esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DRL Based TDIA Transmission Scheduling Algorithm (DRLSA-IA)
### Reward Mechanism Based on Time-Domain Interference Alignment  
Different from other TDIA-based algorithms that obtain optimal scheduling using the enumeration method, we utilize deep reinforcement learning to achieve the TDIA-based scheduling. In reinforcement learning, the agent judges whether his behavior is good or bad by accepting rewards from the environment, to ensure that the agents can move toward the goal state by choosing behaviors with higher returns. In our algorithm, the goal is to achieve the TDIA-based scheduling. Therefore, we design the TDIA-based reward mechanism.  
Time-domain interference alignment means that the interference is aligned in a time slot that has already collided or interfered. In this way, each node can reserve more free time slots to receive and transmit messages. We give an example to intuitively explain time-domain interference alignment, as shown in Figure 6. At time \(t3\), the propagation delays between node \(A\), node \(B\), node \(C\), and node \(D\) are \(d_{AD}=1,d_{BD}=2\), and \(d_{CD}=1\), respectively. Node \(A\) is expected to send the message \(m_{A}\) to node \(D\). The expected node of the messages \(m_{B}\) and \(m_{C}\) sent by node \(B\) and node \(C\) is not node \(D\). Thus the two messages \(m_{B}\) and \(m_{C}\) are the interference for node \(D\). The message \(m_{B}\) is sent by node \(B\) in time slot \(1\). The message reaches node \(D\) in time slot \(3\) due to the propagation delay. Thus, time slot \(3\) is an interfered time slot for node \(D\). To reduce the number of interfered time slots, message \(m_{C}\) preferably also reaches node \(D\) in time slot \(3\). Therefore, message \(m_{C}\) must be transmitted by node \(C\) in time slot \(1\). In order to successfully receive message \(m_{A}\), message \(m_{A}\) cannot reach node \(D\) in time slot \(3\). Message \(m_{A}\) must be sent by node \(A\) in a time slot outside of time slot \(2\).  
As shown in Equation (10), if the current transmission action \(a_{i}(t)\) follows the TDIA idea, it will be rewarded. In particular, a transmission action \(a_{i}(t)\) will not allow self-transmission, i.e., the source node \(i\) and the destination node \(j\) cannot be the same. The destination node \(j\) successfully receives the message in the receiver time slot. This transmission does not interfere with the ongoing message reception. If the above conditions are satisfied, this transmission satisfies the time-domain interference alignment idea.  
\[R(t)=\begin{cases}0,&\text{if }a_{i}(t)=j\ \&\ i=j\\ 0,&\text{if }a_{i}(t)=j\ \&\ o_{j}^{i}(t+2d_{ij})\
eq 1\\ 0,&\text{if }a_{i}(t)=j\ \&\ s.t.\ o_{k}^{i}(t+2d_{ik})\
eq 0.\\ 1,&\text{otherwise}\end{cases} \tag{10}\]  
The reward of time slot \(t\) is dependent on the observations of time slot \(t+2G\) due to large propagation delays. Considering this issue, we make corresponding improvements in DRLSA-IA and describe DRLSA-IA in detail in Section 4.3.
# Path Planning Strategy for Implementing a Machine Control System in Grader Operations
## 3 Path Generation Algorithm for Graders
### Work Path Generation Algorithm
#### 3.3.1 Extract Edge  
To generate a path, the first step is to understand the shape of the road. Therefore, after selecting the work area from the 3D target model, the coordinates of the outer boundary lines of the road within the work area are extracted and saved in a CSV file. The coordinates were extracted at 2 m intervals and saved in separate CSV files for the left and right boundary lines relative to the direction of the road. The start and end lines are not extracted. For example, as shown in Figure 8, if the direction of the road is from line A to line D-C, the coordinates of lines A-D and B-C are extracted. Only the (x, y) coordinates were extracted, and the z values were processed separately later. Figure 9 shows an example of the extracted coordinates of the outer boundary lines and the centerline.  
Figure 8: Mathematical representation of motor grader work path and repeated rate.  
Figure 7: Work path generation process.
# Characterization of Responsive Plasters for Passive Moisture and Temperature Control
## 4 Conclusions  
In this study, the laboratory-prepared form-stable PCM composites based on diatomic and dodecanol together with Hydropam SAP were used as admixtures for the preparation of a novel plaster with enhanced performance in terms of a passive hygothermal moderation of indoor air. The performed experimental characterization of the material parameters of the plasters with up to 25 wt. % of PCM and 5 wt. % revealed significant changes in the functional performance, thus the synergistic effect can be assigned to the application of SAP and PCM. The most important findings can be described as follows:  
* Application of selected admixtures influenced the basic material properties to a minor extent, namely, a slight increase in the total open porosity should be noted;
* The incorporation of the diatomic-based PCM composite into plaster did not affect the phase change transition in terms of the temperature range. The phase change enthalpy was increased proportionally to the applied dosage;
* Modifications in the hygric properties, and sorption characteristics in particular, point to the beneficial effect of the highly hygroscopic SAP admixture. In fact, the water vapor storage capability was tripled, although the hysteresis was shifted substantially;
* The humidity control performance accessed by MBV increased up to 2.38 g/(%RHm\({}^{2}\)), which corresponds to excellent moisture buffering, according to Nordtest classification, thanks to the synergic effect of both applied admixtures;
* The obtained results show the energy-saving potential of the developed phase change, humidity control plaster due to extended functionality.  
Consequent research should be aimed at the long-term performance of modified plasters, together with semi- or real-scale experiments that allow the calculation of potential energy savings.  
Conceptualization, J.F. and J.K.; methodology, J.F., J.K. and L.P.; validation, J.F., J.K. and M.K.; formal analysis, R.C.; investigation, J.F., L.P., J.P. and J.K.; resources, J.F., R.C. and M.K.; data curation, J.F., J.K. and J.P.; writing--original draft preparation, J.F. and J.K.; writing--review and editing, J.F.; visualization, J.F.; supervision, R.C.; project administration, J.F.; funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Czech Science Foundation, under project No. 18-03997S and by specific university research by the Institute of Technology and Business in Ceske Budejovice, under project No. SVV201802.  
The authors declare no conflict of interest.
rapidity, and low cost. Therefore, many adsorbents such as activated carbon, silica, modified fiber, chitosan, and polymeric resin have been used for wastewater treatment [13; 14; 15; 16; 17].  
Among these available adsorbents, strong-base anion exchangers display better adsorption ability for ASAs owing to their strong electrostatic interaction, but their regeneration is an inherent challenge. Compared with strong-base anion exchange resin, weak-base anion exchangers show better regeneration performance, and some commercial weak-base anion exchangers such as D301 and ND900 have been widely applied for wastewater purification [2; 18]. However, the results were not always satisfactory because of lower selectivity of these commercial resins for ASAs to inorganic anions, especially sulfate, which is often present in ASA waste streams at a high level [19; 20]. Therefore, many efforts have been made by scientists to modify weak-base anion exchangers to improve their adsorption capacity and selectivity, mainly focusing on the optimization of functional groups [11; 12; 21; 22; 23] and the adjustment of the resin matrix structure [24]. It is worth noting that the functional group density of an adsorbent also affects its adsorption selectivity [25; 26]. Moreover, the control of the number of functional groups is easy and less expensive than conventional modification operations. Nevertheless, few studies with regard to the functional group density of a weak-base anion exchanger affecting selective adsorption of ASAs from saline effluent have been reported so far.  
Therefore, the objective of this paper is to prepare a series of weak-base anion exchangers with different functional group densities by controlling the dosage of dimethylamine as a functional reagent on chloromethylal polystyrene (CMPS). Afterwards, the synthesized resins were used for the removal of ASAs from aqueous solution with or without inorganic salts. \(p\)-Toluene sulfonic acid (PTSA) was selected as a representative of ASAs because of its toxicity and low biodegradability, and Na\({}_{2}\)SO\({}_{4}\) as the competitive inorganic salt due to its wide existence in ASA wastewater. Batch adsorption experiments were carried out to evaluate the adsorption capacity and selectivity for PTSA for the sulfate of each resin. Taking into account practical application, column adsorption experiments were implemented using simulated industrial wastewater with PTSA and Na\({}_{2}\)SO\({}_{4}\). The results would improve the understanding on the functional group density effects of weak-base anion exchange resins on removal of ASAs from saline wastewater and provide more choices for the design and optimization of anion exchangers used in a complex water environment.
# Blockchain-Assisted QoS-Aware Routing for Software-Defined Wide Area Network
## 3 System Architecture
### QoS-Aware Route Selection (QRS) Model
#### 3.1.2 Routing Capability Model (RCM)  
Each router needs to forward the traffic in the best-suited path, considering the QoS requirements of the traffic. Each possible routing path (for a particular application's traffic) possesses a specific capability to fulfill the cloud user's preference prerequisites. The various capabilities of each routing path can be considered a total of z parameters. A routing capability, denoted as p\({}_{i}\), can be expressed as  
\[p_{i}=[p_{i1}\ \ \ p_{i2}\ \ \ p_{i3}\ \...\ \ p_{iz}].\]  
The capability of the same router to forward the data in y different paths can be given by P:\[P=\begin{bmatrix}p_{1}\\ p_{2}\\ p_{3}\\ \vdots\\ p_{y}\end{bmatrix}=\begin{bmatrix}p_{11}&p_{12}&p_{13}&\ldots&p_{12}\\ p_{21}&p_{22}&p_{23}&\ldots&p_{2n}\\ p_{31}&p_{32}&p_{33}&\ldots&p_{3n}\\ \vdots&\vdots&\vdots&&\vdots\\ p_{y1}&p_{y2}&p_{y3}&\ldots&p_{yz}\end{bmatrix}\]  
Various capability features encompass a range of values, and Min-Max normalization can be employed to ensure that they adhere to the constraint \(0\leq p_{ij}\leq 1\) (where \(1\leq i\leq y\), \(1\leq j\leq z\), and a higher value represents greater capability).
## 3 Proposed Yielding Protocol
### Yielding Algorithms  
While the yielding protocol is in operation, a vehicle constantly monitors the road conditions using its sensors. If it is at the head of traffic and is running at a slow enough speed when it comes to an unsignalized intersection, it checks if one of the yielding patterns applies. It also checks the turning signals of other vehicles. If a yielding pattern applies, the following yielding request algorithm as shown in Algorithm 2 is executed:  
```
Step 1: The vehicle in question generates a yielding ID. This ID denotes the current yielding process. Step 2: It sends a yielding request message and waits for a response. Step 3: If there is no vehicle in the surroundings, it moves on. If it receives no response even though there are vehicles in the surroundings, it re-sends the message. If it receives an agreement message, it identifies the message sender vehicle and finalizes the agreement. If it receives a time-out message, it sends a cancellation message and restarts the yielding request algorithm all over again. Step 4: If it receives an agreement from the competing responding vehicle and confirms that it can proceed safely, it moves on, sends a thanks message, and terminates the algorithm. Here, the required agreement is an agreement from the competing vehicle described in Section 3.3. It can vary depending on the particular yielding pattern. If it receives a time-out message before it begins to move on, it sends a cancellation message and restarts the yielding request algorithm all over again.
```  
**Algorithm 2**Yielding Request Algorithm  
If the vehicle in question is not involved in any yielding process and receives a request message, it executes the following yielding response algorithm as shown in Algorithm 3.  
```
Step 1: It uses the yielding ID included in the received request message as the yielding ID of the current yielding process. Step 2: It identifies the message sender vehicle. If it cannot identify the sender vehicle, it terminates this algorithm. Step 3: It decides whether to yield and sends either an agreement message or a rejection message accordingly. If it has sent a rejection message or if it has sent an agreement message but there is no conflict situation, it terminates this algorithm. Step 4: It waits for a thanks message. When it has received a thanks message or a cancellation message, it terminates this algorithm. If the waiting time has expired, it sends a time-out message and terminates this algorithm.
```  
**Algorithm 3**Yielding Response Algorithm  
A state transition diagram for these two algorithms is shown in Figure 5.
## 2 Methodology
### Components of the CTCR Methodology
#### 2.1.3 Cost and Duration Projections  
The periodical projection of the cost on completion of the project is based on the EVM technique, because its efficiency has been demonstrated in its application to complex construction projects. The efficiency of the predictions improves as the execution of the project advances, to a point where after approximately 60% of the time of the planned duration has passed, it presents cost forecasts that fit the simulated costs with a 100% probability of occurrence. Under this criterion, EVM is an important tool that enables very efficient cost forecasts after approximately 60% of the period has passed, giving a suitable margin of time for decision making to correct deviations from the project budget [29].  
Equation (7) uses the EVM criterion for cost forecasts that can be applied periodically during the execution of the project.  
\[P_{Costsi}=P_{T}\times\frac{\sum_{j=1}^{n}C_{Rj}}{\sum_{j=1}^{n}A_{Rj}\times C_{ Pj}} \tag{7}\]  
where \(P_{costs\,i}\) is the projected cost at completion of the project, which is calculated in evaluation period \(i\) (in monetary value), \(i\) is the evaluation period, and \(j\) is the number of activities executed up to the evaluation period \(i\). \(P_{T}\) is the total project budget. \(C_{R}\) is the real executed cost of each activity up to period \(i\) (monetary value). \(C_{P}\) is the planned cost of each activity up to period \(i\) (in monetary value). \(A_{R}\) is the measurement of the progress of each activity in the work (in %), and \(n\) is the total number of activities executed up to period \(i\) (units of time).  
The periodical projection of the duration to completion of the project is based on the real and planned physical progress curves over the course of time. The horizontal projection of the real physical progress over the planned physical progress on the time axis is calculated giving a value in units of time that indicate whether execution of the project is ahead of or behind schedule. In Equation (8), the calculation of the projection is presented.  
\[P_{Timei}=T_{A}-T_{P} \tag{8}\]  
where \(P_{time\,i}\) is the projected time up to completion of the project, which is done in evaluation period \(i\) (in units of time), and \(i\) is the evaluation period. \(T_{A}\) is the actual evaluation time in the period \(i\), and \(T_{P}\) is the projected time in the planned physical progress.
# Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach
###### Abstract  
Community Energy Internet (CEI) integrates electric network and thermal network based on combined heat and power (CHP) to improve the economy of energy system in Smart Community. In the CEI, an energy sharing framework for prosumers equipped with photovoltaic-thermal (PVT) system and heat pump (HP) is introduced. Supporting by the PVT and HP, the prosumer has four role attributes with either heat or electricity producer/consumer. A social welfare maximization model is built for the CEI, including PVT-HP prosumers, CHP system, and utility grid. Considering there are multiply participants in the local market of CEI, the social welfare maximization problem is decoupled by using Lagrange multiplier method. Moreover, a consensus-based fully distributed algorithm is designed to solve the problem. Finally, six residential buildings are selected as the case study to validate the effectiveness of the proposed method.  
energy internet; prosumer; energy management; consensus; demand response Article
## 2 Principles and Methods
### The Criteria to Evaluate the Performance of the Optimal Constellations  
To evaluate the performance of the optimal GNSS-R constellations, the spatial and temporal distribution characteristics of the GNSS reflections observed should be taken into consideration. The spatial distribution can be presented by the longitudinal and latitudinal distributions of the GNSS reflections. The temporal distribution is depicted using the mean revisit time, which is defined as the time interval between successive passes in one bin and is calculated by dividing the period with the number of continuous ground tracks on the Earth's surface formed by the reflections [15].
# Using Statistical Shape Models to Optimize TKA Implant Design
## 4 Discussion
Similarly, when looking at the patella, several studies have analyzed the morphology of the patella intraoperatively on several anatomies [10] or CT scans [35]. The bony coverage of various components could also be evaluated using a variety of patients' anatomies [10, 36]. The use of a limited database of statistically relevant bones allowed us to easily analyze the bony coverage and peak restoration of patella components on the input population. One limitation of the models is that they represent the femur, patella, and tibia bones independently from each other. Knowing the relative position of the bones would be valuable information. An alternative would be to compute SSMs for the whole leg. That means building SSMs that comprise the tibia, the patella, and the femur. These SSMs can be valuable to analyze, for example, the relative size of the tibia and femur, or the relative alignment of the patella and the femur. However, the alignment of the bones is strongly influenced by the patient's position during the CT scan. Since the patient is scanned lying on his back, this bone position does not necessarily match the knee in active conditions such as standing upright or being in flexion. For this reason, it would be interesting to work with 4D SSMs that include gait to capture the correct anatomical position and relative bone position during motion. This would also help us to link the models to the expected kinematics and allow to visualize and thereby better understand the kinematic of each patient's joint. Another limitation is the lack of soft-tissue information, which limits the quality of the virtual implantation. Moreover, cartilage is not represented in the models if they are based on CT data. The SSMs presented in this paper were built on 112 and 120 CT datasets. This limits the power of the model to provide insight for specific parts of the population. For example, it is difficult to draw design conclusions for very small patients (such as, for example, the first percentile of the population). Additionally, since these tools are recent and rely on methods that most of the medical community are not familiar with, it can be very useful to complete this validation on individual anatomies.
## 3 Simulation Model
### Second Stage--Routing Path Application
#### 3.2.2 Core Node Simulator  
Core nodes are responsible for processing CP reservations, for switching the bursts from an input to an output port without OEO conversion, and for handling contentions. Signaling in OBS is typically implemented using one out-of-band channel, meaning that CPs are transmitted on a wavelength different from the group of wavelengths used to transmit bursts. This model uses \(\lambda_{0}\) for the transmission of CPs. Several signaling schemes have been proposed for burst scheduling but the Just in Time (JIT) and the Just Enough Time (JET) are two of the most popular protocols using distributed signaling. These are both one-way and source-initiated signaling schemes, which means that the bursts are sent to the core network without waiting for acknowledgments regarding the success or failure of the reservation attempts. Despite being closely related, they differ in the duration of the reservations. The JIT protocol uses immediate reservation with the data channel being reserved as soon as the CP reaches the node, while JET delays the channel reservation until the burst arrival (see Figure 4). This technique, together with the implicit release, makes JET more efficient than JIT regarding bandwidth utilization, resulting in lower blocking rates and low end-to-end delay [5]. For these reasons our strategies are evaluated under a JET-type behavior scheme.  
Together with burst forwarding without leaving the optical domain, core nodes are also responsible for taking contention resolution actions. Contention occurs when multiple bursts from different sources are destined for the same output port at the same time [66]. In addition to the initial path selection strategies adopted on the edge nodes, in handling burst contentions, the core nodes are assumed by default to be equipped with devices having full wavelength conversion capability. This means that, by default, no end-to-end wavelength continuity constraint exists, and that any incoming wavelength can be shifted to any outgoing wavelength, if one is available. As a result, only if there is no wavelength available on the output port the burst will be dropped without any further contention resolution action.