esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
### Shortcomings of Existing Approaches  
Based on the challenges from Section 2.4, the related work can be evaluated to derive apparent shortcomings (\(SH\)); see Table 1. Existing strategic network design approaches mainly focus on strategic structural decisions rather than tactical and operational decisions. With respect to structural decisions, the approaches partially consider the uncertainty and the large number of design parameters, but relevant data farming applications only present use cases within production lines. Only one approach addresses a closely related application in the supply chain context. Therefore, there is a lack of solution space management for operational decisions in strategic network design (\(SH\)).  
Moreover, strategic network design approaches either do not cover the entire solution space or reduce the solution space to a very limited number of alternative solutions. Only one strategic network design approach [21] uses a simplified solution space management process. Most relevant data farming applications use established data farming experiment design methods that properly cover the solution space. However, all data farming applications deal with a limited number and interdependencies of input planning parameters. Thus, another shortcoming arises from the need for an approach that covers the solution space of complex systems with a large number of highly interdependent design parameters (\(SH\)2).  
Furthermore, most strategic network design approaches apply mathematical optimization models to generate an optimal alternative solution with limited transparency of the solution path. A user of an optimization algorithm cannot understand how the algorithm finds its optimal solution and thus cannot understand the interdependencies of planning parameters and resulting network performance. Moreover, relevant data farming applications only show a transparent solution path due to the limited number of input design parameters and interdependencies. In contrast, a transparent solution path and a structured process are critical to an industrial application of solution space management. This leads to another shortcoming in providing a user-friendly, industrial application-focused, well-structured, highly transparent solution space management approach (\(SH\)3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Criteria** & **CH1** & **CH2** & **CH3** \\ \hline \multicolumn{5}{c}{**Publications**} \\ \hline [PERSON] [27] & _Strategic_ & & & & \\ [PERSON] et al. [29] & _network_ & & & & \\ [PERSON] [28] & _design_ & & & & \\ [PERSON] [21] & _approaches_ & & & & \\ [PERSON] [30] & & & & & \\ \hline [PERSON] et al. [31] & & & & & \\ [PERSON] et al. [32] & _Relevant data farming_ & & & & \\ [PERSON] et al. [33] & _applications_ & & & & \\ [PERSON] et al. [35] & & & & & \\ \hline \hline \end{tabular} \(\bullet\) Predominantly fulfilled, \(\blackblackblackblackblack\) Partially fulfilled, \(\odot\) Not fulfilled.  
\end{table}
Table 1: Evaluation of the related work based on the challenges (\(CH1\)–\(CH3\)).
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 1 Introduction
However, the network coding technique cannot been applied in practical UWSNs because too many nodes are involved in data transmissions and there are no corresponding routing protocols for this technique. In this paper, we propose a novel cross-layer routing protocol base on network coding (NCRP) for UWSNs to overcome the drawbacks of current UWSNs routing protocols. NCRP is a kind of sender-side routing protocol but it does not need to keep updating neighborhood information frequently as most sender-side protocols do. It combines the design of transport layer and network layer. The transport protocol we use here is network coding based hybrid ARQ protocol (NCHARQ) [16]. The maintenance and correction of paths are operated along with data packets transmissions and thus our proposed protocol can increase channel utilization rate and reduce energy consumption significantly. The main contributions of this paper are listed as follows:  
1. Extended opportunistic receiving mode. We design a routing protocol with a primary path and several secondary nodes. The nodes in the network can not only receive packets from their previous hop nodes in the primary path, but also obtain encoded packets from long-distance nodes under certain probabilities. This design can make full use of multicast nature in underwater transmissions and save energy. The network is more robust to environment changes as multiple nodes can participate in one decoding procedure and the breakdown of a single node will not effect the network performance heavily. 2. Based on the node mobility in underwater environment, we design a new route maintenance and update algorithm. The new algorithm can delete or add nodes when detecting inefficient transmissions without frequently updating neighborhood information with beacon messages. So packet collisions can be reduced and the route breakdown can be avoided. Moreover, data transmissions can be more efficient and thus energy consumption is decreased. The rest of this paper is organized as follows. In Section 2, we review the related works in this field. Next, we give the design background in Section 3, which includes our channel model, network model and an overview of NCHARQ. We present the detailed design of NCRP in Section 4. The effect of some parameter settings and simulation results are given in Section 5 and we conclude our paper in Section 6.
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
## 2 Methodology
### Experimental Setup
#### 2.1.1 Structure of Experimental Setup  
Here, the experimental setup, illustrated in Figure 1(I,II), consists of aluminum profile rigid bars designed to mimic the human arm. One bar represents the upper arm and the other bar represents the forearm, both of a length of 0.3 m and a weight of 0.135 kg. The bars are connected by a hinge, allowing for approximate frictionless movement and the simulation of the elbow joint at 90\({}^{\circ}\) as shown in Figure 1(I,V) and 120\({}^{\circ}\) as shown in Figure 1(II,VI). The upper bar is fixed to the frame using a nut and bolt and can be adjusted at specific angles (but during the experiment, we keep the upper bar fixed vertically). The lower bar's angle can be adjusted in the full range of elbow joint, i.e., 52\({}^{\circ}\) to 180\({}^{\circ}\), as per anthropometric data [38]. Here, 180\({}^{\circ}\) represents the fully extended posture of the forearm. A hanging system has been designed to suspend the load at the end of the lower bar to emulate different forearm weights as the load.  
To replicate the actuation mechanism of a cable-driven exosuit, a motor (Rhino RMCS 2103 DC motor) was mounted onto the aluminum profile bar, as shown in Figure 1(III), driving the nylon-braided cable. The lower bar on the experimental setup employs a 50 kg capacity load cell (Phaeton CZL-U-700 e) with a \(\pm\)0.1% accuracy to measure cable tension. This load cell is housed in a 3D-printed enclosure, as shown in Figure 1(IV), which allows it to tilt so that the cable remains parallel to the measuring axis. The readings of the load cell can be viewed on an LCD display attached to the lower bar. A pulley, which acts as an anchor point, is positioned on the upper bar to guide the cable to the motor. The distance between the anchor points and the joint is adjustable to simulate different anchor positions. The motor was selected based on the static model calculations, presented in Section 2.2, which estimated a required torque of 6 Nm to account for the weight of the forearm and hand (approximately 2 kg for an adult) [38]. A motor capable of handling a maximum load of 5 kg was chosen, incorporating a safety factor. The Arduino UNO microcontroller and joystick were used to actuate the motor in order to set the lower bar at the desired position. Overall, the experimental setup enables the real-time monitoring of the cable tension for different joint angles and anchor positions.
## 4 Experiments and Results
### Deployment Node Setup  
For carrying out the experiments in the next sections, a lab server with a public IP and a certified domain (for secure connection) was used. The machine was equipped with an Intel Core i7-3820 3.6 GHz and 16 Gb RAM. The server was configured with a debian-based Linux OS and Node.js to deploy the ATON services through the PM2 process manager (see Section 3.1) in cluster mode with 4 cores (2 threads each) handling incoming client connections, remote requests and collaborative sessions (including users' states and audio streaming). The framework was configured with a few users who created, edited and published several 3D scenes through Shu back-end (see Section 3.4). Access to the collections was possible through an integration of data folder with _NextCloud_ ([[https://nextcloud.com/](https://nextcloud.com/)]([https://nextcloud.com/](https://nextcloud.com/)), accessed on 22 November 2021)--on the same server--allowing users to easily and privately manage their content.
## 5 Conclusions  
In summary, we performed a simulated control tower experiment and measured fifteen parameters representing the physical characteristics and abilities of ATCOs in this study. Eleven static parameters were measured in the eight situations, and four dynamic parameters were quantified during the experiments of the four states. The effects of fatigue and tension on the physical characteristics and abilities of ATCOs and the influence mechanisms were investigated. The following conclusions were drawn:  
1. Both fatigue and tension affected the performance of the physical characteristics or abilities of the ATCOs. The indicators of fatigue in decreasing order of influence were HR, RTOS_AVG, STT, RTOS_SD, DHR_AVG, RTL, ROA, RTS, ART, and TDSA. In the FA state, the HR, DHR_AVG, STT, ROA, and TDSA were lower, and the RTL, RTS, ART, RTOS_AVG, and RTOS_SD were higher than those in the AL state. The attention, perception, reaction time, decision-making abilities, and comprehensive performance of the ATCOs were adversely affected by fatigue.
2. The indicators of tension in decreasing order of influence were DHR_AVG, STT, DHR_SD, RTOS_SD, and ART. Tension decreased the STT and increased the ART, DHR_AVG, DHR_SD, and RTOS_SD. The attention, decision-making abilities, and comprehensive performance of the ATCOs were adversely affected by tension.
3. Both fatigue and tension initially affected the ATCOs' physiological characteristics, which, in turn, impaired their physical abilities. However, the influence mechanisms were different. The dominant effect of fatigue was slowing down, whereas the primary effect of tension was instability.  
These results are helpful in assessing and managing the fatigue and tension states of ATCOs, quantifying the effects of fatigue and tension on their behavior and ability, and ultimately, improving the safety of air traffic control.  
Methodology, writing, and editing, X.Z.; investigation, M.L.; experiment design, P.B.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Joint Fund of Civil Aviation Research of National Natural Science Foundation of China (No. U1933122 and No. U1533117) and the National Key Research and Development Program of China (No. 2022 YFB4300904).  
All subjects gave their informed consent for inclusion before they participated in this study. This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Civil Aviation University of China (Protocol code E2019012, 1 March 2019).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 4 Discussion  
### Model Performance in the Subtropical Coniferous Forests  
The goodness of fit for the DBH-height allometric equation after model calibration was better than the default value in ED-2.2 (Table 3), indicating that the ED-2.2 model can reliably simulate tree height and related tree biomass (e.g., leaf, sapwood, heartwood, bark, and root). Although ED-2.2 models are commonly used in tropical forests such as the Amazon rainforest [49; 82], they are rarely used to model the dynamics and response of subtropical coniferous forests to disturbances in southern China [65]. Consequently, we refitted the DBH-height allometric equation based on forest inventory data and corrected the sensitive physiological and ecological parameter to localize the parameter in ED-2.2 as much as possible (i.e., SLA).  
The ED-2.2 model qualitatively captured the seasonality of NPP in subtropical coniferous forests (Figure 1), indicating that the model can be used to simulate the monthly productivity dynamics of subtropical coniferous forests in southern China. The results of the model for monthly productivity changes in _C. lanccolata_ and _P. massoniana_ forests are highly similar to those of another study on the monthly growth of _C. lanccolata_ forests in subtropical China [83].  
The mismatch between simulated annual NPP and MODIS-NPP gradually decreased over time (Figure 2), which means that we can use ED-2.2 to accurately predict the future productivity dynamics of subtropical coniferous forests. This study found that ED-2.2 performed better in simulating NPP in the second period (2014-2021) compared with the first period (2005-2013) of model validation. Although ED-2.2 overestimated annual NPP compared with MODIS products, the error between model simulation and remotely sensed observation gradually decreases over time. Another study using the ED2 model to predict  
Figure 5: Relationships between canopy air space temperature [K] and NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)]. The blue points represent the NPP under the baseline scenario (B0) and before (January–May) and after (November–December, except for October) the warming scenarios. The light, moderate, and dark red points represent the NPP during (July–September) and after (only October) a mild (W1), moderate (W2), and severe (W3) warming scenarios, respectively. The boxplots on the top and right indicate the distributions of canopy temperature and NPP under three warming scenarios (W1, W2, and W3), respectively.  
changes in the carbon flux of a Puerto Rican tropical forest under climate change scenarios showed similar results [84]. The mismatch in evaluating NPP might be derived from the uncertainty of remotely sensed products, and when soil water stress was present, MODIS data have the potential to underestimate vegetation production [85].
# University Social Responsibility: The Case of Italy
## Appendix A The Survey  
1. Does the university adopt formalized policies (in terms of procedures and controls) in favor of University Social Responsibility (e.g., sustainability analysis of its suppliers, integration of the USR with the governance, living lab projects, involvement of its stakeholders in sustainability practices, etc.)?  
* Yes
* No
* Other  
2. Has the University included an explicit reference to the issue of sustainability in its Statute?  
* Yes
* No
* Other  
3. Does the university have ad hoc training courses on topics related to sustainability?  
* Yes
* No  
4. Does the university organize awareness-raising events on issues related to sustainable development, including in the academic environment (e.g., academic events, conferences, etc.)?  
* Yes
* No
* Other  
5. Does the university join, [...], to national and/or international associations/Ranking/Networks?  
* Yes
* No
* Other  
6. Does the university allocate research funds to university sustainability?  
* Yes
* No  
7. Does the university have a specific organizational unit dedicated to university sustainability?  
* Yes* No
* 8. Does the university have a website or a page within the university website dedicated to university sustainability policies?
* Yes
* No
* What are the main challenges that the university should take up to be \"more sustainable\"?
* Reduction of emissions related to the university structure (reduction of energy consumption, waste, etc.)
* \"Virtuous\" individual behavior of university attendees (e.g., sustainable mobility, attention to waste disposal, etc.)
* Implementation of on-line services to reduce travel and resources (paper etc.)
* Reduction of food and non-food waste (energy, paper, etc.)
* Greater attention to issues related to the transport and mobility of students, professor, and technical-administrative staff
* Greater involvement and integration of the culture of sustainable development within the educational proposal
* Other
* 10. What does the university believe are the barriers to USR policies (including writing social reporting forms)?
* Perception of a non-priority for the university
* Insufficient resources/time to devote to the project
* Non-\"profitability\" of the project
* Difficulty in finding qualified personnel to carry out this type of project
* Other
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
## 2 Biped Robot Modeling
### Motion Equations  
Generally, seven degrees of freedom are needed to describe a five-link planar biped robot. However, according to the definition of the walking cycle in Section 2.2, foot-1 always keeps contact with the ground and does not slip in the current step; hence, the biped robot can be regarded as a rooted system. Therefore, the generalized coordinates can be described as \(q=[q_{1};q_{2};q_{3};q_{4};q_{5}]\) (see Figure 3). A coordinate frame is attached to the flat ground called the world frame, its origin is at the ankle of foot-1. In addition, counterclockwise is the positive direction of the angle. In this paper, we assume the biped robot walks from left to right on level ground.  
Based on [PERSON]'s formula, the motion equation of SSP is written as the following compact form:  
\[M(q)\ddot{q}+C(q,\dot{q})\dot{q}+G(q)=B\tau, \tag{1}\]  
Figure 3: Generalized coordinates of the five-link biped model. The x-direction is the walking direction and the z-direction is the upright direction, angles are measured by counterclockwise direction.  
Figure 2: Walking cycle including double support phase (DSP) and single support phase (SSP). Origin of world frame is at the stance ankle. L is the step length, and \(S_{1}\) and \(S_{2}\) are the hip horizontal positions at the beginning and at the end of SSP, respectively.  
where \(M\) is symmetric and positive defined inertia matrix, \(C\) is the matrix related to centrifugal and Coriolis terms, \(G\) is the vector of gravity terms, \(\tau=[\tau_{1};\tau_{2};\tau_{3};\tau_{4};\tau_{5}]\) is the vector of actuated joint torques, and \(B\) is a constant matrix composed of ones and zeros representing the contribution of the actuated joint torques.  
During the DSP, it can be considered that foot-2 is constrained on the basis of the motion equation of the SSP. The constraint equation can be expressed as  
\[h=\left[\begin{array}{c}x_{2}(q)+L\\ z_{2}(q)\end{array}\right]=0. \tag{2}\]  
where the additional constraints \(x_{2}(q)\) and \(z_{2}(q)\) are the horizontal and vertical positions of the ankle of foot-2, respectively; L is the step length as shown in Figure 2. The motion equation of the DSP is  
\[M(q)\ddot{q}+C(q,\dot{q})\dot{q}+G(q)=B\tau+J^{\mathrm{T}}\Gamma, \tag{3}\]  
where \(J=\partial h/\partial q\) is the constrained Jacobian matrix, \(\Gamma\) is the Lagrange multiplier.
## 3 Materials and Methods
### Genome Sequencing and Mining  
The genomic DNA of _T. afroharzianum_ ACCC 33109 was extracted using the CTAB method. Two libraries with an insert size of 20 kb and 350 bp were constructed using the SMRTbellTM Template Kit (Pacific Biosciences, Menlo Park, CA, USA) and Next(r) UltraTM DNA Library Prep Kit (New England Biolabs, Ipswich, MA, USA) following the manufacturers' instructions, respectively. The PacBio Sequel and Illumina NovaSeq platforms were used to sequence the whole genome at Novogene Bioinformatics Technology Co., Ltd. (Beijing, China). Clean reads (>6000 bp) were preliminarily assembled using the SMRT Link v5.0.1 and corrected using the variant Caller module for fine mapping. Genes were annotated using Genewise, AUGUSTUS v.3, and MAKER v.2 with the default parameters, which were further integrated by GLEAN ([[https://sourceforge.net/projects/glean-gene/](https://sourceforge.net/projects/glean-gene/)]([https://sourceforge.net/projects/glean-gene/](https://sourceforge.net/projects/glean-gene/)), accessed on 18 March 2025). The gene models were functionally annotated against the NR, GO, KEGG, KOG, Pfam, Swiss-Prot, CAZy, Secondary Metabolism, Secretory, DFVF, P450, PHI, and TCDB databases, respectively. Genes and gene clusters related to terpene biosynthesis were predicted using antiSMASH v.4.0.2 and mapped to the KEGG pathway of terpenoid synthesis. Core genes and specific genes were analyzed by the CD-HIT rapid clustering of similar proteins software with a threshold of 50% pairwise identity and 0.7 length difference cutoff in amino acid. Homologue searches for putative functions and multiple sequence alignments of the 15 _TaTS_ genes from ACCC 33109 were performed using the NCBI blastX and ClustalW, respectively. The unrooted phylogenetic trees were constructed using the maximum likelihood method in MEGA X. The bootstrap value was set as 1000 replicates.
## 2 Related Work
### Encrypted Traffic Classification and Feature Engineering
#### 2.2.3 Deep Learning-Based Models  
The most significant advantage of deep learning is that it can directly execute with no prior expert knowledge of feature engineering. When used in encrypted classification, these models can directly take the raw encrypted traffic as the input. The base model includes CNN [39], text-CNN [40], LSTM [41] and CapsNet [42]. With the development in clustering methods, the combination of different deep learning models makes a better effect than single model, such as Deep Packets [43] (integrated CNN and SAE), STNN [28] (integrated LSTM and 3D-CNN), LS-CapsNet [44] (GRU for feature extraction and CapsNet for classification), and CENTIME [45] (integrated ResNet and AutoEncoder).  
These deep learning methods improve the accuracy and lower the research barrier, but the performance is still not satisfied in the actual network environment.