esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Evaluation of Complex Networks Designs for an Energy-Efficient IP/WDM Core Network
## 8 Conclusions  
In this work, we show that in addition to the applied methods and approaches through means of a greedy algorithm and optimization models to ensure efficient resource utilization of a network to minimize its power consumption, network properties such as the clustering coefficient and average path length can play an important role in the further reduction of energy consumption in a network. In this work, we present the main properties and designs of the main complex network models, covering random, small-world, and scale-free networks, as well as the interplay between the topology design and the network's power consumption in the IP/WDM core network. We performed a comprehensive comparison of properties and energy consumption between the small-world, SFN, and random topology designs against the current topology design of the NSF network (base network). A MILP model previously introduced by [1] was implemented for energy assessment of each of the developed topology designs. The results have shown that it is possible to further reduce the power consumption of the NSF network by 28% or 13% if the SWN or SCF network designs are considered. As a result, we find that changing the topology connectivity structure by implementing the scale-free, small-world, or random networks eventually changes the graph physical connectivity and therefore the nodal degree. An important note here is that the geographical location of the nodes, number of nodes, and total number of edges are not changed, and this supports the purpose of this research, which is that power consumption can be reduced if the topology is designed differently.  
For future work, an energy-aware routing greedy algorithm shall be developed to mimic the behavior and verify the results obtained from the energy-minimized MILP model described in Section 6. Moreover, in future work, we will demonstrate that the proposed designs of small-world and scale-free networks with rich connectivity can be implemented to display a high degree of stability and tolerance against impairments and link or node failures.  
Conceptualization, A.A.H. and Z.H.N.; methodology, A.A.H.; software, A.A.H.; validation, A.A.H. and Z.H.N.; formal analysis, A.A.H. and Z.H.N.; investigation, A.A.H.; resources, A.A.H.; data curation, A.A.H. and Z.H.N.; writing--original draft preparation, A.A.H.; writing--review and editing, A.A.H. and Z.H.N.; visualization, A.A.H. and Z.H.N.; supervision, A.A.H.; project administration, A.A.H.; funding acquisition, A.A.H. and Z.H.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Evaluation of Nitrogen and Phosphorus Removal
## Materials and Methods
### Nutrient Mass Load Calculations and Removal Efficiency  
For each sampling location, total nutrient mass loads were determined based on runoff volumes and measured event mean concentration (EMC) (Equation (3)). Volumes were calculated using flow rates recorded each minute by autosampler flow modules and summing the total volume over the sampling interval.  
\[\text{Total Mass}=\text{Q}\ ^{*}\text{EMC}, \tag{3}\]  
where, Q = flow volume (L) and EMC = event mean concentration of analyte (mg L\({}^{-1}\)). Nutrient loads were used to determine the mass load removals and mass removal efficiency using the equation [32]:  
\[\text{RE}\ (\%)=(\text{M}_{\text{i}}-\text{M}_{\text{f}})\times 100/\text{M}_{ \text{i}}, \tag{4}\]  
where, M\({}_{\text{i}}\) = initial mass entering the system (g), M\({}_{\text{f}}\) = final mass leaving the system (g). For positive values nutrients are retained, for negative values there is an export of nutrients. One storm event was removed from the analysis due to flow measurement instrumentation error.
# Energy Consumption Model for Drilling Processes Based on Cutting Force
###### Abstract  
Accurate energy consumption modelling is critical for the improvement of energy efficiency in machining. Existing energy models of machining processes mainly focus on turning or milling, and there are few energy models for drilling. However, since drilling is often applied to roughing and semi-finishing, and the cutting parameters are large, the energy consumption is huge, and it is urgent to study the consumption of energy during the drilling process. In this paper, an energy consumption model for drilling processes was proposed. Idle power, cutting power, and auxiliary power were included in the proposed energy consumption model, using the cutting force to obtain the cutting power during drilling. Further, the relationship between cutting power and auxiliary power was analyzed. Cutting experiments were then carried out which confirmed the correctness of the proposed model. In addition, compared with several existing energy consumption models, the proposed model had better accuracy and applicability. It is expected that the proposed energy consumption model will have applications for the minimization of energy consumption and improvement of energy efficiency but not limited to only drilling energy consumption prediction.  
drilling; cutting power; energy consumption modelling; energy saving +
Footnote †: journal: Applied Sciences
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
## 5 Analysis of the Channel Access Prioritization of Packets
### Conditions for TSN-Compliant Scheduling
#### 5.3.1 Access Stratum Mapping Rules for Time-Sensitive QoS Communications Flows  
Packets belonging to Time Sensitive Communications (TSC) QoS Flows must be able to be scheduled separately from packets of Best Effort traffic or other TSC QoS Flows that are in use in the same UE. Therefore, every TSC QoS Flow needs to be mapped to its own Radio Bearer. In this way, different TSN streams appear to the MAC scheduler as different logical channel instances, i.e., separately servable queues.
## 4 Branch-and-Price-and-Cut Algorithm
### Pricing Subproblem  
To ease the presentation of the pricing subproblem formulation, we define the following additional parameters and variables. Let \(\beta_{i}\), \(\gamma_{i}\), \(\kappa_{t}\), \(\lambda_{t}\), and \(\delta\) be dual values corresponding to constraints (22)-(26), respectively. Parameter \(\epsilon\) is a sufficiently small positive number. We define variable \(w_{i}\) to denote whether node \(i\) is visited or not. Binary variable \(L_{it}\) is 1 if and only if node \(i\) is visited before time \(t\). \(B_{t}\) is an integer variable in the pricing subproblem and shares the same definition as \(B_{t}^{r}\) in the master problem, without the \(r\) index. Using these notations, the pricing subproblem can be stated as follows:  
\[\min\rho_{0}+\sum_{i\in P\cup D\cup N_{0}}\sum_{j\in P\cup D\cup N _{end}}(C_{ij}+C_{j})x_{ij} -\sum_{i\in N_{IF}\cup N_{OF}\cup N_{OE}}w_{i}\beta_{i}-\sum_{i\in N _{IF}^{P}\cup N_{IF}^{D}\cup N_{E}^{P}}w_{i}\gamma_{i}-\sum_{t\in T_{critical}} B_{t}(\kappa_{t}+\lambda_{t})-\delta \tag{28}\] \[s.t.\quad\sum_{j\in P\cup D\cup N_{0}}x_{ji}=\sum_{i\in P\cup D \cup N_{end}}x_{ij}=w_{i},\quad\forall i\in P\cup D\] (29) \[\sum_{i\in P\cup D}x_{N_{0}i}=\sum_{i\in P\cup D}x_{iN_{end}}=1\] (30) \[y_{i}+T_{i}+T_{ij}\leq y_{j}+(1-x_{ij}\overline{M},\quad\forall i \in P\cup D\cup N_{0},j\in P\cup D\cup N_{end}\] (31) \[L_{i}w_{i}\leq y_{i}\leq U_{i}w_{i},\quad\forall i\in P\cup D\] (32) \[z_{iN_{end}}+T_{iN_{end}}x_{iN_{end}}\leq H,\quad\forall i\in P\cup D\] (33) \[y_{i}-t\leq(1-L_{it})\overline{M},\quad\forall i\in N_{IF}^{P} \cup N_{IF}^{D}\cup N_{E}^{P},t\in T_{critical}\] (34) \[y_{i}-t-\epsilon\geq-L_{it}\overline{M},\quad\forall i\in N_{IF }^{P}\cup N_{IF}^{D}\cup N_{E}^{P},t\in T_{critical}\] (35) \[B_{t}=\sum_{i\in N_{IF}^{D}\cup N_{IF}^{D}\cup N_{E}^{P}}L_{it}, \quad\forall t\in T_{critical}\] (36) \[x_{ij},w_{i},L_{it}\in\{0,1\},z_{ij},y_{i},B_{t}\geq 0,\quad \forall i,j\in\{P\cup D\cup N_{0}\cup N_{end}\},t\in T_{critical} \tag{37}\]  
The goal is to find the optimal route with the least reduced cost, as expressed in (28). Constraints (29) are flow constraints. Constraints (30) ensure that the route must begin and terminate at the depot. Constraints (31) calculate the starting times of nodes, whereas constraint (32) ensures the adherence to time windows. Constraints (33) are the maximal duration constraints. Constraints (34) and (35) identify which nodes are visited before time \(t\). Then, the accumulated increment in the depot's stock of empty containers before critical time \(t\) is calculated by constraints (36).  
This pricing subproblem can be considered a variant of the well-known elementary shortest path problem with resource constraints (ESPPRC), which is NP-hard in a strong sense. To exactly solve it, we propose a label setting algorithm.
## 6 Recent Developments and Innovative Approaches
### Smart Photocatalysts and Responsive Materials  
Emerging research also focuses on stimuli-responsive photocatalysts, which can adapt their activity in response to environmental conditions such as light intensity, temperature, or pollutant concentration. Examples include the following:  
* Self-cleaning surfaces to prevent fouling.
* Magnetically recoverable catalysts that allow rapid separation after use.
* Photoelectrochemical (PEC) systems, which couple light and bias voltage to enhance electron-hole separation and pollutant removal efficiency.  
[PERSON] (2023) explored the development of self-cleaning smart photocatalytic coatings designed to prevent fouling in water treatment applications [169]. These coatings utilize photocatalytic materials that, upon exposure to light, degrade organic contaminants on surfaces, thereby maintaining cleanliness and operational efficiency. The research highlights the potential of these coatings to reduce maintenance requirements and enhance the longevity of water treatment systems. [PERSON] et al. [170] developed magnetically recyclable nanophotocatalysts (MRNPCs) for the efficient removal of recalcitrant organic pollutants from water. These MRNPCs can be easily separated from treated water using external magnetic fields, facilitating catalyst recovery and reuse. The review covers various synthesis methods, applications, and challenges associated with MRNPCs, emphasizing their role in enhancing the practicality of photocatalytic water treatment processes. A study performed by [PERSON] et al. [171] investigated the application of photoelectrocatalytic (PEC) systems for the treatment of municipal wastewater containing emergent chemical and biological pollutants resistant to conventional treatments. The research focuses on the removal of these pollutants using PEC approaches, highlighting the potential of coupling light with an applied bias voltage to enhance electron-hole separation and improve pollutant degradation efficiency.  
Figure 13d showcases innovative smart photocatalytic technologies designed to adapt dynamically to environmental conditions during water treatment. It highlights three key approaches: self-cleaning surfaces that minimize fouling and maintain efficiency, magnetically recoverable catalysts that enable easy separation and reuse, and photoelectrochemical (PEC) systems that enhance pollutant degradation by coupling light with an applied bias voltage. These responsive materials represent a significant step toward more efficient, sustainable, and operationally flexible photocatalytic systems.
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
###### Abstract
Developing a cost-effective user-centric web application for automated Sentinel-2 data processing designed to support agronomists, farmers, and other agricultural professionals by enabling systematic crop growth monitoring.
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 2 Network Traffic Modelling
### Network-Level Transmission Intensity
#### 2.2.1 Transmitter Volatility, \(tsv\)  
It is defined to capture the dynamic behavior of the devices or transmitters. The number of devices in the network is constant, but not the number of active transmitters in each time frame. Previous studies also showed that the number of transmissions by each device in various time frames during each work cycle differs [3; 10].  
Therefore, the throughput contribution of each transmitter based on the number of transmissions, or generated packets, in each time frame, is one element for quantifying the network dynamically. Since the scope is on the network level, \(tsv\) is defined as a relative change of transmitter's behavior over time in terms of the number of transmissions. Consequently, the dynamic of throughput within each time frame can be studied relative to \(tsv\). That is, studying the relation between throughput and transmitter changes where the varying amount of data transmitted by the same device, due to the number and size of the transmitted packets, can be accounted for.
## 4 Experimental Results
### Experiment 2: Human Interaction  
The second experiment aims to demonstrate that the gain tuning rules provided in Theorem 1 are applicable to real teleoperation scenarios involving time-varying delays and a human operator controlling either robot. For this purpose, the PID controllers used in the previous experiment were retained, while the PI control law emulating human dynamics was removed.  
In this setup, the human operator first moves the local end-effector, while the remote manipulator tracks the delayed position of the local robot.  
At approximately \(t\approx 43\,\mathrm{s}\), the operator brings the system to a desired position. The results are depicted in Figure 5.  
As shown in (24), both the human operator and the integral term of the PID controller prevent the local end-effector from falling due to gravity. When the operator releases the end-effector, it briefly descends before the system reaches consensus, and neither end-effector falls to the ground. At \(t\approx 62\,\mathrm{s}\), the operator moves the remote end-effector until it is brought to a stable position and released at approximately \(t\approx 89\,\mathrm{s}\). As in the first instance, the remote end-effector initially descends slightly but stabilizes shortly thereafter, compensating for gravitational effects.  
A video of Experiments 2 and 5 can be viewed at [[https://www.youtube.com/watch?v=GBkOU9](https://www.youtube.com/watch?v=GBkOU9) fD498]([https://www.youtube.com/watch?v=GBkOU9](https://www.youtube.com/watch?v=GBkOU9) fD498) (accessed on 21 October 2024).  
Table 3 provides the RMSE values for this experiment, which can be compared with those from Table 2. The results indicate that human interaction yields better performance. This improvement is expected, as the operator moves the end-effectors more slowly than the programmed trajectory used to emulate human dynamics.  
These RMSE values reflect a marked improvement when the human operator interacts with the system compared to the emulated dynamics. For Joint 1, the RMSE is reduced by over 60%, indicating the human operator's ability to adapt to environmental variations and system delays. The results confirm that the PID tuning methodology is robust and effective in real-world teleoperation scenarios.  
Figure 5: Experiment 2. Position tracking with human interaction: \(\mathbf{q}_{1}(t)\) (—), \(\mathbf{q}_{t}(t)\) (- - -).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Joint** & **RMSE** [\({}^{\circ}\)] \\ \hline
1 & 2.7470 \\ \hline
2 & 2.1181 \\ \hline
3 & 1.5224 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment 2: position tracking RMSE with human interaction.
## 3 Narrative Review
### Differential Diagnosis of Orbital Myositis Through Imaging
While not definitive for diagnosis, the involvement of peri-muscular tendons is a significant indicator of non-thyroid inflammatory conditions. However, features such as unilateral involvement, fat infiltration, and early superior oblique muscle involvement are more suggestive of pseudotumor than thyroid eye disease [19]. When distinguishing between inflammation and lymphoma proves difficult, diffusion-weighted imaging (DWI) can offer additional insight [4]. DWI helps differentiate between malignant and benign orbital lesions by assessing the water molecule movement in tissues. Analyzing apparent diffusion coefficient (ADC) maps and calculating ADC values enables radiologists to identify lower ADC values in malignant tumors, indicating higher cellularity, while benign tumors typically show higher ADC values with less restricted diffusion [28]. A new imaging technique, MRI-DCM (Dynamic Contrast-Enhanced Magnetic Resonance Imaging), is being developed to aid in differential diagnosis. MRI-DCM offers more detailed insights into orbital lesions compared to conventional CT and MRI scans. It uniquely distinguishes between adjacent tissues and those that are interconnected. For instance, in Patient A, MRI-DCM showed that the mass was directly linked to the medial rectus muscle, suggesting that the muscle was likely the source of the problem and supporting a diagnosis of myositis. This capability is clinically valuable as it helps determine the origin of a retrobulbar lesion without necessitating a potentially risky biopsy at the orbit's apex [29]. The characteristics of the main differential diagnoses are summarized in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **Imaging Characteristics** & **Key Differentiators** \\ \hline Thyroid Orbitopathy & Bilateral inflammation, sparing of & Bilateral involvement, absence of \\  & myotendinous junction & myotendinous junction inflammation. \\ \hline IgG4-Related Disease (IgG4-RD) & Bilateral lacrimal gland and extraocular & \\  & muscle (EOM) involvement, inferior rectus & Serum IgG4 levels are often elevated. \\  & often affected & \\ \hline Orbital Cellulitis & Associated with fever, leukocytosis, and & Contrast-enhanced CT * shows abscess \\  & infection history & formation requiring drainage. \\ \hline Orbital Lymphoma & Focal masses with heightened signal & Tumor appears as mass, usually with a \\  & intensity in EOMs & heterogeneous signal on CT/MRI *. \\ \hline Carotid-Caverous Fistula (CCF) & Enlarged superior ophthalmic vein (SOV), & Symptoms of venous congestion; \\  & retrograde blood flow & transcranial Doppler and angiography \\  & used for diagnosis. \\ \hline \hline \end{tabular}  
* MR: Magnetic Resonance Imaging; CT: computed tomography.  
\end{table} 
Table 3: Differential diagnosis of orbital myositis.
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 5 Influence of High and Low Tide Levels on Typhoon Waves  
### Translating Tide Level  
According to the above verification results, the tidal level results obtained through this method are consistent with the actual situation, indicating that it is feasible to input tidal level results into the SWAN model.  
The impact of typhoon waves on hydraulic structures is more severe during high tide than at other times. In this section, the high tide level and low tide level obtained from the tidal level conversion at the time of the typhoon's landing are used in the typhoon simulation. Additionally, the obtained simulation results are compared to analyze the influence of high and low tide levels on typhoon waves.  
Typhoon Maria is a typical example in which the landing time of the typhoon coincides with the arrival of the high tide level. The simulation under normal conditions represents the simulation of the typhoon waves when the high tide level arrives. By simulating the tide level when Typhoon Maria arrives, the tide level simulation results at each time point (24 h) in the study area can be obtained. These results are similar to the simulation results of the previous Typhoon Mekkhala and are consistent with the actual situation. Since the change in the typhoon wind field on the time axis is affected by the background wind field, it is necessary to eliminate the effect of the background wind field to explore the influence of different tidal levels on the typhoon. To facilitate the comparison of the influence of different tidal levels on the effective typhoon wave height, the method of translating the tide level instead of the typhoon process is adopted. The tide level in the calculation area is shifted back 6 h so that when Typhoon Maria makes landfall, the tide level at the landing site is at the lowest tide time to explore the effect of the tide level.  
Figure 8 shows the tidal height comparison of each verification point before and after the tidal level shift of Typhoon Maria. Upon comparing the results before and after the shift, it can be observed that when the typhoon arrives at the Beishuang buoy during the highest tide level and the shifted lowest time, a notable difference of 5 m is observed between the high tide and the low tide levels. At the No. 4 buoy, the difference is 4.1 m.  
Figure 7: Comparison of calculated and measured wave heights at each verification point of Typhoon Mekkhala. (**a**) Gulei buoy. (**b**) No. 1 buoy. (**c**) No. 2 buoy. (**d**) No. 2 Strait buoy.  
Figure 9 shows the tide level simulation results of the two verification buoys, Beishuang and No. 4, during the landfall of Typhoon Maria. It can be seen from the figure that the tidal heights at both buoys follow a 12-h cycle. The simulation results of the tide level are consistent with the semidiurnal tide characteristics in the Taiwan Strait.
## 2 Second Messengers in Cyanobacteria
### Cyclic Adenosine 3',5'-Monophosphate, cAMP  
Cyclic AMP is synthesized by adenylate cyclase (AC) (class III nucleotidyl cyclases family, Pfam: Pfam00211) proteins using ATP as a substrate and hydrolyzed to AMP by cAMP-specific phosphodiesterase (PDE) (Figure 4). Cyclic AMP is a widespread molecule in cyanobacteria. In an analysis of cyanobacterial genomes, only the picocyanobacteria _Prochlorococcus_ and _Synechococcus_ were reported to lack cAMP receptors, which can bind cAMP and serve as transcriptional regulators that impact diverse responses [17]. Species lacking cAMP receptors likely lost them during the course of evolution to adapt to new environments [17]. In an assessment of 83 finished cyanobacterial genomes present in the IMG database, AC genes homologous to the class III AC family (Pfam00211) were also found to be widespread (Table 1; Table S3), except in some strains that include mostly _Prochlorococcus_ and _Synechococcus_ strains (Table S4).  
Figure 4: External factors controlling intracellular levels of cAMP and phenotypes or processes that are controlled by cAMP in cyanobacteria. Cyclic AMP is synthesized from ATP by adenylate cyclases (AC) and degraded to AMP by cAMP-specific phosphodiesterase. Green arrow indicates that increased synthesis of cAMP leads to noted phenotypes. +, indicates process promoted by increased cAMP levels.