhance remote education by detecting emotional cues in speech.
## 5 Implementation and Evaluation
### Drift Score Testing and Results  
To test TDM, we introduce delays in the intended publishing interval (\(\delta\)) of \(\tau_{1}\) to manipulate the timing of the data transmission. Our objective is to observe the time when TDM detects a 50%, 70%, or 90% drop in the transmitted data's drifting score and determine the relationship between \(\lambda\) and \(\delta\).  
We begin testing with 0 additional delay on \(\delta\), serving as a baseline for the initial data points (to establish normal system behavior). Then, we introduce delays, starting with a 1.2\(\delta\) delayed interval and progressing in increments of 0.2% until reaching a 2\(\delta\) delayed interval.  
Drift scores are calculated at each publishing interval (\(\delta\)). For the testing, we set the publishing interval (\(\delta\)) to 15 s, meaning that normal data transmission occurs every 15 s.  
When the data are sent with a delay of 3 s (delayed interval = 1.2\(\delta\)), the smart broker takes around 60 s to detect a drifting score drop below 0.7, as shown in Figure 6. Doubling the data transmission delay to twice \(\delta\) reduces the detection time to only around 30 s for detecting the same drop. Additionally, the smart broker can capture a 0.5 drop in drifting score in approximately 90 s when the delayed interval is 1.2\(\delta\). Conversely, it takes around 30 s to capture the same 0.5 drop when the delayed interval is doubled (2\(\delta\)). Furthermore, a significant decrease in the detection time was observed for detecting a drop to a score of 0.3 with increasing delayed intervals.  
The smart broker demonstrates quicker detection of score drops below 0.1, 0.3, and 0.5 as data transmission delays increase.  
The results show that the higher the delays in the arrival of data, the quicker the drift score drops. A smart broker's parameters have to be set properly to watch for a drop to a desired level and set the values for \(\lambda\). For example, if the above data indicate that the typical delay in data arrival is 20% on 15s, then the \(\lambda\) should be set to 60 s (4\(\delta\)) to watch out for a 0.7 quality score. To watch out for a 0.3 score, the \(\lambda\) should be 120 s (8\(\delta\)). Note that this also acts as a net for greater delays as well.
# Impact of Retention Agents on Functional Properties of Recycled Paper in Sustainable Manufacturing
## 2 Materials and Methods  
### Fibrous Materials and Retention Agent  
For this research, white wastepaper sourced from a paper mill was selected. This included products made from bleached pulps, specifically scraps of wood-free waterproof paper with minimal print and no adhesive or colored components, classified as grade 3.04 in accordance with the EN643 \"List of European standard types of wastepaper\" [31]. For comparison, two additional grades of white wastepaper were chosen, labeled as 1.3 and 3.2. Previous analyses on these types of wastepaper [32] demonstrated that it was feasible to eliminate at least 80% of the fine mineral fraction, achieving an average pulp recovery rate of 76.5% along with the best strength properties. These grades also exhibit very similar chemical compositions and impurity levels, as established in earlier studies [32].  
The wastepaper delivered from the paper mill was segregated into uniform fractions, manually shredded, and thoroughly mixed to ensure the homogeneity of the sample. The prepared wastepaper was then sealed in polypropylene foil bags and stored in tightly sealed barrels to prevent moisture and contamination. After mechanical shredding, the samples were packed into airtight containers and kept at a stable temperature of around 15 \({}^{\circ}\)C.  
As a retention aid, the study utilized a cationic polyelectrolyte derived from acrylamide and a cationic acrylic acid derivative. This additive was incorporated into the secondary pulp in varying amounts, ranging from 0.1% to 1.0%.
# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 6 Conclusions  
Real-time and accurate transmission of SVSV is of practical significance to ensure the safe navigation of intelligent ships. The ship monitoring unit plays a similar function to the \"black box\" of an aircraft when the ship is sailing, and the transmission and storage of the monitoring video need to occupy a lot of network bandwidth resources and memory space. Therefore, using efficient data compression algorithms is the most reliable solution for transmitting and storing SVSVs and is also the development trend of data transmission between ship and shore for intelligent ships. It is believed that the compression delay is one of the main factors affecting the total transmission delay when transmitting the SVSV between ship and shore of intelligent ships. Among them, the coding complexity is highest when the video is coded intra-frame and so further reduces the latency caused by coding is achieved by using an advanced HEVC coding scheme to compress the SVSV and using convolutional neural networks to optimize the intra-frame coding process in HEVC. The final experimental results show that the proposed algorithm can reduce the storage and transmission bit-rates while reducing the delay caused by encoding.  
During the development of future intelligent ships, higher-resolution surveillance video and 3D video will also be introduced into the system development to promote the progress of ship autopilot, provide more detailed visual information to the shore-based maneuvering console and make the ship visual obstacle avoidance better, but the type and volume of SVSV data will also increase exponentially. Therefore, we will improve and optimize the adaptability of the algorithm in this paper in the future and do further research on the optimization of the inter-frame coding of SVSV.  
Methodology, H.L.; investigation, H.L and Z.W.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, Y.Z. and Z.W.; supervision, Y.Z. and project administration, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Liao Ning Revitalization Talents Program (No. XLYC1902071).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 13: R-D curves contrast.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 3 Elements of Frequency Selective Surfaces
### Class 2: Loop Type  
This group contains the closed loop elements, which are either simple or meandered to reduce the size. These elements are resonant when the total lengths of the metallic trace is equal to full wavelengths at the operation frequency. Transmission line theory explains the operations mechanism of the cross-shaped loop element. The half dipole arms are akin to a shorter dipole that is loaded with reactance. This class also has hexagonal elements that have better scan stability [111].
## 3 Processes for Bioenergy Production
### Biochemical Methods
#### 3.2.4 Anaerobic Digestion
The advantages of SS-AD, in comparison to L-AD, are: lower reactor volumes, higher organic loading rates (OLRs), less water for dilution, a lower mixing requirement, no floating substrates (in the bioreactor), lower costs for managing the digestate, and overall, a lower energy input for operation [52; 55]. Typically, due to the higher OLR during SS-AD, the accumulation of volatile fatty acids (VFAs), organic acids, and ammonia leads to an overload of the reactor and the whole system is prone to fail, especially when energy crops are used as a mono-substrate. Thus, the high solids content in SS-AD, which is considered an advantage, can also produce dead zones in the reactor and can lead to reductions in CH\({}_{4}\) production efficiency, as well as causing an overload of the reactor. Therefore, in this regard, further research is needed on reactor configurations to eliminate the build-up of toxic and/or inhibitory compounds in SS-AD systems. The efficiency of SS-AD systems can be further improved through different reactor operating strategies, adequate OLRs and retention times, maintenance of healthy and well-balanced microbial communities, and subsequent identification and quantification of the efficiency of lignocellulosic-biomass-digesting microbes [29]. A possible future path for SS-ADs could be the two-step co-digestion of energy crops and animal manure. This approach could improve the microbial hydrolysis of substrates, prevent VFA build-up, and provide the necessary nutrients and trace elements. It would also be interesting to study and understand how the maturity and composition of animal feed affect the digestibility of biomass [29]. The AD of N-rich substrates, such as animal manure, is also not recommended as it can result in digester instability caused by ammonia toxicity from the rapid degradation of organic nitrogen such as urea and protein [56]. In co-digestion processes, substrates from different origins (e.g., animal manure, agroindustrial wastes, crop residues, etc.) are mixed to increase their degradability and also to adjust the C/N ratio and, therefore, the efficiency of the process. The C/N ratio of feedstocks is critically important to facilitate the conversion of lignocellulosic biomass to CH\({}_{4}\)[57; 58; 59]. Thus, co-digestion of carbohydrate-rich lignocellulosic biomass with nitrogen-rich animal waste has significant implications for the maintenance of an optimal C/N ratio for commercial CH\({}_{4}\) production with renewable feedstocks [5; 58]. Several studies to date have demonstrated the successful anaerobic co-digestion of livestock wastes and lignocellulosicoses. [PERSON] et al. [59] reported the co-digestion of rice straw and swine manure in a series of batch experiments where co-digestion increased the CH\({}_{4}\) yield by an impressive 71% compared to the mono-digestion of rice straw, using a pig manure to rice straw ratio of 2:1 (on a vs. basis). The C/N ratios of the co-substrates (i.e., the mixture of swine manure and rice straw) and mono-substrate (i.e., rice straw) were 21.7 and 47, respectively.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### Covering Problems
#### 4.2.1 Minimization Variants
Packing-Covering Duality and Erdos-Posa Property.Given a set system \((V,\mathcal{C})\) where \(V\) is the universe and \(\mathcal{C}=\{C_{1},\ldots,C_{m}\}\) is a collection of subsets of \(V\), Hitting Set is the problem of computing the smallest \(S\subseteq V\) that intersects every \(C_{i}\), and Set Packing is the problem of computing the largest subcollection \(\mathcal{C}^{\prime}\subseteq\mathcal{C}\) such that no two sets in \(\mathcal{C}^{\prime}\) intersect. It can also be observed that the optimal value for Hitting Set is at least the optimal value for Set Packing, while the standard LP relaxations for them (covering LP and packing LP) have the same optimal value by strong duality. Studying the other direction of the inequality (often called the packing-covering duality) for natural families of set systems has been a central theme in combinatorial optimization. The gap between the covering optimum and packing optimum is large in general (e.g., Dominating Set/Independent Set), but can be small for some families of set systems (e.g., \(s\)-\(t\) Cut/\(s\)-\(t\) Disjoint Paths and Vertex Cover/Matching especially in bipartite graphs). One notion that has been important for both parameterized and approximation algorithms is the Erdos-Posa property [138]. A family of set systems is said to have the Erdos-Posa property when there is a function \(f:\mathbb{N}\to\mathbb{N}\) such that for any set system in the family, if the packing optimum is \(k\), the covering optimum is at most \(f(k)\). This immediately implies that the multiplicative gap between these two optima is at most \(f(k)/k\), and constructive proofs for the property for various set systems have led to \((f(k)/k)\)-approximation algorithms. Furthermore, for some problems including Cycle Packing, the Erdos-Posa property gives an immediate parameterized algorithm. We refer the reader to a recent survey [139] and papers [119; 140; 141]. The original paper of Erdos and Posa [138] proved the property for set systems \((V,\mathcal{C})\) when there is an underlying graph \(G=(V,E)\) and \(\mathcal{C}\) is the set of cycles, which corresponds to the pair Cycle Packing/Feedback Vertex Set; every graph either has at least \(k\) vertex-disjoint cycles or there is a feedback vertex set of size at most \(O(k\log k)\). Many subsequent papers also studied natural set systems arising from graphs where \(V\) is the set of vertices or edges and \(\mathcal{C}\) denotes a collection of subgraphs of interest. For those set systems, Erdos-Posa Properties are closely related to Set Packing introduced in Section 4.1.3 and \(\mathcal{F}\)-Deletion problems introduced in Section 4.6.
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 5 Performance Evaluation
### Simulation Results
#### 5.3.3 OTPP vs. Online Path Planning Algorithm  
Figure 15a,b show the OTPP compared with online path planning algorithms in terms of localization errors and coverage. The online path planning algorithms of interest are MMN [19], Li [20], and MBL [21], with unknown nodes randomly deployed over a square sensing field of 1000 m \(\times\) 1000 m, and communication range, set to 35 m, 50 m, 70 m, and 90 m, respectively. It is observed that the localization error of the OTPP \(\cong\) 0.2\(CR\) is less than that of MBL, MMN, and Li. Although the localization accuracy of MMN and Li are close to that of OTPP, the localization coverage of these online path planning algorithms is less than 50%, whereas OTPP can achieve \(\cong\)95%. As the communication range increases, the coverage area is also improved (e.g., MBL); however, the MAN will determine the direction and path of the trajectory based on the number of location request messages from unknown nodes, and there is no guarantee that all the unknown nodes can receive three packets from the MAN.
# Finite-Horizon Kinetic Energy Optimization of a Redundant Space Manipulator
## 3 Results and Discussion
### Simulation Results
Figure 8: Comparison of the spectral radius of the inertia matrix using PMKE (dashed line) and LSV (dotted line) methods during the execution of trajectory 4. Figure 7: Trajectory 4, comparison between PMKE (**left**) and LSV (**right**). First joint: blue lines; second joint: red lines; third joint: yellow lines. The figure shows that the maximum eigenvalue of the inertia matrix for the PMKE trajectory is always lower than the maximum eigenvalue for the LSV one, indicating that the matrix coefficients are generally lower. Particularly, the maximum eigenvalue for PMKE is as low as 0.0556 for \(t=6.33\) s, while for LSV its minimum value is 0.0998 for \(t=5.71\) s.  
While the comparison with the LSV method can help to understand how the PMKE method stands with respect to another local method, it is also worth noticing that the solutions obtained with the PMKE method for the considered trajectories are very similar to the globally optimal ones. Figures 9-12 show the PMKE solutions on the left and the global optimal solutions on the right, both stroboscopic plots and joint variables. For trajectories 1 and 2, the similarity can be easily noticed in both the stroboscopic and joint variables plots. The longer trajectories (3 and 4) feature some difference in terms of joint positions; however, the solutions are still very similar as it can be noticed in the stroboscopic plots. A complete overlap is not possible with the limited prediction window of the PMKE method: the longer the trajectory is, the higher the chance that local effects will influence the final energetic cost. However, the PMKE method is able to compute the best joint velocities to perform kinetic energy minimization with the chosen prediction window, and these are very similar to the ones of the global optimal solution.
# Evaluation of the Large-Length Indoor Standard Installation Based on Single Point Scanning
###### Abstract  
The large length indoor standard installation (LLISI) serves as a standard traceability system for large-scale measuring devices, and hence evaluating the accuracy of its measurements during motion is of great importance. A laser tracker, as a single-point scanning method with strong anti-interference ability and high accuracy for 3D measurement, can meet the measurement requirements of LLISI. In this study, a laser tracker was used to evaluate the accuracy of LLISI during motion based on the single-point scanning method. Fifteen measurement combinations including five measurement intervals and three measurement speeds were selected in the experiment and the results were compared with the measurement data of a laser interferometer. By analyzing the local movement speed of the ISS platform, the uncertainty in local measurement speed of the platform was evaluated. The results showed that the laser tracker has high measurement accuracy and good repeatability for the measurement of LLISI, which can provide strong support for data resource protection of LLISI.  
single point scanning; speed measurement; large-length indoor standard installation (LLISI); laser trackers; laser interferometer
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 7 Performance Evaluation  
### Symmetric Topologies  
We first evaluated our procedure in symmetric scenarios in order to evaluate whether it can reduce the variance observed in the LS-based procedure.
## 5 The Proposed Scheme
### Construction of ABE-RS  
In this section, we outline our detailed scheme comprising ten algorithms. First, the \(AA\) executes the setup algorithm to initialize system parameters and establish essential access control structures for anomaly detection. Users run the \(\mathsf{Idkgen}\) algorithm to obtain identity key pairs, enabling secure identity verification. The \(AA\) employs the \(\mathsf{AttKgen}\) algorithm to generate attribute-based decryption keys, enforcing strict attribute-based access control over video surveillance data. The \(DO\) encrypts video data using the \(\mathsf{EncML}\) algorithm, integrating a revocation list to swiftly deny access to malicious or anomalous users. The \(DO\) also employs the \(\mathsf{IdxEnc}\) algorithm to construct a secure keyword index set \(I_{w}\), facilitating controlled and efficient keyword searches within encrypted video data. Subsequently, the \(DU\) executes the AKgen algorithm to obtain authorization keys for authentication, and it then runs the \(\mathsf{Trapdgen}\) algorithm to create trapdoors \(T_{w}\) for keyword queries, uploading them to the cloud server. Upon receiving queries, the cloud server performs the \(\mathsf{Verify}\) algorithm to authenticate users. It then executes the \(\mathsf{Search}\) algorithm to locate and return matching encrypted video files only to authorized users. Finally, the \(DU\)decrypts the retrieved ciphertext using the Dec algorithm if their attributes satisfy the defined access permissions.  
As shown in Figure 3, the proposed ABE-RS scheme is a cryptographic protocol composed of ten logically sequential algorithms. These algorithms are executed in a well-defined order across different entities (AA, DU, DO, and CSP) as part of a theoretical security framework. Unlike real-time systems, the workflow does not involve time-sensitive or concurrent execution. Therefore, issues such as temporization failure or algorithm rescheduling are not applicable. Furthermore, each algorithm operates on distinct cryptographic inputs and does not rely on shared stateful resources, thus avoiding resource contention. This design ensures predictable and secure execution in practical implementation.