esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Detailed analysis of the air mass coupling state in the vertical canopy profile through double-layer eddy covariance observations of the sparse canopy is necessary for specific sites using partitioning evapotranspiration. We investigated whether the state of air mass coupling in the vertical layer of the sparse canopy affects the double-layer eddy covariance of partitioning evapotranspiration and further compared the consistency of ecosystem transpiration and evaporation between the double-layer eddy covariance and single EC methods for above the canopy: the modified relaxed eddy accumulation method, the conditional eddy covariance method, and the flux variance similarity method. Our results indicate the following:  
(1) Even in a sparse canopy, it is necessary to investigate the coupling state of air masses in the canopy profile separately during day and night because the mixing state by strong thermal effects during the day can confuse the decoupling of air masses at night, which leads to further errors in the determination of the coupling state. This study showed a decoupling phenomenon in both diurnal and nocturnal canopy vertical-layer air mass mixing, which is especially serious at night.  
(2) The study on the separation of evapotranspiration components using double-layer eddy covariance considering different coupling states at day and night showed that although the decoupling process existed during the daytime, it did not significantly affect the separation results of evapotranspiration. The separation results of evapotranspiration in the fully coupled state derived from the most stringent threshold diagnosis method were consistent in terms of magnitude and intraday variation trends.  
(3) Based on the double-layer eddy covariance results after the coupled state diagnosis, ecosystem evapotranspiration was highly correlated (0.86-0.97) with the rest of the separation results based on the single-layer eddy covariance above the canopy. The MREA method used to separate ecosystem evapotranspiration in the sparse canopy had the best effect, and the daily changes in all three months were consistent with the coupled state separation results. The FVS method was the best for ecosystem evaporation.  
Conceptualization, X.W. and J.Z.; Methodology, X.W., Y.Z., S.S., P.M., J.Z. and H.H.; Formal analysis, X.W. and H.H.; Investigation, X.W.; Resources, X.G.; Data curation, X.W., Y.Z. and J.Z.; Writing--original draft, X.W.; Writing--review & editing, J.Z. and H.H.; Project administration, P.M., J.Z. and H.H.; Funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Special Project on National Science and Technology Basic Resources Investigation of China (2021 FY100701).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results  
### Error Signals and Locking Performance  
To maximize the signal-to-noise (SN) ratio of the error signal in the hybrid CW+pulsed mode, the gain of the PD amplifier was adjusted to keep the CW signal slightly below the maximum input level of the Laselock (1 V). During the pulses the detected signal reached the saturation limit of the detector (5 V), so that the input signal was clipped, reducing the amount of noise introduced. Furthermore, the dither amplitude was increased by a factor of four compared to our usual CW operating settings. In the Laselock system the dither amplitude is given as a percent value of the maximum voltage range (150 V) and a value of 0.2% or 0.3 V was used in the experiment. The phase of the lock-in detection was set to 90\({}^{\circ}\)degree and the frequency of the dither was tuned to a mechanical resonance of the piezo assembly at about 147 kHz where the error signal was maximized. At dither frequencies close to a multiple of the PRR of the Nd:YAG laser significant additional noise was observed.  
The system was first operated with CW pumping only. When ramping the etalon piezo-voltage with PID control turned off, a clean error signal is observed as shown in Figure 2. Here the error signal has clean linearly increasing sections, separated by jumps during mode-hops of the laser frequency. The zero-crossing point of the error signal (indicated with dashed horizontal and vertical lines) coincides with minimal intensity fluctuations in the photodiode signal, indicating that the etalon transmission peak matches to the frequency of a cavity mode. Turning the piezo ramp off and activating the PID loop results in the data in Figure 3, where the error signal stays close to zero and the PD signal is stable.  
Finally, when introducing the pulsed pumping, the situation changes as shown in Figures 4 and 5. Though the PD signal now fluctuates wildly, the error signal during the piezo ramp still shows linear trends separated by mode-hops, though with significant addition of noise. In the locked state the error signal varies much more compared to the CW mode, but the amplitude of the fluctuations still stays within a smaller range than in scanned mode, so that it can be assumed that no mode-hops occur. We note that this noise only weakly scaled with pulse energy, so that this effect did not restrict power scaling.  
Figure 3: PD and error signals in CW-only mode—locked.  
Figure 2: PD and error signals in CW-only mode—scanning.
## 8 Results and Discussion of Reviewed Papers
### UAV-Gesture  
[PERSON] et al. [94] and [PERSON] et al. [95] utilized the UAV-Gesture dataset to evaluate their recommendations for human action and gesture recognition. They used the UAV-Gesture collection of 119 high-definition RGB movies representing 13 unique motions used to control UAVs. The dataset is ideal for testing recognition systems due to its diversity of views and movement similarities. The [PERSON] et al. method achieved 0.95 action recognition accuracy on the UAV-Gesture dataset. Mean precision, recall, and F1-score for the system were 0.96, 0.95, and 0.94. Several investigations supported by confusion matrices showed the system's ability to distinguish gestures. [PERSON] et al. found that their gesture recognition method outperformed DD-Net [96] and P-CNN [45] by 3.5% in accuracy. The authors stressed the need of using 2D skeletal data from movies to boost recognition accuracy. Real-time performance makes their method suitable for embedded AI hardware in dynamic UAV situations.
# A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem
## 4 MDP Framework for HFSP
### Description of MDP  
Reinforcement learning is usually described with an MDP. In HFSP, obviously, the location of the workpiece in the next stage is only related to the current position, but not to the previous location, which is Markov property, manifesting HFSP can be attributed to a MDP.  
An MDP is a tuple \((S,A,P,R)\), where \(S\) denotes a set of states, \(A\) is a set of actions, \(P:S\times A\mapsto[0,1)\) is the state transition distribution upon taking action \(a\) in state \(s\), and \(R:S\times A\times S^{\prime}\mapsto R\) is the reward after taking action \(a\) and transferring to state \(s^{\prime}\) in state \(s\).  
An agent in reinforcement learning learns the policy \(\pi\) by continuously interacting with the environment over a number of time steps and getting environmental feedback, at each time step \(t\), the agent selects the action \(a\) at state \(s_{t}\) and transfers to the next state \(s_{t+1}\) from the policy \(\pi(s)\). A policy \(\pi:S\times A\mapsto R_{+}\) is a mapping from state-action pair to the probability of taking action \(a\) in state \(s\), so \(\sum_{a\in A}\pi(s,a)=1\) (\(\forall s\in S\)).  
The Q-learning algorithm is one of the most illustrious algorithms in reinforcement learning. The action-value, or \(Q(s,a)\), of a particular state under policy \(\pi\), is:  
\[Q(s,a) =\mathrm{E}(\sum_{k=0}^{\infty}\gamma^{k}r_{t+k+1}|\,s_{0} =s,a_{0} =a,\pi), \tag{8}\]  
where \(r_{t+1}(s,a)\) is the reward function in time step \(t\), and \(\gamma\) is the discount factor.  
The Bellman operator and the optimal Bellman operator for policy is defined in literature [37] as Equations (9) and (10):  
\[T^{\pi}Q(s,a) =\underset{s^{\prime},\pi,a^{\prime}}{E}(r(s,a)+\gamma Q(s^{ \prime},a^{\prime})), \tag{9}\] \[T^{\ast}Q(s,a) =\underset{s^{\prime},\pi}{E}(r(s,a)+\gamma\underset{a^{\prime}} {\max}Q(s^{\prime},a^{\prime})), \tag{10}\]  
where the expectation is over next state \(s^{\prime}\sim P(\cdot,s,a)\), the reward \(r(s,a)\), and the action \(a^{\prime}\) is from policy \(\pi(s^{\prime})\). Both the Q-value function and the optimal Q-value function are the fixed points of corresponding Bellman equations. [PERSON] [38] proved the uniqueness of the fixed point of each operator and the convergence of value iteration. The iteration equation of Q-value is represented as follows [39]:  
\[Q(s,a)=Q(s,a)+\alpha(r+\gamma\underset{a^{\prime}}{\max}Q(s^{\prime},a^{\prime })-Q(s,a)), \tag{11}\]  
where \(\alpha\) is the learning rate.
# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 2 Materials and Methods
### Agent Model
#### 2.3.1 State Space  
The state space input included a single-channel semantic label map generated by the semantic segmentation model, with pixel categories covering road boundaries, lane lines, drivable and non-drivable areas, as well as on-road elements (traffic participants, obstacles), and off-road elements (buildings, trees, etc.). The single-channel semantic label maps generated by the DeepLabv3P network were downsampled to a fixed resolution (256 \(\times\) 256 pixels) and then fed into the CNN used by the reinforcement learning agent. This method ensured computational efficiency, reduced memory consumption, and retained the essential spatial and semantic information required for decision-making. Additionally, it included information such as vehicle speed and distance provided by radar and the Global Navigation Satellite System (GNSS). GNSS and radar data were fused as additional numerical features with the features extracted by the CNN from the semantic label map. Specifically, the CNN processed the single-channel semantic label map to extract spatial and semantic features. At the same time, GNSS and radar data, including vehicle speed, distance to obstacles, and lateral deviation, were represented as normalized numerical vectors. During the fully connected layer stage, these numerical features were concatenated  
Figure 3: Proximal Policy Optimization algorithm block diagram.  
with the CNN features, forming the complete state space for the reinforcement learning agent. The state set is represented as:  
\[s=(v_{ego},L_{ego},D_{ego},d_{c},v_{adv},d_{adv},M) \tag{6}\]  
In the formula, \(v_{ego}\) is the speed of the ego vehicle, \(L_{ego}\) is the position of the ego vehicle, and \(D_{ego}\) is the distance between the ego vehicle and the lane line. \(d_{c}\) is the lateral distance between the vehicle and the lane centerline, \(v_{adv}\) is the speed of other vehicles, \(d_{adv}\) is the distance to other vehicles, and \(M\) is the single-channel label map generated by semantic segmentation.
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 2 Production of Valeric- and Propionic-Rich VFA Streams from Agri-Food Wastes  
Production of PHBV (poly(3-hydroxybutyrate-co-3-hydroxyvalerate)), a biodegradable biopolymer with better thermal and mechanical properties than poly-hydroxybutyrate (PHB), is intended for use in bioplastics sectors, such as packaging (coated paper) and geotextile nets for agriculture [28]. The amount of 3 HV (3-hydroxyvalerate) in the copolymer typically varies from 5% to 24%, depending on the production conditions and the precursors used. A higher 3 HV content tends to reduce the crystallinity of the material, resulting in increased flexibility and improved formability [29].  
In the production of PHBV from VFAs, valeric and propionic acids play a fundamental role in increasing the amount of 3 HV, thereby improving the mechanical properties of the material, such as flexibility and thermal resistance [30]. Given the importance of these acids in the composition of VFAs used as precursors for bioplastic production, it is particularly important to produce a fermentation broth in which they are present in significant amounts. In the literature, [PERSON] et al. [31] discussed that the optimal concentration of valeric acid for increasing the 3 HV fraction in PHBV can be modified to achieve fractions ranging from about 10% to as high as 90%, depending on the strain used and culture conditions. Their study also mentions that propionic acid as a precursor can achieve different levels of 3 HV in PHBV.  
In order to get deep into the methods to convert agri-food wastes into VFA-rich streams, the Section 2.1 introduces the methodology to transform agri-food waste into solution-rich VFAs by means of dark fermentation. Then, typical agri-food waste currently employed in this biotransformation is summarized. Finally, the most suitable wastes for obtaining the propionic- and valeric-acid-rich VFA solution will be presented.
# Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
## 3 Methods
### HAR Task Offload in Edge Computing
#### 3.1.2 Transmission Time between Wearable Sensors and Servers  
The human activity recognition task of wearable devices can be offloaded to the edge server to reduce the local computing load, and the network transmission problem in the mobile edge environment needs to be considered in the process of computing offloading. The transmission rate between the wearable device and the server in the mobile edge environment can be calculated by \(R_{n}\), representing:  
\[R_{n}=B\log_{2}(1+f_{SNR}(d_{i,n})) \tag{3}\]  
where \(B\) represents the transmission bandwidth between user equipment and servers. \(f_{SNR}(d_{i,n})\) represents the signal-to-noise ratio within the channel.  
After that, the transmission time for uploading HAR tasks to edge servers is calculated using transmission rate \(R_{n}\) and HAR task's data size \(DS_{t_{i}}\):  
\[T_{t_{i}}^{up}=\frac{DS_{t_{i}}}{R_{n}} \tag{4}\]
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 2 Related Work
### Controller Placement in Terrestrial Networks  
The emergence of SDN controllers extends the flexibility of terrestrial networks, which achieves intelligent management and robustness. The CPP considers three main issues in the SDN-enabled network, including the locations, numbers, and roles, which are formulated as the optimization problem to minimize the latency, to reduce the cost, and so on [12; 13]. There is some research on controller placement schemes based on terrestrial SDN networks. [PERSON] et al. [14] noticed that the static assignment between switches and controllers exists in dynamic traffic, which led to high maintenance costs and varying response times in data center networks. They formulated the dynamic controller assignment problem as an optimization problem to minimize the total cost. [PERSON] et al. [15] studied the dynamic controller assignment methods based on the stable-matching game, in which flow specific requirements and quality of service (QoS) violations are considered to minimize the controller response time. [PERSON] et al. [16] proposed the heuristic-based Varna optimization algorithm for the CPP to manage the software-defined Wide-Area Network (WAN), which is able to minimize the total average latency. Moreover, there are multiple controllers responsible for distributed management, and the controller placement approach with multiple objects (e.g., network latency, reliability, and load-balancing) is addressed to realize multi-dimensional optimization [17]. The optimization algorithms for controller placement, including the linear programming [18], greedy algorithm [19], and SA [20], have also been proposed to solve comprehensive problems in various scenarios.  
In addition, the energy consumption of controller placement schemes, including deployment cost and energy consumption, has also been investigated to achieve green economics. [PERSON] et al. [21] put forward the network partitions and preferable locations of the active controllers to solve energy-aware controller placement schemes for IoT flows. [PERSON] et al. [22] proposed an energy-efficient controller association algorithm, GreCo, to decide controller placement. They reduced the number of active links to save energy; however, rerouting control paths could cause controller overload. [PERSON] et al. [23] investigated a binary integer program to solve CPP considering the number of links on the control paths and controller load. However, they assumed that the control traffic of each switch was the same. [PERSON] et al. [24] optimized the energy consumption in SDN using an energy-aware traffic engineering approach. They minimized the number of links that could be used to satisfy a given traffic demand and proposed a heuristic algorithm.  
The above researchers discussed the dynamic controller placement schemes deployed under the static scenario. In the real world, the satellites are moving all the time, which poses challenges for optimizing their entire performance under dynamic traffic.
## 3 Results  
We have shown that the NPF encoding of an input \(n\)-bits long high-entropy data creates a payload of size \(\approx n\cdot(1-\frac{2}{d})\) which requires \(n.\frac{2}{d}\) bits disambiguation information for a chosen \(d\) parameter. Larger \(d\), e.g., \(d\geq 8\), values decrease the overhead and total space consumption becomes almost equal with the input.  
The hardness of decoding an encoded data without the knowledge of the used code-word set had been addressed as early as in 1979 by [PERSON] [20], and later by others [21; 22]. More recently, non-prefix-free codes have also been mentioned [23] in that sense. We observe that in absence of any of the two partitions in the proposed scheme, decoding the original data is hard, which can help to keep data privacy. We proved that a given payload, which has been generated from an input via a known code-word mapping \(\Sigma\to W\), has more than \(2^{2^{d-1}-2}\) possible decodings in absence of the correct disambiguation information, when \(d\) is chosen such that \(n=d\cdot 2^{d}\). Since a given payload cannot be decoded properly back into the original input without the correct disambiguation information due to this exponential ambiguity, it can be preferred to encrypt only the disambiguation information, and leave the payload plain. In such a case, the amount of to-be-encrypted volume would be \(\frac{2}{d}\) of the original data. For instance, this brings a reduction of around 75% when \(d=8\) and 90% when \(d=20\). Notice that the ambiguity is computed assuming the \(\Sigma\to W\) mapping is public, where keeping the code-word set secret will introduce additional strength in privacy.  
We also analyzed the how much information is released by the disambiguation information regarding the payload. It is proved that when \(2\cdot 2^{d}\leq n\leq\tau\cdot d\cdot 2^{d}\), for \(\tau=\frac{d-1.44}{2}\), the contraction of the possible message space \(2^{n}\) due to the public disambiguation information can be accommodated by keeping the code-word set secret. Thus, if one encrypts the payload and keeps the code-word mapping secret, an attacker can not gain an advantage by knowing the disambiguation information. Such a scheme can still provide a reduction in encryption cost, which is expected to be much less when compared with the first scenario above. In this case, the \(d\) value needs to be small since the ratio of the payload increases with the larger \(d\) values. However, since smaller \(d\) values generate an overhead, it seems appropriate to use \(d=6\) in practice.  
Figure 3: NPF encoding/decoding speed in megabytes per second on different file sizes with different \(d\) values. Note that “NoMap” means no look-up table is used.
# Energy Trading with Electric Vehicles in Smart Campus Parking Lots
## 2 Proposed System Architecture for Energy Trading in a Smart Parking Lot
### Data Acquisition Layer  
The data acquisition layer is responsible for collecting data from different electric vehicle subsystem through sensor nodes and monitoring devices. Based on the application requirement, slow/fast data acquisition modules are used. Taken the electric vehicle battery as an example, there are a variety of sensor nodes that are used for monitoring the battery status, such as voltage, current, and temperature.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 4 Finite Element Modeling of Mono-Anchor Head
### Finete Element Modeling
#### 4.3.2 Material Properties  
The material properties of the carburized 42 CrMo4 anchor head were assigned to 1 mm thick outer and inner surface layers as shown in Figure 12. The wedge was made of AISI 4320 steel, and the yield and tensile strength were obtained through uniaxial tensile tests. To assess the strength of the seven-wire strand, the nominal strength specified ISO 6934-4 [25] was referenced. The outer wire of the strand under axial load exhibits not only tensile behavior, but also torsional behavior due to the helical geometry [26]. The nominal strength of the strand takes into account the loss due to torsion of the individual wire, so the strength of the wire should be higher than the nominal strength of the strand in ISO 6934-4 [25]. Therefore, the strength of the individual steel wires was adjusted to match the stress-strain curve of the static load test. The material properties of the anchor head, wedge, and individual wire are described in Table 5.  
Figure 11: Details of anchorage components.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multirow{2}{*}{\begin{tabular}{c} **Elastic Modulus** \\ **(GPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Yield Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Tensile Strength** \\ **(MPa)** \\ \end{tabular} } \\ \hline \multirow{2}{*}{Anchor head} & Outer & \multirow{2}{*}{210} & 1073 & 1412 \\  & Inner & & 925 & 1219 \\ \multirow{2}{*}{\
\begin{tabular}{c} Wedge \\ Wire \\ \end{tabular} } & 210 & 572 & 707 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Tensile Strength** \\ **(MPa)** \\ \end{tabular} } & 195 & 2259 & 2510 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Material properties.