esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Scenario with More Servers and Fewer Fog Nodes
#### 6.3.2 Latency Results  
**Low average latency:** The system achieves an average latency of 45.95 ms, which is a fast response time considering the number of clients (1500) and the server load. This means that most users experience quick and efficient access to the system, which is crucial in an environment with many simultaneous clients.  
**Optimal performance:** The minimum latency of 0.835 ms is an excellent indicator that the system can provide near-instantaneous responses under optimal conditions. This ensures high efficiency during low loads, offering a smooth experience for users accessing the system during low demand periods.  
**Controlled latency variability:** The standard deviation of latency is 25.98 ms, indicating moderate variability in response times. This means that while some users may  
Figure 7: Server and fog nodes load behavior in relation to time in the scenario with more servers and fewer fog nodes.  
experience different response times, most will not notice significant changes in service quality. This consistency is key to providing a stable and predictable user experience.  
System Resilience and Robustness  
**Maintaining service under high load:** Despite the increase in clients (1500) and the reduction in fog nodes, the system demonstrates great resilience. The combination of servers and fog nodes has been sufficient to keep the service active and avoid critical overloads that affect system availability.  
**Effective load balancing:** The fact that load distribution does not collapse during high-demand periods indicates that the load balancer is functioning correctly, efficiently distributing requests among available servers. This ensures high system availability and a continuous user experience.
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 5 Security Analysis
In addition, controlling a majority of the network resources (51% attack) is computationally impractical due to the decentralized nature of blockchain networks and the significant resources required. Any attempt to create an alternate version of the blockchain would be visible to all honest participants, who would reject the tampered chain in favor of the valid chain. Thus, blockchain ensures the immutability and transparency of data records.
## 5 Industrial Applications of CFD Simulations in Food Technology
### Case Studies and Examples of CFD Implementation in Food Companies  
Several case studies demonstrate the practical applications of CFD in the food industry. One notable example is the optimization of air distribution in commercial ovens, where CFD was used to simulate airflow and thermal profiles to ensure a uniform heat distribution across various baking trays. This simulation helped reduce energy consumption by 15% while improving the consistency of baked goods [89; 149]. In another example, CFD simulations were applied in the development of a high-efficiency freeze-drying process. By simulating the airflow, temperature, and pressure variations in the freeze dryer, the company was able to minimize energy use and maximize the moisture removal efficiency. This not only reduced operational costs but also improved the preservation of sensitive nutrients and flavours in the final product [140; 150]. Additionally, CFD has been successfully applied to simulate heat transfer in continuous sterilization systems, helping food manufacturers optimize processing times and temperature profiles to enhance product shelf life while maintaining nutritional quality [14].
# A Hybrid Exact-Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity
## 2 Problem Description
### Terminology  
In line with the definitions of terms in [6], we use \(S_{i}\), \(p_{i}\), \(d_{i}\), and \(C_{i}\) for the start time, duration, due time, and completion time, respectively, of a job \(i\) in a given schedule. Then, \(T_{i}\) is the tardiness of job \(i\) which is \(max\{0,C_{i}-d_{i}\}\).  
A concrete example involving 12 jobs and a capacity line that reaches a maximum of four units is presented below. This example is the one used as Example 1 in [5]. Table 1 summarizes information related to it alongside the values associated with an optimal schedule for this problem instance, achieving an optimal cost of 20. The schedule corresponding to the table's third line is presented graphically in Figure 1.  
Figure 1: A graphical representation of the schedule in the last line of Table 1.
## 3 Results and Discussion
### Energy Characteristics
While the results shown in Table 3 definitely show differences in the generation thresholds of lasers with different cavity configurations, the absolute values of \(\rm I_{TH}\) must be viewed with caution, because there are many factors that introduce uncertainty into the measurement results, such as cavity misalignment and the subsequent variation in the Stokes mode structure, the influence of residual reflection from KGW crystal faces, changing temporal shapes of Stokes pulses, etc. Bearing in mind the results obtained with stable spherical cavities, we suppose that the appearance of even a weak positive thermal lens in a Raman crystal can lead to a reduction in the conversion efficiency of high-energy lasers. On the other hand, the influence of a thermal lens in the KGW crystal for lasers with a pump spot size of \(\sim\)0.2 mm was investigated in [10; 14]. It was shown that the appearance of a thermal negative lens in KGW causes a decrease in the generated Stokes power, which can be compensated for by using a positive lens in the cavity. The apparent discrepancy between our supposition and the results of [14] can be easily explained:  
* The appearance of a negative lens in low-energy lasers, when the spot size is \(\sim\)0.2 mm, leads to the distortion of Stokes generation in the \(\rm TEM_{00}\) mode, which results in an increase in diffraction losses and worsening of the overlap with the pump beam;
* In high-energy lasers, the efficiency has a low sensitivity to the increase in diffraction losses due to a high gain. A negative lens (or the negative curvature of laser mirrors) does not reduce the uniformity of the Stokes transversal distribution or the overlap with the pump beam (see next paragraph). From this point of view, KGW crystals have an advantage in comparison with most other Raman crystals [15] due to the negative temperature coefficient of the refraction index. Preliminary experiments showed that the application of stable spherical cavities in Raman lasers also leads to worsening laser angular characteristics and lower beam pattern uniformity and reproducibility. Due to these reasons, we suppose that the application of spherical cavities in high-energy Raman lasers has no practical utility. This is why we only show the results obtained for lasers with flat and unstable cavities below.
# Effect of Aging on Class G High Sulfate-Resistant Oil Well Cement Under High Relative Air Humidity
## 4 Conclusions and Observations  
### Conclusions  
In existing studies on the effects of aging on cement, most scholars have focused on construction cement. The impact of aging on oil well cement has not been thoroughly investigated, yet this issue becomes prominent in practical applications. Therefore, to determine the allowable time to avoid significant performance changes due to aging, this paper studies the changes in compressive strength, thickening time, and rheological properties of cement with aging time, and conducts a mechanism analysis. The specific research conclusions are as follows:  
1. In an aging environment with 90% RH relative air humidity, the cement sorbed H2O and CO2 from the air, led to a reduction in the specific surface area and surface energy of the powder. The contents of the clinker phases C3S, C3A, and gypsum decreased, whereas the quantities of the hydration products AFt and CH increased. This process hindered the reaction between the cement and the water, leading to a decrease in both the hydration rate and heat release.
2. The compressive strength, thickening time, and rheological properties of aged cement were affected. As the hydration of the cement slowed, the surface energy of the cement powder decreased, which negatively impacted the compressive strength. The reduction in the gypsum content was insufficient to adequately limit the hydration of C3A, leading to a shortened thickening time. Additionally, the increase in hydration products increased the friction between the particles, further degrading the rheological properties.
3. For Class G oil well cement aged in an environment with 90% relative humidity and at a temperature of 25 \({}^{\circ}\)C, the permissible time to avoid significant changes in cement properties should be less than 7 d, with a maximum aging period not exceeding 14 d. Furthermore, at a usage temperature of 80 \({}^{\circ}\)C, the performance of the cement paste aged for 28 d was found to be inadequate to meet the necessary operation requirements.
# Mitigation of Nonlinear Distortions for a 100 Gb/s Radio-Over-Fiber-Based WDM Network
## 1 Introduction
### Related Work  
To develop the communication network resistance against the advanced NLIs and LDs, the use of equalizers in RoF technology is considered as a general solution but the industry demands for an ultra-reliable network from the 5G mobile systems, if RoF transport is utilized as a fronthaul network. This area has huge potential for research due to several advantages of RoF transport, in order to design the optimized structure and be able to support current demands of capacity with overcoming NLIs and LDs. Some of the recent work in this domain is discussed as follow.  
RoF transport has been explored recently for the transmission of millimeter-wave signals for 5G system [11]. In [12], authors have studied an adaptive RoF system for the next generation C-RAN, with the goal to compensate NLIs. A 2 \(\times\) 2 multiple input multiple output (MIMO) with OFDM waveform is explored in [13] for the intermixing noise of signals after the RoF transport. A WDM-RoF network is analyzed in [14], keeping 0.4 nm channel spacing and fixed input power while focusing transmission of data streams over a range of optical fiber lengths. In [15], RoF transmission system isdiscussed by authors with multimode operation to transport multi wavelength optical comb signals. The inter-modal noise for such systems is the same for RoF transmission as its digital counterpart for the 4 QAM-OFDM-based signal transmission. In [16], authors have analyzed D-band millimeter wave generation and propagation scheme employing cascaded intensity modulator and in-pulse quadrature modulators (QMs). These two modulators operate on optical carrier by suppressing the main mode to generate two pairs of dual single-sideband signals. A multi service digital RoF system for a neutral host fronthaul link is presented in [17] based on the multiband multiplexing. In [18], authors have demonstrated an RoF transmission system in S-band using a 2 \(\times\) 2 multiple input multiple output (MIMO)-OFDM with orthogonal and bi orthogonal wavelets. Optimization of signal processing schemes for downlink and uplink of a C-RAN architecture is studied in [19], which serves wireless link users with nonlinear energy harvesting (EH). Similarly, in [20], the authors have described a full duplex RoF system in terms of integrated silicon ring modulator using coherent dual wavelength closer source. However, the drawbacks like side-lobes with high power levels, low spectral efficiency, high cost and complex structure make space for alternative options in the WDM-RoF system. Other recently reported RoF demonstrations include millimeter-wave transmission supported ultrahigh speed transmission [21] by making use of the direct photonic upconversion to generate millimeter-waves at the remote antenna unit [22]. A comparison of RoF transport has been made in [23] with free-space optics setup for QAM modulated data transmission using SMF of the order of 50 km [24]. Thus, this paper proposes an optimized design of the WDM-RoF system with advance modulation formats to overcome the NLIs and PAPR issues.
# The Use of an Exact Algorithm within a Tabu Search Maximum Clique Algorithm
## 3 Minor Improvements in HTS2
If the neighborhood to which exact search is applied has at least _Nontabu max_ vertices, a protection mechanism is invoked (selecting a random vertex in the neighborhood) to avoid application of the exact search to an excessively large neighborhood. Typical values of 30 or 60 were used. In HTS2, a parameter \(\lambda_{2}\) from the main heuristic, to be outlined in the next section, is used instead of _Nontabu max_. A final change is to replace the condition that the tabu search optimization runs for _Tstime_ seconds unless the size of the clique increases, by the requirement that it runs for 10,000 iterations, unless the size of the clique increases. This avoids the need for the parameter _Tstime_ and proved satisfactory for all instances. Pseudocode for the revised tabu search algorithm is presented in Algorithm 5. ```
The algorithm is invoked for a graph \(G=(V,E)\) with \(C:=\)[random vertex in \(V\)]. Parameters supplied are the threshold \(\theta\) from Algorithm 2, and two further thresholds \(\lambda_{1}\), \(\lambda_{2}\) for exact search. The sequence of values \(S=[s_{i}]\) is also supplied. Following the call to the main heuristic, \(C\) contains the best clique found by the main heuristic at iteration \(k\). Main_heuristic()_
```
1:\(C:=\)[random vertex in \(V\)];
2:\(Exactstored:=False\); \(adjchoices:=s_{1\pm\mathrm{mod}}\;|s|\);
3:while\(|N(C)|>0\)and\(|N(C)|\geq\theta-|C|\)do
4:if\(|N(C)|<\lambda_{2}\)then
5:\(Best:=\emptyset\); \(External\_lower\_bound:=\theta-|C|\);
6:\(Exact(\emptyset,N(C))\);
7:\(C:=C\cup Best\);
8:else
9:if\(|N(C)|<\lambda_{1}\)and\(Exactored=False\)then
10:\(\mathrm{C_{stored}}:=C\); \(N(C)_{\mathrm{stored}}:=N(C)\);
11:\(Exactored:=True\);
12:endif
13:if\(|N(C)|\leq adjchoices\)then
14:\(U:=N(C)\);
15:else
16:\(U:=\{v_{j_{1}},v_{j_{2}},\ldots,v_{j_{d}choices}|v_{j_{i}}\in N(C)\text{ selected randomly}\}\);
17:endif
18: Choose \(u\in U\) such that \(|N(C\cup\{u\})|\) is maximal;
19:\(C:=C\cup\{u\}\);
20:endif
21:endwhile
22:if\(Exact restored=True\)and\(|C|\geq\theta\)then
23:\(Best:=\emptyset\); \(External\_lower\_bound:=\theta-|C_{\mathrm{stored}}|\);
24:\(Exact(\emptyset,N(C)_{\mathrm{stored}})\);
25:\(C_{\mathrm{temp}}:=C_{\mathrm{stored}}\cup Best\);
26:if\(|C_{\mathrm{temp}}|>|C|\)then
27:\(C:=C_{\mathrm{temp}}\);
28:endif
29:endif
30:return(\(C\)); ```
**Algorithm 3**Main heuristic at iteration \(k\). ```
1:\(\text{i\_Optimize}(C)\)
2:\(Current\_best:=C\);
3:Recursive_i_opt(C)
4:for all \(S\subset C\) with \(|S|=i\)do
5:if\(|N(C\setminus S)|>i\)then
6:\(Best:=\emptyset\); \(External\_lower\_bound:=i+1\);
7: Execat(\(\emptyset,N(C\setminus S)\));
8:if\(|Best|>i\)then
8:\(Current\_opt:=Best\cup(C\setminus S)\)
9:if\(|Current\_opt|>|Current\_best|\)then
10:\(Current\_best:=Current\_opt\);
11: Recursive_i_opt(\(Current\_opt\));
12:endif
13:endif
14:endif
15:endfor
16:return(\(Current\_best\)); ```
**Algorithm 4**\(i\_\)Optimize.
## 5 Policy-based Adaptive QoS Management: CNQF Runtime Validation
### Initial Configuration Policies  
For a QoS domain under CNQF administration, the initial configuration policies are entered into the GUI policy tool. These policies govern the configuration of the PEPs (routers) in the transport network with specific QoS provisioning capabilities. Current CNQF implementation supports DiffServ QoS mechanisms; initial/adaptive runtime management policies that utilize DiffServ QoS mechanisms can therefore be specified by an administrator. Extension of CNQF prototype to support others QoS technologies is such as MPLS can be achieved without rebuilding the PAL, PDL and most of the MCAL elements. The only requirement is the RC extension (using the wrapper functionality of the _ResConImpl_ class); this is one major advantage of the CNQF architecture.  
The CNQF GUI has provision for the DiffServ Code Points (DSCP) codes necessary for marking packets for service differentiation (in the edge routers) to be specified along with the selection of queuing discipline for implementing the per-hop-behaviors (PHB) for the QoS domain. Once these are entered by the admin, the relevant RB instance is called to process the information and will in turn invoke the associated RC instances to configure the routers with the selected configuration parameters. Once the PEPs have been configured, the RC instance returns a status message displayed in the CNQF GUI's status pane. Next, the policies relating to how specific flows, flow types or group of flows identified by _identifier_ are to be handled (initial static QoS provisioning) are entered in the GUI. The format used is:  
_If (flow\(=\)\(=\)identifier) then (policy action)_  
Here, the _identifier_ can be a flow's IP address or a range of IP addresses (for group of flows) or other unique flow identifiers. This will normally be used for configuring flows for specific preferential QoS treatment (e.g., for Expedited Forwarding PHB in DiffServ domain). If not configured, the default best effort treatment is applied to a flow. Each of these policy rules adds an entry to a look-up table specifying which flows have which DSCP and therefore a corresponding PHB. Through the use of the look-up table each flow's current QoS configuration state is maintained. The RC employs the look-up table to add or modify Linux TC filter parameters within a file which the kernel employs for configuration of the router to implement the required DiffServ PHB.
## 3 Proposal
### Framework Architecture and Behavior  
The framework components exchange messages through TCP/IP sockets. The MEE is used to model a Python application, execute the model, and reconfigure the application. The MEE is used to model the structure of the application as a PN. In the next example, we introduce a transition enriched with a statement and a guard.  
**Example 3**.: _Transition \(T_{5}\) from the grading example depicted in Figure 2 is enriched with the statement_  
_Passed_Frame_show_decision(\"Passed!\") and the guard decision. The statement calls the function_  
_show_decision(\"Passed!\") from the Python file_ _Passed_Frame_. The guard ensures that this statement can only be executed when the variable _decision_ is _True_._  
The MEE sends the transition guards to the PEE to be reevaluated using the Python instruction _eval_ any time the marking of the PN changes. The PEE answers the MEE with the set of guards satisfied, and the MEE sends the transition statements to the PEE when a transition is fired, and then awaits to receive the execution outcome.  
The MEE supports both automatic and manual firing of transitions. When firing a transition, it follows the protocol depicted in Figure 7. This protocol is modeled as a PN. The MEE sends the corresponding statement to the PEE when firing a transition. It waits for a response that can be either an \"EXECUTED\" or an \"EXCEPTION\" message. In the former case, the MEE updates the marking. In the latter case, it restores the marking to the original state and opens a popup dialog showing the exception thrown. The color of transitions can be green, red, or orange to denote the states of enabled, disabled, or running, respectively.  
The PEE is a Python program implementing server features. It receives guards and instructions, and returns the outcome of their evaluations and executions, respectively, to  
Figure 7: The protocol followed by MEE when firing a transition.  
the MEE. For instance, when the PEE receives an instruction, it executes the instruction. If the instruction is executed without an exception, the PEE sends an \"EXECTTED\" message. Otherwise, it sends an \"EXCEPTION\" message concatenated with the exception content.  
Listing 2 shows the Python function to be executed when receiving an instruction. It uses the _exec_ command inside a _try except_ block to execute the received instruction.  
```
def execute(instruction): try:  exec(instruction) reply(\"EXECTTED\") except Exception as e: reply(\"EXCEPTION:\" + str(e))
```  
In contrast, in the evaluation using the instruction _eval_ when an exception is raised, we assume that the evaluation has not succeeded and the guard is not fulfilled. In this case, we consider that the transition is disabled. We follow this strategy because expressions may eventually be evaluated as true, once, for example, the variable used in the expression is defined or used (dynamic typing).
## 2 Simulation Methodology
### Segmental Properties of Human Body in Finite Element Environment
The general expression for a damped system is  
\[[C]=\alpha[M]+\beta[K]+\sum_{j=1}^{N_{mat}}\beta_{j}[K_{j}]+\beta_{c}[K]+[C_{ \zeta}]+\sum_{k=1}^{N_{de}}[C_{k}] \tag{5}\]  
\(\alpha\) = mass matrix multiplier constant;  
\(\beta\) = stiffness matrix multiplier constant;  
\(\beta_{j}\) = material dependent stiffness matrix multiplier constant;  
\(\beta_{c}\) = variable stiffness matrix multiplier constant;  
\(C_{\zeta}\) = frequency dependent damping matrix;  
\(C_{k}\) = element damping matrix;  
\(j\) = material (1,...,\(N_{mat}\));  
\(k\) = element (1,...,\(N_{ele}\)). This analysis work considered the linear perturbation step followed by the modal dynamics step in finite element. The main focus was paid to \(C_{\zeta}\), which can be defined as  
\[\{u_{r}\}^{T}[C_{\zeta}]\{u_{r}\}=4\pi f_{r}\zeta_{r} \tag{6}\]  
\(u_{r}=\text{r}^{\text{th}}\) mode shape;  
\(f_{r}=\text{frequency at r}^{\text{th}}\) mode shape;  
\(\zeta_{r}=\text{damping ratio for r}^{\text{th}}\) mode shape. \[\zeta_{r}=\zeta+\zeta_{mr} \tag{7}\]  
\(\zeta=\text{constant damping ratio}\);  
\(\zeta_{mr}=\text{modal damping ratio for r}^{\text{th}}\) mode shape. As the main aim of this research work was to establish a simulation methodology to find out the frequency data at different locations of the human body, the damping model of the human portions were simplified by considering only damping coefficient and constant damping ratio. The damping coefficients for lower torso, arms, upper torso, and hands was considered as 2376.4, 145.8, 1797.7, and 1797.7 Ns/m, respectively, while studying the vertical and fore-aft vibration inside a 75 kg human body [36]. Vibration analysis on a human body [37] of 90 kg considered damping coefficients for head including neck, chest including upper torso, lower torso, and pelvis including thigh as 620, 8550, 5685, and 3564 Ns/m, respectively. Investigation on a human of 80 kg in seated condition [38] took the damping coefficients for head, back, torso, thorax, and abdomen as 3575.506, 3575.506, 291.878, and 291.878 Ns/m, respectively. On the basis of the closest match to the human mass used for this research work, damping coefficients for head, torso, upper arms, lower arms, hands, thighs, legs, and feet were considered as 620, 1797.7, 145.8, 145.8, 1797.7, 2376.4, 2376.4, and 2376.4 Ns/m, respectively, for this simulation work. Constant damping ratio was considered as 0.2 on the basis of the work done on human joints [39,40].