esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analyzing Zoom's E2 EE Capabilities  
### Zoom's E2 EE Capabilities  
Zoom plans to provide E2 EE security by subdividing it into four phases. According to Zoom's E2 EE whitepaper [2], E2 EE security features have been specified in up to two phases. Zoom's E2 EE security plan is as follows:  
* Phase 1: Client Key Management
* Phase 2: Identity Check
* Phase 3: Transparency Tree
* Phase 4: Real-Time Security  
Phase 1 of Zoom's E2 EE involves Client Key Management. Client Key Management is a system designed to securely distribute the meeting key (\(MK\)) among the entities (clients) participating in a meeting, enabling them to encrypt and decrypt meeting data. E2 EE Phase 2 is an Identity Check. In Phase 1, the identity status of each client is based on the displayed screen name during the meeting, whereas in Phase 2, the client's identity status is traced using Sigchain. By reviewing the record of the client's identity status during the meeting, it becomes possible to prevent attacks from suspicious devices.  
Zoom's E2 EE phases 3 and 4 have not been applied and are planned for application in the future. Phase 3 introduces the Transparency Tree, which expands the authentication assurance to enable all clients in the conference to verify devices and keys. Phase 4 involves Real-Time Security, which strengthens meeting security through additional signatures and enables client Sigchain verification without passing through the Zoom server.  
In Section 3, we analyze Client Key Management, which is the first step in E2 EE security. The details and plans for the remaining phases of Zoom's E2 EE security are included in Zoom's E2 EE whitepaper [2].
## 2 Materials and Methods
### Methods
#### 2.2.3 Feature Extraction from POIs  
The categorization of functional areas is not only related to topography and building distribution but is also intimately connected to the array of human economic activities occurring within them [49; 50; 51]. We utilized POI data to extract socioeconomic characteristics of functional areas, enriching the information available for each grid. First, we extracted POI data relevant to our study from 23 original categories. We excluded auto-related and infrastructure-related categories as they were less relevant to functional activities and reclassified the remaining POIs into 14 categories: Public Facility, Food and Beverages, Science/Culture and Education Service, Enterprises, Medical Service, Accommodation Service, Commercial House, Daily Life Service, Tourist Attraction, Shopping, Transportation Service, Finance and Insurance Service, Sports and Recreation, Governmental Organization and Social Group. We employed a spatial join methodology to connect each POI with its intersecting grid, thus obtaining all POI data points within a given grid. Finally, we applied statistical methods to calculate the proportion of these 14 indicators. The socioeconomic characteristics of each grid, represented by the feature vector \(F_{poi}\), can be summarized as follows:  
\[F_{poi}=\left(\frac{POI_{1}}{\sum POI^{\prime}},\frac{POI_{2}}{\sum POI^{\prime} },\cdots,\frac{POI_{14}}{\sum POI}\right) \tag{3}\]  
\(POI_{i}\) represents the number of POIs in category i within the grid. \(\sum POI\) is the total number of POIs across all 14 categories within the grid.
# Modeling and Control of an Underactuated System for Dynamic Body Weight Support
## 3 Dynamic Equations of Motion  
The formulation of equations regarding motion development was necessary to construct the physical model of the device. The performed calculations were based on an essential simplifying assumption that considered the rope to be a stiff and non-tensile component. This is due to the complexity of the rope modeling process (especially using synthetic ropes as multi-fiber bodies) and the fact that the ropes were characterized by very high stiffness with increasing tension and number of load cycles [36; 37; 38; 39; 40]. The adopted assumption is justified only in the case of a control system that performs well and ensures that constant load support related directly to rope tension and deformation is maintained. If the control system does not perform the desired motion with a high level of accuracy, this assumption cannot be made and the physical model must also include the rope. Figure 3 presents the physical model adopted for the body weight support system.  
The dynamic performance of the adopted physical model can be described by differential equations of motion. The mathematical model was developed using [PERSON]'s equations of the second kind, which can be written as:  
\[\left\{\begin{array}{c}I_{Z}\tilde{\varphi}+\left(b_{Z}+b_{s}\frac{h^{2}}{4 \pi^{2}}\right)\tilde{\varphi}-b_{s}\frac{h}{2\pi}\tilde{x}+k_{s}\frac{h^{2}} {4\pi^{2}}\varphi-k_{s}\frac{h}{2\pi}x=M_{Z}\\ m_{Z}\tilde{x}+b_{s}\dot{x}-b_{s}\frac{h}{2\pi}\tilde{\varphi}+k_{s}x-k_{s} \frac{h}{2\pi}\varphi=2F\end{array}\right. \tag{1}\]  
where \(\varphi\) is the drive motor shaft rotation angle, \(x\) is the displacement of the passive part, \(I_{Z}\) is the reduced moment of inertia of the active part, \(m_{Z}\) is the reduced mass of the passive part, \(b_{Z}\) is the simplified ratio of the active part's motion resistance, \(b_{s}\) is the damping system ratio, \(k_{s}\) is the spring stiffness modulus, \(h\) is the thread size of the screw drive system, \(M_{Z}\) is the servomotor torque, and \(F\) is the set load of the body weight support.  
The system is determined with variables \(\varphi\) and \(x\), whereas the rotation angle \(\varphi\) is the programmable variable.  
Figure 2: Body weight support system.  
The mathematical model for the relieving system with 3 degrees of freedom is described in [34].  
A problematic issue is related to the impact of patient body inertia. During training, the patient's body is supported with a vertical load (rope tension) that provides weight reduction. Considering the patient's inertia related to their whole body mass is an incorrect approach, as such a system cannot be considered synonymously to the patient's whole-body support. Ignoring their mass is also not justified, as mass has a significant impact on the device's dynamic performance. Therefore, calculations were performed to determine whether the patient's mass affected the reduced mass of the passive part proportionally to the adjusted body support force.
## 2 State of the Art
### Homomorphic Encryption Schemes  
Soon after the idea of public key cryptosystems was introduced by [PERSON] and [PERSON] (see [32]) in 1975, the first implementation of such a system was given by [PERSON], [PERSON] and [PERSON] in 1977. This system, which was--after its inventors--called RSA [33], already showed homomorphic properties in multiplication: With the RSA public key modulus being \(m\) and the exponent \(e\), the encryption \(\epsilon\) of message \(x\) is given as:  
\[\epsilon(x)=x^{e}\mod m\]  
For the multiplication of two plain texts \(x\) and \(y\), it hence follows:  
\[\epsilon(x\cdot y)=(x\cdot y)^{e}\mod m=x^{e}\cdot y^{e}\mod m=\epsilon(x) \cdot\epsilon(y)\]  
Generally speaking, this means that multiplication of two cipher texts \(c_{1}\) and \(c_{2}\) can be done without decrypting them first. Ultimately, this means that this operation can even be performed by some third-party not in possession of the private key for decryption. This useful and convenient property encouraged researchers to further pursue and find more encryption schemes with homomorphic properties.  
In [34], the author introduced yet another encryption scheme with homomorphic properties, which allows much broader applications such as the calculation of the sum over all input data. It has a very useful application for privacy and anonymization, as we show in Section 5.5.  
It took another decade until [PERSON] published his work on a fully homomorphic encryption scheme in 2009 (see [5; 6]). In contrast do the semi- or partially homomorphic schemes known before, fully homomorphic encryption allows any arbitrary computation on cipher texts with no need to decrypt and giving out any cleartext information at all. Unfortunately, fully homomorphic encryption is more expensive in terms of computational demand, thus people need to decide on a trade-off between performance and versatility. We utilize the simulation framework presented in this paper to further analyze this trade-off and present our results in Section 6.
# Approximate Networking for Universal Internet Access
## 2 Approximate Networking Technologies
### Approximate Networking Software: Algorithms  
Implementing approximate networking can be useful in many scenarios where aspects of a system, such as ease of use, functionality, and robustness, may be more important than performance alone. The idea of approximation is an off-used tool in networking algorithms [45]. Approximate networking algorithms [46] (also called heuristics) are often required in networking to tackle discrete optimization problems (many of which are NP-hard, and thus there are no efficient algorithms to find optimal solutions). Such algorithms have been widely used in scheduling, routing, and QoS problems in networking.  
In the book Network Algorithmics [45], [PERSON] has distilled 15 important high-performance principles that have wide applicability in networks, many of which are based on efficient approximations and trade-offs. In particular, Principle 3 talks of trading certainty for time (P3a) and trading accuracy for time (P3b). With P3a, randomized strategies are used when deterministic algorithms may be too slow (e.g., the use of randomization in deciding when to transmit after a collision in millions of Ethernets worldwide). With P3b, the idea is to relax accuracy requirements for speed (e.g., by using lossy compression, approximation thresholds, approximate sorting, and approximate set membership query algorithms such as the Bloom filter and Cuckoo filter).  
In particular, Bloom filters, which is a high-speed, approximate set membership query algorithm whose tests that can return false positives (but never false negatives), have been extensively applied in networking in a wide variety of settings [47]. Bloom filters are important since a large number of applications require fast matching of arbitrary identifiers to values. Since it is common to have millions, or even billions of entries, we require efficient and scalable methods for storing, updating, and querying tables. Although Bloom filters can return a few false positives, their real utility is in alleviating the scaling problems that a number of diverse network applications face. [PERSON] and [PERSON] have articulated the _Bloom filter principle_[47]: \"whenever a list or set is used, and space is at a premium, consider using a Bloom filter if the effect of false positives can be mitigated\". Bloom filters have been used for diverse ends in networking and distributed systems such as caching, peer-to-peer systems, routing and forward, and monitoring and measurement [47].  
_Code perforation_ is another software-based approximate computing technique that can be used to automatically identify error-resilient portions within a code that can be skipped while keeping the error within a predefined range [6].
## 2 FSO Communication System
### Receiver Side  
The role of the Rx is to capture the transmitted optical signal and convert it back into an electrical signal. It typically includes a photodetector, which senses the incoming light, and various electronic components to amplify and process the received signal. The Rx must be aligned precisely with the Tx to ensure accurate signal detection and minimize data loss. FSO systems are generally classified into two main types based on their detection mechanisms: non-coherent and coherent systems. Although coherent systems typically offer enhanced performance by effectively rejecting background noise, reducing the effects of AT, and achieving higher sensitivity at the Rx, Intensity Modulation with Direct Detection (IM/DD) systems are more prevalent in terrestrial FSO applications. This is due to their simpler design and lower cost. This section will delve into the details of both IM/DD and coherent FSO systems. Also, alternative solutions to conventional coherent Rxs are presented, involving simpler transmission schemes based on direct detection (DD) systems.
# On the Evaluation of Complex Networks Designs for an Energy-Efficient IP/WDM Core Network
###### Abstract  
As the Internet grows in capacity, the energy consumption of Information and Communication Technologies (ICT) are significantly increasing. Significant research efforts on energy conservation have been devoted to devise different technological solutions to address raised concerns surrounding the power consumption of networking equipment and its impact firstly on the emission of greenhouse gases and secondly on electricity bills. In this work, we investigate energy-efficient physical topologies for NSF IP over wavelength-division multiplexing (WDM) network for the purpose of minimizing energy consumption by redesigning its current physical connectivity. We implement different network topologies, such as implementing the small-world, scale-free (SFN), and random networks on the NSF network, then evaluate and compare its physical properties and network power consumption with the current NSF topology design using a mixed-integer linear programming model, all with the aim of minimizing the network total power consumption. The evaluation shall optimize and minimize the embodied energy consumption of network equipment in the IP and optical layers. Results have demonstrated that the implementation of the proposed energy-minimized topology designs can significantly improve the node's clustering coefficient, reduce network's diameter, and reduce energy consumption of the NSF IP over WDM network to 28% if compared with the current design implementation.  
complex network; small-world networks; SFN; energy efficiency; WDM; optimization; MILP
## 4 Discussion
### Implementation Justification of a Modular Software for Defect Detection in Smart Agriculture  
The novelty of this research lies in developing a modular software solution tailored for identifying and reporting defects within the context of SA, strawberry classification, and processing. The software solution is developed within an environment comprising MySQL for the database, Python for the application tier, and JavaScript for the presentation tier. This setup leverages the capabilities of SA technologies to create a framework conducive to realizing the quality management objectives.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multirow{2}{*}{**Defect Class**} & \multicolumn{3}{c}{**Number of Identified Defects**} \\ \cline{3-4}  & & **Human Resources** & **Applied Software** \\  & & **Inspection** & **Inspection** \\ \hline \multirow{2}{*}{1} & Class B & 163 & 190 \\ \cline{2-4}  & Class C & 5128 & 5554 \\ \hline \multirow{2}{*}{2} & Class B & 188 & 195 \\ \cline{2-4}  & Class C & 3653 & 3964 \\ \hline \multirow{2}{*}{3} & Class B & 211 & 201 \\ \cline{2-4}  & Class C & 5248 & 6026 \\ \hline \multirow{2}{*}{Overall} & Class B & 562 & 586 \\ \cline{2-4}  & Class C & 14,029 & 15,544 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of visual inspection results obtained by human resources and software solution utilization in observed organization.  
Central to this paper is the fusion of SA research principles with quality objectives aimed at achieving conformity to prevent defects and align strategic objectives with operational enhancements. Organizations can leverage the software solution's inherent modularity, and organizations can streamline defect identification and reporting processes, fostering greater transparency, efficiency, and effectiveness.  
In essence, this research represents an effort in smart agriculture and strawberry processing by offering a comprehensive software solution that harnesses the transformative potential of SA technologies to usher in a new era of quality management and operational excellence.  
The achievement of quality objectives is facilitated through the enhancement of connectivity with reports of emerging insights, the acquisition of knowledge through the utilization of appropriate YOLOv8 and YOLOv9 models, and the automation of problem reporting and resolution via intelligent agents using mobile devices.
## 3 Results and Discussion
### Microscopic Evaluation  
A microscopic evaluation was conducted using CLSM and SEM. For CLSM, the protein and fat fractions were stained green and red, respectively. As shown in Figure 5, the obtained structures were mainly comparable. However, both CLSM and SEM micrographs revealed that OH-treated samples tended to contain larger fat droplets within the protein gel matrix compared to COV-treated samples. This includes, for example, more fat droplets surpassing a diameter of 50 \(\upmu\)m. This finding is assumed to cause the faster greasy perception of OH-treated samples during TDS. As shown by [PERSON] et al. [59], the oiliness of a flavored emulsion increased during sensory evaluation when oil droplet sizes increased from 20 to 50 \(\upmu\)m.  
A more porous network, as well as a decreased integrity of the stabilizing protein matrix which surrounds each fat globule, is expected to cause the observed increase in fat droplet size of OH-treated samples. Fat droplets in meat emulsions are surrounded by protein layers that prevent the coalescence of fat droplets during the cooking of meat emulsion batters [9]. Given that OH leads to less denaturation, as described in Section 3.3 and 3.4, this will have a destabilizing effect on the protein layers and, hence, also on the fat droplet integrity. Thus, the likelihood of fat droplets to coagulate and form bigger-sized fat droplets is enhanced when heated via OH. In addition, the thermally induced protein gel networks under OH are more open and porous compared to COV-treated gel network structures [39; 40]. A more open and porous gel network under OH will enhance the possibility of incorporating larger fat droplets compared to COV.  
In contrast to the results from this study, [PERSON] et al. describe smaller fat droplets in OH-treated meat emulsions batters compared to COV-treated samples, as determined via light microscopy [9]. The authors ascribe smaller fat droplets in OH-treated samples to occurring as a result of \"\(\ldots\)slightly greater amounts of interfacial protein material\" [9]. This difference could be attributed to the applied electrical frequency, as [PERSON] et al. used a low frequency of <100 Hz. As described by [PERSON] et al., low frequencies can lead to protein alterations due to a lower thermal stability [18], which could result in a slightly thicker  
Figure 5: Representative micrographs of Bologna-style sausages heated via ohmic and conventional heating (OH and COV, respectively). (**a**,**b**) Micrographs taken via CLSM (proteins are stained green and fat is stained red); scale bar represents 15 \(\upmu\)m. (**c**–**f**): Micrographs taken via SEM at different magnifications as indicated; scale bars are given in the micrographs.  
layer of denatured proteins. In contrast, an electrical frequency of 10 kHz is likely to lead to less protein denaturation and a destabilizing effect on the protein layer, as described above.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 8 Conclusions  
From the development side, several paper related artifacts have been developed in this work. Firstly, a core application orchestrating all the components of emulation has been created. An orchestrator application for ONOS has been developed for the reactive management of flows and hosts. Open-source contributions to Mininet-WiFi and wmedium have been made on a crucial feature that allows users to operate in isolated Wi-Fi mediums. Three heuristic algorithms and a branch-and-bound style quasi-optimal task offloading algorithm were developed, and their performance analysis along with the Only-cloud baseline case has been conducted in terms of request rate and mobility.  
When the demand in the system is low, the Only-Cloud approach is generally adequate. On the other hand, we note that MEC-based solutions are especially required in high-load scenarios because of typical back-haul connection bottleneck and latency increase. Since it can make better decisions in advance while the others must make decisions with considerably less information in real-time, Q-OPT outperforms the others in terms of the objective function, particularly as the number of service requests rises. Among the heuristic algorithms, ADP achieves better performance results since the processors in ADP handle tasks one at a time without overloading the infrastructure.  
The first direction for future work is to expand our framework for smart routing. The most serious issue we have encountered in our system tests is the limited capacity of wireless links. Smarter network congestion management and routing algorithms can help us to improve the capacity of the system and mitigate this issue. It could also be worth developing smarter heuristic approaches for task offloading. Lastly, better management of UAV mobility could be used to handle area-based congestion. We have observed that some of the APs are more likely to be active during our simulations. According to our analysis, this is because of the traffic model which leads vehicles, especially to the central areas in the simulated geographical area.  
Conceptualization, O.K., G.G. and F.A.; Methodology, O.K., G.G. and F.A.; Software, O.K.; Writing--original draft, O.K.; Writing--review & editing, G.G. and F.A.; Visualization, O.K., G.G.; Supervision, G.G. and F.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:**[[https://github.com/onurklngc/drone-network-onos-and-mininet/tree/master](https://github.com/onurklngc/drone-network-onos-and-mininet/tree/master)]([https://github.com/onurklngc/drone-network-onos-and-mininet/tree/master](https://github.com/onurklngc/drone-network-onos-and-mininet/tree/master)).