esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
With the development of 3D printing methods, factories equipped with this type of equipment are increasingly being built. It is important from the investor's point of view that the developed layout plan takes into account the specific requirements of 3D printing machines. When developing the above-mentioned layout plans, the optimization of workstation layout is an important step. The question has arisen as to whether and how the known methods (e.g., CRAFT, CORELAP, MST, or Schmigalla triangles) will work well with 3D printing machines.  
The concept of layout is very broad and there is no strict definition of it. This is presented and interpreted in various ways; one of the most popular definitions states [1] that \"A facility layout is an arrangement of everything needed for the production of goods or delivery of services. A facility is an entity that facilitates the performance of any job.\" [2; 3; 4; 5]. On the other hand, the factors that should be taken into account when designing the layout are clearly specified, ensuring adjustment to the requirements of the production system [6]:  
* the type, variety, quality, and quantity of raw materials, work-in-process, and finished goods;
* the technological specification at each stage and the process of conversion as depicted in a flow process chart;* the type of machinery and manufacturing aids necessary for the conversion process as stated above;
* the human factor of skill level at each stage, the number of persons required, the safety requirements and their ergonomics;
* the statutory and regulatory requirements as per provisions of the factories act must be considered for the plant layout;
* the line balancing and waiting factor decides the space requirement for the material storage;
* the material handling system is an integral part of the plant layout. It is one of the major cost areas. It is decided based on the extent of automation required and associated cost, speed, volume to be handled and type of material;
* the change factors in terms of future expansion, product modification, product range, flexibility and versatility of the organization, its products and technology; and
* service factors for machines and men have to be provided for. The space for machine maintenance, safety requirements for men and machines, provision of canteen, urinals, and toilets for workers, etc. is to be provided in the plant layout.  
The variety of layout requirements, their large number and scale (from the location of the production hall on the industrial plot to the design of a single workstation), and the need to combine knowledge from various areas (from health and safety regulations, requirements regarding the order of arrangement of workstations, access between them, necessary connections, to their ergonomics) makes the implementation of a comprehensive layout project long and costly.  
With the growing popularity of 3D printing techniques [7,8], layout designers will increasingly face the task of designing the layout of workstations taking into account the requirements for this type of device.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 7 Conclusions  
In this paper, we proposed a network lifetime-extended power-optimized cooperative MAC protocol for WSNs. In the protocol, the LETPO algorithm is used to calculate optimal transmission powers of the sender and its cooperative nodes under certain constraint conditions based on linear  
Figure 21: Number of data packets transmitted by each node vs. offered traffic load.  
Figure 20: Throughput vs. offered traffic load.  
programming and greedy algorithm, which obtains diversity gain and transfers part of energy consumption of one data packet transmission from the sender to the cooperative nodes, thus balancing the local network energy consumption and extending the network lifetime. Furthermore, a cooperative node selection scheme is used to efficiently choose cooperative nodes with high channel gain and avoid HTS transmission collisions by limiting the maximum number of cooperative nodes to transmit HTS packets and coordinating their access delay. Simulation results show that compared to the DT and EE-CR protocols, the proposed PO-CMAC protocol can efficiently improve the network lifetime, average number of data packets transmitted by each node and network energy utilization at both uniform and non-uniform network traffic load. Furthermore, it can adapt to different network traffic load by adjusting the parameter \(M\), thus extending the network lifetime efficiently. In general, the PO-CMAC protocol with \(M=1\) is the best for uniform traffic load, and the PO-CMAC protocol with \(M=2\) is suitable for non-uniform traffic load.  
In the future work, we will improve our protocol by optimizing both throughput and energy consumption performance in a multiple data rate network. We also plan to embed a duty cycling mechanism into our protocol to further extend network lifetime and improve throughput.  
This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 61231013, 61271195 and 61301083; the National Science Foundation for Innovative Research Groups of China under Grant No. 61521091; and the State Scholarship Fund of the China Scholarship Council (CSC) under Grant No. 201406025008.  
[PERSON] and [PERSON] conceived the idea, analyzed the performance and designed the simulation; [PERSON] and [PERSON] performed the simulation experiments and analyzed the data; [PERSON] and [PERSON] wrote the paper; and all of the authors actively participated in review and revise the paper.  
The authors declare no conflict of interest.
## 3 Basics of Our Framework to Provide Video-Streaming Services over MANETs  
In this section we review the basic features of the video transmitted. Video frames are distributed using RTP/RTCP (Real-time Transport Protocol/RTP Control Protocol) over UDP as transport layer protocols.  
The considered video-streaming service uses a basic layered MPEG-2 VBR video code. The video flow is composed of sets of frames, usually 4 to 20 frames, named group of pictures (GoP). A GoP has three kind of video frames: I, P and B, with a frame-pattern repeated in each GoP. The composition of a GoP and the relationship between frames in the decoding process is depicted in Figure 1. I (Intra) frames encode spatial redundancy and form the base layer, providing a basic video quality. I frames carry the most important information for the decoder. If an I frame is lost, the whole corresponding GoP will be useless for the decoder and will be discarded. P (Predicted) and B (Bi-directional) frames carry differential information with regards to preceding (for P) or preceding and posterior (for B) frames, respectively. We considered those features to assign different to each video frame according to their importance in the video stream. Thus, I frames have the highest priority, P frames a medium priority and B frames the lowest one.  
We assume that the IEEE 802.11e [22] standard works in the media access control (MAC) layer. This standard provides QoS support for services such as video-streaming. The IEEE 802.11e includes four different access categories (AC). Each packet from the higher network layer arrives at the MAC layer with a given priority. That packet is classified at the MAC layer to the proper AC. We defined a mapping between the different classes of packets into one of the four ACs:  
* AC0: signaling
* AC1: high priority packets (I frames)
* AC2: medium priority packets (P frames)
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 3 Methodology
### Driving Characteristics Comparison of Micro-EVs and Conventional Vehicles
Using the GPS device, the speeds of both the micro-EVs and the conventional vehicle were compared as they passed through the narrow road sections. The results demonstrated the strengths of the micro-EV on narrow roads.
# Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
## 4 Results and Discussions
### Limitations  
EAA transport may not suit all goods within the identified commodity categories. As the EAA technology evolves, logisticians must reconsider factors such as bulkiness, packaging limitations, cost, and relative demand. Even though manufacturers aim to augment EAA payload, there will be a capacity limit, making it challenging to transport certain kinds of bulky goods with EAAs. On the other hand, as EAA technologies continue to evolve, and systems can manage larger autonomous fleets at all hours and at a lower cost, it will become more profitable to carry certain high-demand, high-value, and light-weight goods. The International Air Transport Association (IATA) suggests that smartphones, tablets, laptops, gaming electronics, pharmaceuticals, medical items, and urgently needed factory parts or tools are some examples of items that are suitable for air transport [56].  
While this study provides valuable insights into identifying commodity categories and locations that would be economically desirable for transport by EAAs, further assessment is necessary to determine the specific types of goods within these categories that are suitable. Consequently, this study forms a foundation for logisticians to initiate a more detailed assessment of the types of goods within the identified commodity categories and locations that would be most economically desirable for transport by EAAs.
## 2 Methods  
The proposed model consists of hybridizing a seasonal autoregressive integrating moving average (SARIMA) model as a linear model, and a non-linear model such as a support vector machine for regression (SVR). These methods are two of the most important forecasting models concerning their respective domains. Additionally, a clustering method, Kohonen self-organizing map (SOM), is combined with this hybrid (SARIMA-SVR) model. The methodology and basic concepts are next described.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 2 Background
### Network Resilience  
Network resilience refers to a network's ability to defend against and maintain an acceptable level of service in the presence of various faults and challenges to normal operation [28]. Resilience is a general term that applies to several areas, such as cybersecurity, fault tolerance, software dependability, and network survivability [29]. The different network resilience disciplines [30] are mapped in Figure 4. In this study, network resilience falls under Challenge Tolerance, specifically, under Disruption Tolerance, which is highlighted in blue in the figure.  
A challenge is defined as an event that impacts normal network operations [14]. In general, a challenge can be caused by malicious or non-malicious sources. A major non-malicious challenge in a communication network is a disruption [30]. Disruptions come from challenges in the communication environment that make it difficult for the network to maintain stable end-to-end connections between nodes. Disruption tolerance is the ability of a network to tolerate disruptions in connectivity among its nodes. Examples of disruptions are weak signal connectivity, unpredictably long delays, and power or battery issues [30]. This study addresses two types of disruption, which, in this context, are called challenges. The first is a relatively trivial challenge that causes weak signal connectivity, i.e., the storm challenge. The second is a severe challenge that disconnects some nodes completely, i.e., the fire challenge.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 5 Conclusions  
In this paper, we have evaluated the feasibility of performing robust multipath and spoofing mitigation with spatial filtering techniques in GNSS antenna arrays of a very small size. In particular, four-antenna rectangular and two-antenna linear arrays have been  
Figure 4: MEE in a suburban scenario with ULA2 (**top**) and URA4 (**bottom**) array distributions.  
Figure 3: Cancellation of replica with respect to its delay in a suburban scenario with ULA2 (**top**) and URA4 (**bottom**) array distributions.  
considered in a simulation campaign carried out for benchmarking different beamforming techniques in challenging environments, namely urban and suburban. The simulation results have shown the goodness of the LMMSE-Capon hybrid and the power-based Capon beamformers in such small arrays, particularly in the presence of coherent multipath and spoofing. Therefore, they have become potential candidates for signal mitigation purposes in decimeter-wave handheld devices, and their use over conventional beamformers has become mandatory to overcome the limitations of small arrays. Furthermore, although four-antenna arrays do provide better performance, comparable results have been obtained for the case of two-antenna arrays, thus confirming the suitability of the latter for the practical implementation of spatial filtering techniques in smaller devices such as smartphones.  
Conceptualization, S.L.-S. and J.A.L.-S.; methodology, L.P.-R. and S.L.-S.; software, L.P.-R., S.L.-S. and G.F.-C.; validation, L.P.-R., S.L.-S. and G.F.-C.; formal analysis, L.P.-R. and S.L.-S.; investigation, L.P.-R. and S.L.-S.; resources, G.S.-G. and J.A.L.-S.; data curation, L.P.-R. and S.L.-S.; writing--original draft preparation, L.P.-R. and S.L.-S.; writing--review and editing, all authors; visualization, L.P.-R. and S.L.-S.; supervision, S.L.-S. and J.A.L.-S.; project administration, J.A.L.-S.; funding acquisition, G.S.-G. and J.A.L.-S. All authors have read and agreed to the published version of the manuscript. This work has been supported in part by the Spanish Agency of Research (AEI) under the Research and Development projects PID2020-118984 GB-I00/AEI/10.13039/501100011033 and PDC2021-121362-I00/AEI/10.13039/501100011033.  
Not applicable.  
Data Availability Statement: Data are contained within the article.
# A Fragile Watermarking Scheme for Authenticity Verification of 3D Models in GLB Format
## 3 Materials and Methods
### Watermark Extraction and Originality Verification  
The process of extracting the HMAC code, i.e., the watermark, is analogous to the embedding process, but follows a slightly altered order (Figure 5). It also requires using a steganographic key. First, the vertices and bits storing the watermark data are identified in the marked model. From these, the HMAC that was hidden in the watermarking process is extracted. This code is then compared with a new HMAC value (HMAC*) calculated from the verified 3D model. It is indicated as HMAC* in Figure 5. Before computing the new HMAC, the bits previously used for watermark embedding are reset to zero. The modified model is then saved as a temporary file, from which the HMAC* is computed. Finally, the extracted HMAC code is compared with the newly calculated one. If both 512-bit sequences are identical, the model is considered original and unmodified. Any differences between the compared HMAC values indicate that the verified model has been altered since it was marked.  
Figure 4: The scheme of the steganographic key.
# Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking
## 2 Performance of Traditional Doppler Aiding
The equivalent phase error transfer function of the aided loops is:  
\[\theta_{\varepsilon}\left(s\right)=H_{\varepsilon}\left(s\right)\theta_{i} \left(s\right)-H\left(s\right)n\left(s\right) \tag{1}\]6  
When \(\omega T_{a}>2\pi\), we can find a frequency \(\omega_{b}\) meeting \(\omega_{b}T_{a}<2\pi\) and \(\omega T_{a}=\omega_{b}T_{a}+m2\pi\), which yields \(\theta_{ia}(kT_{a})\approx-\frac{A}{\omega}C_{a}\left(\omega T_{a}\right)\cos \left[\omega_{b}kT_{a}+\phi_{a}\left(\omega T_{a}\right)\right]+\frac{A}{ \omega}C_{a}\left(\omega T_{a}\right)\cos\left[\phi_{a}\left(\omega T_{a} \right)\right]\). For this case Equation (12) still suffices. It is accurate enough, provided that the product of \(\omega_{b}\) and \(T_{a}\) satisfies \(\omega_{b}T_{a}\ll 2\pi\), as can be validated by the simulation in Section 3.2. Thus, \(C_{a}\left(\omega T_{a}\right)\) is the ratio of the phase error amplitudes of aided loop to unaided loop. Figure 3 shows how this ratio varies with \(\omega T_{a}\) according to Equation (10). Note that the \(C_{a}\left(\omega T_{a}\right)\) expression is applicable to any loop orders except for 1. It can be seen that: (1) If \(\omega T_{a}<2.3311\), the Doppler aiding can improve tracking performance by \(C_{a}\left(\omega T_{a}\right)<1\); (2) When \(\omega T_{a}\gg 2\pi\), \(C_{a}\left(\omega T_{a}\right)\approx 1\), the aiding can hardly help to mitigate the dynamic stress error; (3) When \(\omega T_{a}\ll 2\pi\), \(C_{a}\left(\omega T_{a}\right)\approx\frac{1}{2}\omega T_{a}\), \(\lim\limits_{\omega T_{a}\to 0}C_{a}\left(\omega T_{a}\right)=0\). Hence, for the traditional Doppler aided loop, the aiding rate is expected to be as high as possible to efficiently reduce the dynamic stress error. However, the practical INS-offered data rate may not satisfy the cases with high frequency component or large amplitude of the LOS dynamics.
# SpaceSheep: Satellite Communications for Ovine Smart Grazing
## 3 Results
### Alarm Transfer Cost and Latency  
Information transfer latency is a critical factor when it comes to an alarm transmission system. The latency of the JSON-encoded alarm system was measured and compared with the WiFi connectivity latencies. After the satellite connection was established, the messages used for testing simulated the transport of the collar information received by the WSN to the cloud-hosted application. In the first test, two metrics were used to assess the daily volume of the traffic exchanged: (a) the number of connected collars (1, 2, 5, 10, 20, 50, 100, 200, 500, and 1000); (b) the reporting periodicity (s) (30, 60, and 90). In this scenario, the same number of messages was simulated for each combination of the previous two metrics, and the duration of the experiment varied according to periodicity, with a longer duration of 90 s. From the results obtained, the amount of data generated in a 24 h interval was extrapolated, and the cost was calculated.  
The results show an increase in the amount of data generated with an increase in the number of collars and a decrease in the period. The maximum volume of data was achieved for 1000 collars and a period of 30 s, with a daily volume of approximately 1.89 GB.  
In terms of financial cost, considering the data transmission cost of this network (of EUR 4.79 to 9.58 per Mbps), even the unrealistic scenario, whereby the herd consisted of a single sheep, would have a significant cost: for a period of 90 s, the daily cost would be between 9.0 \(\times\) 8 \(\times\) 4.79 \(\approx\) EUR 345 and 9.0 \(\times\) 8 \(\times\) 9.58 \(\approx\) EUR 690.  
Regarding the second test, the latency of the system for the transmission of collar information was registered at different times during the day, using both WiFi connectivity and EchoStar's satellite link. This experiment was repeated 10 times for both 1 and 100 collars. The results were registered with a 95% confidence interval using a t-distribution.  
As for the results test, Figure 2 shows the variation in the time taken to send the information. As can be seen, in both cases the latency increased alongside the message size. Both technologies showed some fluctuations throughout the day, although this would have no impact when applied in a realistic scenario.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Timestamp** & **Device Type** & **ID** & **Alarm Type** & **Priority** & **Additional Information** \\ \hline Sat Nov 20 07:22:36 2021 & Beacon & 1 & Battery & Low & Battery: 18 (\%) \\ Sat Nov 20 07:36:12 2021 & Collar & 2 & Infraction & Low & \#Warnings = 21 (in 3 min) \\ Sat Nov 20 08:19:47 2021 & Beacon & 1 & Battery & High & Battery: 6 (\%) \\ Sat Nov 20 08:37:14 2021 & Collar & 2 & Equipment & High & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of alarm notifications.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 4 Challenges and Future Work
### Reduce the Computational Cost of OBDA  
Computing the OBDA involves complex mathematical calculations and requires a large amount of computing resources, which would consume considerable power. This presents a significant challenge for FSO communication remote nodes with limited SWaP budgets. To promote the widespread adoption of FSO communication on various platforms, the computational cost of OBDA must be reduced to some extent. Potential measures for overcoming this challenge are shown below:  
1. Algorithm Optimization: Use different algorithms of varying complexity to compute the OBDA for different types of terminals. Large communication terminals, such as ground stations, can use relatively complex and advanced OBDA algorithms due to their high tolerance for high computing costs. For remote small units, relatively simple OBDA algorithms can be used, and the computational accuracy can be appropriately reduced to alleviate the computational pressure;
2. Distributed Computing: Assign the task of computing the OBDA for each communication party to a node with powerful computing capabilities. And the computing results are then transmitted to the remote communication node via FSO communication or RF link. This measure could reduce the computational burden and power consumption of small communication nodes. However, it introduces additional delay. Therefore, it is essential to comprehensively consider the capabilities of terminal and relay equipment, communication environment, and application requirements to achieve optimal performance and efficiency;
3. Machine Learning Techniques: Utilize machine learning to improve the accuracy and efficiency of the OBDA algorithm. By leveraging the complex relationship between the link factors and OBDA, machine learning can improve accuracy and efficiency, resulting in reduced computational costs. In addition, neural network hardware accelerators combined with field-programmable gate array (FPGA) can be reconfigured for different target applications while maintaining low power consumption. This measure leverages the ability of FPGA parallel computing to achieve low-delay processing.  
By implementing these measures, the computational cost of OBDA can be reduced, leading to lower power consumption. This can enable FSO communication on platforms with limited power supplies and facilitate its proliferation in various scenarios, such as military operations, disaster relief, and remote sensing applications.