esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Impact of Network Configuration on Positioning Performance
#### 3.3.1 Configuration without Buoys
In general, configurations without subsurface or surface buoys were unable to achieve positioning when the seafloor station distance exceeded 19 km, which can be avoided in the \"3+1+1\" network configuration with a subsurface buoy and sea surface buoy. In addition, the height of the subsurface buoy can be adjusted to improve the performance of the geodetic network. Therefore, the subsurface buoy is a necessary and important component to make UGNs more resilient and precise. Although the sea surface buoy and the subsurface buoy had similar performances in kinematic positioning, the surface buoy could be equipped with a satellite communication module for transmitting the UGN data, receiving remote-control commands, etc. Moreover, the GNSS equipment on the surface buoy can be used for the clock synchronization of acoustic equipment, and the acoustic signal from the buoy can send time synchronization information to other stations in the UGN, which is crucial to maintaining accurate system time synchronization.
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
## 5 Proposed Hardware Realization of Flexible 5G NR LDPC Encoder
### Circular Shifting Network for High Flexibility
For example, for \(D=4\), if the remainder is 0, there is no need for reordering, which can be concluded from positions of newly generated CPMs in Figure 9. However, if the remainder is 1, the first bit group must be placed last, the second must be placed first, the third must be placed second, the fourth must be placed third, etc. The described architecture provides another important feature. The shifted bits for lifting sizes other than 96, 192, or 384 are aligned to one side of the data block. Several examples are shown in Figure 10. Light green and dark green areas represent groups of \(Z\) valid bits that should be shifted, whereas the hatched area represents nonvalid data. This property is of crucial importance since such data alignment drastically simplifies the remaining encoder blocks: core parity bits calculator, input data interface, and output data interface. Finally, the proposed procedure can be easily applied to a more generic CS network that can shift more than four independent blocks of data. However, in such cases, due to the high irregularity of check nodes in 5G NR LDPC codes, in this particular application, the ratio of the throughput gain with respect to the additional complexity would not be as high as it is for the former cases. Additionally, the processing of the first four rows in the base graph matrix cannot be further parallelized since core parity bits are necessary for later processing. Nevertheless, other LDPC encoder or decoder architecture may have more benefits from additional shifting network splitting. For example, in order to obtain better HUE, row-based parallel architectures could use a shifter that processes all inputs in parallel for small lifting size values but combine parallel and serial processing for higher lifting sizes. Again, for 5G NR, PCM has a broad range of row weight values, and such a shifter would not always be used in its full capacity, but for codes that have relatively high row weights for all rows, not just for the first \(4Z\) as in 5G NR, benefits to the HUE would be significant. Similar conclusions apply to codes whose column regularity is higher. Figure 10: Shifting network architecture for the proposed 5G NR encoder. The network is capable of circular shifting four independent groups of data if \(Z\leq 96\), two independent groups of data if \(96<Z\leq 192\), and one group of data if \(192<Z\leq 384\).
# Distributed Combinatorial Maps for Parallel Mesh Processing
## 3 \(n\)D Distributed Combinatorial Maps
### A Data Structure for Storing nD Distributed Combinatorial Maps  
To store an \(n\)-dmap \(DM\), any classical data structure for representing an \(n\)-map can be used to store each block of \(DM\) (see for example [29; 30]). However, we need to represent additional information about critical darts in order to propose an efficient solution to retrieve \(\beta_{n}\) links with a good complexity. Our solution is to store the following information in each block \(M^{b}\), \(\forall b\in\{1,\ldots,k\}\), in addition to the classical data structure that represents the \(n\)-map:  
* each critical dart is marked with a Boolean mark, and stores its label;
* one associative array gives for each label used in this block the critical dart \(d\) of \(M^{b}\) with this label.  
The Boolean mark allows us to test in constant time if a dart is critical or not. Each critical dart \(d\) directly stores its label, which can thus be retrieved in constant time. Moreover, the associative array allows us to retrieve the critical dart having a specific label (If we use a hash table for the associative array, retrieving a dart from its label can be done in amortized constant time.).  
Note that each block is fully independent in this data structure, and there is no need for shared memory. This is the perfect solution to implement a parallel algorithm on different computers thatcommunicate with message passing system, as we will see in our experiments.  
**Possibility to store block numbers of critical darts.** It is possible to store in each critical dart the number of the other block sharing this label. This additional information can be used in order to optimize the communication between the tasks, because each block knows directly the other block that contains a given critical dart. It is then possible to send messages only to concerned blocks. In return, there is a small overhead in terms of memory space. Without this information, the memory space occupied is smaller, but messages must be broadcast. This possibility offers a compromise that can be used or not, depending on the priority of applications.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 2 BitTorrent-Like Systems and Network Coding Background
### The Butterfly Example  
In this subsection, we show a simple multicast network, with a single source node that sends messages to multiple recipient nodes. The butterfly network is the conventional example where throughput gain using network coding can be demonstrated.  
In the directed graph shown in Figure 2, the source \(S\) sends two messages to each of the recipients \(R_{1}\) and \(R_{2}\). With traditional routing, \(R_{1}\) will receive the first message through the path A-\(R_{1}\), and the second message through the path C-B-D-\(R_{1}\). Similarly, \(R_{2}\) will receive the first message through the path C-\(R_{2}\), and the second message through the path A-B-D-\(R_{2}\). Assuming that each node can send only one multicast message per time slot, then using the traditional store-and-forward routing scheme, the link \(BD\) becomes a bottleneck. Furthermore, \(R_{1}\) will receive message <\(a\)> two times, and \(R_{2}\) will receive message <\(b\)> two times, leading to redundancy and wastage of the link bandwidth.  
Figure 1: Illustration of piece distribution in baseline and network coding BitTorrent. (**a**) Baseline BitTorrent; (**b**) Network coding BitTorrent.  
Figure 2: Butterfly network with traditional routing. (**a**) node B sends packet b; Then, (**b**) node B sends packet a [20].  
Applying network coding as shown in Figure 3, the two messages <\(a\)> and <\(b\)> can be XORed at router \(B\), a new message <\(c\)> is created such that <\(c=a\oplus b\)>. Then, this message is forwarded to router \(D\) which will send message <\(c\)> as a multicast to both \(R_{1}\) and \(R_{2}\). \(R_{1}\) can retrieve message <\(b\)> by Xoring message <\(a\)> with message <\(c\)>, as <\(b=a\oplus(a\oplus b)\)>, while \(R_{2}\) can retrieve message <\(a\)> by Xoring message <\(b\)> with message <\(c\)>, as <\(a=b\oplus(a\oplus b)\)>. Using this novel encoding technique, the time required to deliver the two messages to the recipients is decreased by one time slot. Furthermore, neither redundant packets nor wastage of bandwidth are incurred.  
In more complex scenarios, we need more complex coding operations such as random linear network coding (RNLC) [30; 31], or vector network coding [32; 33; 34]. With RNLC, the outgoing packets are linear combinations of original packets, so each encoded packet contains data plus coefficients. RLNC can be applied in a distributed fashion, where each node can independently and randomly select coefficients from the Galois field (GF). GF is very useful for practical purposes because each element of the GF can be represented with the same number of bits that guarantees that the result is representable with the same number of bits as the original packets.
## 2 Global Design and Working Scheme  
The complete working scheme is depicted in Figure 2. The RF energy harvesting system is formed of four stages: the first one is an Archimedean spiral antenna, an ultrawideband antenna that operates from 0.35 to 16 GHz. Even though the Archimedean spiral is a traditional antenna, its interesting properties and capabilities, such as the circular polarization, large bandwidth and omnidirectional pattern, have barely been exploited in RF energy harvesting. This cost-effective miniaturized design (FR4) acts as the harvesting element of the system, acquiring the available environmental power. Moreover, it is also utilized to characterize the power spectrum and to classify the most important spectral power peaks in real indoor and outdoor scenarios at the Telecommunication Engineering School of Universidad Politecnica de Madrid.  
The second and third stages are the matching and conditioning circuits, respectively. The conditioning circuit is essentially in charge of rectifying the power harvested by the antenna. For this purpose, a Cockcroft-Walton multiplier has been used. As briefly discussed in Section 1, the multiplier circuit is also capable of elevating the output voltage apart from rectifying. This is a key point in energy harvesting, as the increase in the input voltage makes the acquired energy more manageable. Then, the matching circuit is designed to maximize the power transfer between the spiral antenna and the conditioning circuit. This stage is implemented through an \(L\) matching network with lumped elements. As will be seen, the parasitic elements of the circuit play an important role in the design of conditioning and matching circuits. They have to be taken into account and to be properly modeled. Otherwise, the efficiency of the circuit would be considerable reduced. Thus, the most  
Figure 2: Full overview of the proposed RF energy harvesting system.  
harmful parasitics are identified in the circuit, and a circuit model is created to correct the frequency shift they produce.  
The fourth stage is the storage circuit. It lays up the power previously acquired by the antenna and subsequently processed by the matching and conditioning circuits. It is formed by a high-capacity shunt capacitor, \(C_{st}\), which feeds the sensor appropriately.  
Multiple tests were carried out to check the correct operation of each stage separately. Additionally, the four stages were connected one to each other and the full system was tested in the laboratory in a real scenario. Unlike the vast majority of studies, we used a real sensor--a temperature sensor--as the load to be supplied. We proved that the sensor is perfectly operational if the antenna is able to collect at least \(-4\) dBm, which is less than the power demonstrated to be acquired by the spiral antenna in Figure 8.
## 2 Methodology
### PVTC-Based Analysis for Single Logic Gates
(**a**) SVTC curve for an inverter gate; (**b**) Calculation of output signal’s pdf; (**c**) Voltage pdf for input signal \(V_{in}\); (**d**) Division of \(V_{in}\)’s pdf. In Figure 5, the \(x\)- and \(y\)-axes are the two input voltage levels, and the \(z\)-axis is the output voltage level. The pdf calculation for the output signal \(V_{out}\) is expressed as  
\[p\left(V_{out}\right)\cdot dV_{out}\cong P\left(V_{out2}<V_{out}\leq V_{out1} \right)\cong\sum_{i}P\left(V_{out,i}\right)\cong\sum_{i}\int p\left(V_{in1,i} \right)dV_{in1}\cdot\int p\left(V_{in2,i}\right)dV_{in2} \tag{4}\]  
where \(\left(V_{in1,i}V_{in2,i}V_{out,i}\right)\) is the _ith_ point on the three-dimensional SVTC curve with \(V_{out2}<V_{out,i}\)\(\leq V_{out1}\). Accurate analysis of transient fault occurrence probabilities, or transient fault rates for different parts of circuits, is an important step for the effective implementation of transient fault mitigation techniques. Therefore, after acquiring the pdf of a circuit's output ports, we can calculate the transient fault rate \(P_{error}\) for these ports. Figure 6 presents an illustration of the transient fault rate calculation for an inverter gate whose normal state is input \"one\" and output \"zero\". As shown in Figure 6, the two shaded areas in the figure are the probability of logic \"0\" and logic \"1\". Then, \(P_{error}\) can be calculated according to Equation (5):  
\[P_{error}=P\left(V_{out}>V_{high,min}\middle|V_{nom}=0\right)=\sum_{i}p(V_{out,i })dV_{out} \tag{5}\]  
where \(V_{high,min}\) is the voltage threshold for logic \"one\", \(V_{nom}\) is the expected correct voltage for the output signal, and \(p(V_{out,i})\) is the estimated pdf of the points whose \(V_{out,i}\) is greater than \(V_{high,min}\).
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 3 Theoretical Foundation of Resilience Curve and Its KPIs in This Domain  
To quantify the resilience of smart energy systems, this study conducts a comprehensive literature review. The review identifies various measurable resilience indexes. From this pool of indexes, and various articles, the current study focuses on a case study within smart energy systems. To quantify CI resilience in this CIs, timeliness plays a vital role. Therefore, we prioritize time-related KPIs for this analysis. Other metrics also impact resilience monitoring, such as Age of Information (AoI), which quantifies data freshness and serves as a critical metric for real-time resilience monitoring [41; 42; 43; 44]. However, these metrics are not derived from the resilience curve, and we leave their exploration for future studies.  
Two reference articles form the foundation for the selection of R-KPIs and resilience curve construction in this study (see Section 3.1 for definitions). An empirical study conducted in the domain of smart PV panels, using a similar testbed [45], serves as a basis for selecting three specific KPIs: functionality loss, minimum performance, and recovery time. To calculate these R-KPIs, it is essential to first construct a resilience curve [46] (see Section 3.1). Given that the collected data are inherently noisy, we apply two methods empirically implemented and evaluated for noisy data in [47]: Support Vector Machine (SVM) and polynomial fitting via least squares (Polyfit) (see Section 3 for definitions). Although SVM was identified in the reference article as the superior method for accurately reflecting system behavior, both methods are employed in this study to allow for comparative analysis. As a practical recommendation for monitoring system resilience, it is advised that once a method is chosen, it should consistently be used throughout the system's lifecycle to construct its resilience curve.
### The Goals and Merits of This Study
#### 1.4.2 Merits of This Study  
The system proposed as described above aims at providing the users with real puppets and constructing a stage to simulate the performance situation of real puppeteers. \"Role play\" will be taken as the main theme of this study. The users can personally experience the operations of real puppets on the stage just like professional puppet performers and utter the narrations of the puppet characters to learn in-deep the puppetry and the related culture. In more detail, the five merits of this study can be identified from the aforementioned role-play-based system design, as described in the following.  
1. Use of digital media to make up traditional puppetry deficiency--the role-play approach adopts digital media to present traditional puppet operations, narrations, plot contents, and maneuvering skills so as to preserve the excellent cultural connotation of traditional puppetry. It also makes up for the visual and auditory deficiencies of traditional puppet shows and performances through digital video production.
2. Understanding and promotion of the puppet tradition--from the point of understanding the classification of puppet roles, through the actual experience in manipulating puppets and uttering the narrations, users can understand the connotation of the puppet roles, learn to figure out the characteristics of various roles, and construct, if desired, basic teaching materials that can be utilized to further puppetry education, so as to promote the participation of the public in inheriting the traditional puppet culture.
3. Attracting the young generation with new technology--through interactive displays, visitors can personally experience the operations of puppets, uttering the narrations, and sensing the stage performance situation. Through the combination of traditional puppetry operations and background videos produced by new media, the new generation of young people will be attracted to know puppetry and further enjoy the way of puppet performance.
4. Promoting understanding of puppetry content--the interface design is based on the maneuvering of the tangible puppets of various roles, and the interaction is carried out by taking advantage of digital media combined with the tangible interface operations. Furthermore, background videos produced by the new media showing expert puppeteers' performances are also played for the user to emulate. All these strategies break through the static display mode seen in museums in which old puppets and static puppet-show models can only be watched without tactile sensation. This leads to a better understanding of puppet shows because easier and more realistic in-person experiencing of traditional puppet operations can be achieved.
5. Making the traditional puppetry content richer for inheritance--through creative thinking in the display design of digital content production, the use of tangible interface, as well as practical operation experience, the display content of the traditional puppet culture expectantly will become richer, more specific, and simpler, and the inheritance of the puppetry will be promoted more easily.
## 4 Application
### Semiconductor Industry  
Semiconductor manufacturers are organized in globally distributed and complex supply networks with complex manufacturing sites [68; 69]. In the supply networks various partners searching for their own local optima are involved. Flows of material, information and finance across different continents must be successfully managed. Additionally, cultures, customs and time zones must be organized [69; 70]. The complex manufacturing process in this industry's so-called wafer fabs, regarding their manufacturing principle also referred to as complex job shop, often containing more than 100 machines and up to 700 process steps results in a lead time of up to three months. Furthermore, it is characterized by criteria such as prescribed due dates of the orders, parallel machines, different process types, sequence-dependent setup times, frequent disturbances and re-entrant order flows [68; 70]. Re-entrant order flows result in wafers that are at different stages of their manufacturing cycle having to compete for the same resources [70]. Therefore, even if complex job shops are a subtype of job shops, they differ significantly [18].  
In supply networks such as this, planning accuracy decreases significantly with an increasing time horizon [9]. A study with 52 multinational semiconductor manufacturerswith an annual turnover exceeding USD 400 million has shown that insufficient forecasts and their implications are the top supply network concern causing for example unnecessary high inventory levels and unreliable deliveries [71]. Even if modern approaches allow for improved forecasts, they are still forecasts with biases and errors [72]. Resulting deviations between forecasts and actual demand lead to many events that require appropriate responses [10].
## 3 Deep Learning in IoMT
### IoMT Architecture  
After we review different frameworks and architectures from different sources [104; 105; 106], we improvise and propose our architecture of DL-empowered IoMT for healthcare, as shown in Figure 1. The whole structure consists of four layers as follows:  
* Service layer: This layer is the headquarters of the system; it can be used as data storage or for sending requests to the user-end and training as a global model aggregator in deploying federated learning [107].
* Network layer: This layer serves as a reliable carrier, making sure to smoothly transmit information to the end-user. It is adaptable to various communication technologies such as LTE [108], 5G [109], and WiFi [110], making it a practical and versatile component of the architecture.
* Edge layer: This layer is an edge remote server layer; it can provide computing resources for the end-users.
* Smart device layer: This layer, comprising a variety of IoMT devices, is designed to be flexible and adaptable. It can sense, send, or collect patient data, and can be deployed in both local and centralized model training, ensuring its versatility and adaptability.  
As shown in Figure 1, the architecture of the IoMT comprises several key components:  
* Sensors: These are at the core of the IoMT and are embedded in places (e.g., wearable devices, smart implants, or ambient monitoring systems). Sensors capture physiological signals like heart rate, glucose levels, blood pressure, oxygen saturation, or movement data.
* Communication networks: The IoMT relies on secure and robust communication technologies like Bluetooth, Wi-Fi, Zigbee, and 5G to transfer data from sensors to processing systems. These networks must ensure low latency and high reliability for supporting real-time healthcare applications.
* Edge devices: Often situated close to the sensors, edge devices preprocess data, reducing latency and offloading computation from the cloud. For instance, smartwatches or edge processors can analyze sensor data locally to detect anomalies or trigger alerts.
* Cloud platforms: Cloud computing provides scalable infrastructure to store and analyze the large amounts of data generated by the IoMT. Cloud platforms support advanced data analytics, integration of multimodal data, and collaborative healthcare services, among others.  
Figure 1: Layer architecture of smart healthcare.
## 2 Materials and Methods
### Data and Processing
#### 2.2.3 DEM Data  
Some researches have illustrated that the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data have a good elevation accuracy over mountainous area [28; 29; 30], ASTER GDEM were adopted to describe the complex terrain in this work. GDEM data available at a 30 m were averaged to obtain the values at 500 m and 1 km resolutions [7]. The surface slope and aspect were derived from the GDEM data, according to a fast algorithm based on a 3 \(\times\) 3 pixels window [31].