esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of MPTCP Congestion Control
## 4 Multi-Homing Solutions
### Transport Layer  
At the transport layer, we are able to take end-hosts requirements/metrics into account. Different solutions have been proposed for this layer. This subsection covers the SCTP and MPTCP protocols.  
* **Stream Control Transmission Protocol** (SCTP) [16]: SCTP is an alternative transport protocol that aims to transfer data in a non-strict reliable manner. This means that data can be transferred in an ordered, partially ordered or an un-ordered manner. SCTP can support multiple IPs per connection. Instead of one data stream per connection, SCTP divides the stream into smaller chunks called associations. To address the head-of-blocking issue, SCTP breaks down the data sequence concept in TCP into so-called regions. Regions are basically an association distributed between multiple sequence streams. Data can be transmitted through any path available to the association. SCTP CC is based on TCP CC. In the case of one association, all the paths share the same CC metrics. If an association has multiple endpoints, then CC might be separate on each path. One of the challenges of SCTP is that it uses a different socket API. This means that applications need to be specifically developed to use SCTP [1,2]. SCTP was not initially compatible with NAT and other middle boxes. [PERSON] and [PERSON], [17] developed a NAT module, called SONATA, to support SCTP. However, SCTP has not been widely deployed outside of niche applications [2].
* **Multi-path TCP (MPTCP)**: In 2013, Multi-path TCP protocol (MPTCP) [3] was released as an extension to TCP to allow multi-homed devices to transfer data through multiple paths simultaneously. MPTCP is compatible with existing TCP applications and functions within the current Internet. The main difference between SCTP and MPTCP is that each sub-flow in MPTCP behaves as an individual and independent TCP flow. MPTCP will be discussed in detail in Section 5. The connection performance of MPTCP is determined by the cooperation of the following [18,19]:  
Figure 3: Mobile IPv4 [1].  
Figure 2: Site-Wide Multi-homing Example [1].  
Path Manager:A path manager decides when to create/remove flows. The decision is made based on application requirements. Some applications might focus on using multiple paths to avoid failover. In this case, the path manager switches to the secondary path when the primary path fails. In other applications, load balancing plays a crucial part in the performance. In this case, the path manager establishes multiple paths to ensure data can be transferred simultaneously.
* **Packet Scheduler:** A scheduler is an algorithm that selects a path to transmit packets on. The main MPTCP schedulers will be discussed further in Section 6.
* **Congestion Control:** The congestion window of each subflow and the overall connection are taken into account based on different properties (e.g., fairness, friendliness, responsiveness). Different MPTCP CC algorithms will be discussed further in Section 7.
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 3 Multi-Frequency Detection Schemes
### Modifications Required for Application Against DVB-T Signals  
Regardless of the detection scheme employed, for the MF integration to be effective, the target echoes received at different frequency channels are supposed to be perfectly aligned on the range/velocity plane. This is typically the case if: (i) the same bistatic geometry is exploited, namely all the frequency channels are broadcasted by the same transmitter and received by the same PR receiver; (ii) the target size is limited with respect to the radar range resolution; and (iii) anomalous propagation effects do not take place or, if present, they yield quite similar propagation paths on the integrated frequency channels.  
In addition, from a discrete signal processing point of view, the range/velocity CAF maps have to be evaluated on common grids to allow a pixel-by-pixel integration of the results obtained at different frequency channels (either the intensity levels or the binary outputs after the application of the first-decision threshold).  
The above issues could be easily managed in a FM radio-based PR [23]. In contrast, the finer range resolution and the wider frequency separation of DVB-T channels might jeopardize the validity of the above assumptions. Therefore, these assumptions have to be carefully verified and proper strategies should be devised to make the system robust to potential limitations. A dedicated discussion of these problems is reported in the following sub-sections distinguishing between limitations arising at a signal processing stage and limitations due to the geometrical and electromagnetic characteristics of the acquisition scenario.
## 4 Proposed Methodology
### Sentence Generation
#### 4.3.2 Decoder LSTM  
The decoder LSTM plays a slightly different role in the training phase and inference phase. The decoder is initially configured to generate the output sequence based on the encoded data. The initial states of the decoder (\(h_{0}\), \(c_{0}\)) are set to the final states of the encoder. In the training phase, a start token is added before each word, and an end token is added to the end of each sentence. These two tokens are used to indicate the starting and ending of the sentence while decoding. The \"Teacher Forcing\" technique is used to train the decoder. The approach utilizes the output of the preceding time-step as the input for the subsequent time-step of the decoder. Afterward, the loss is estimated on the expected outputs from each input sequence. The input sequence is sent through an embedding layer in both the decoder and the encoder to minimize the lengths of the input character vectors since one-hot-encoded vectors may be relatively big. The embedding layer decreases the size of the word vectors from four to three in the encoder section, as shown below. At the output layer of the proposed model, the softmax activation function in Equation (7) is used to handle multiple classes.  
\[\sigma(\vec{w})_{x}=\frac{e^{\vec{w}_{x}}}{\sum_{y=1}^{2}e^{\vec{w}_{y}}} \tag{7}\]  
where \(\sigma\) represents the softmax function, the input vector is \((\vec{w})\), the standard exponential function for the input vector is \(e^{\vec{w}_{x}}\) and for the output vector is \(e^{\vec{w}_{y}}\), and the number of classes in the multiclass classifier is \(z\).  
Lastly, errors are back-propagated throughout the time to update the parameters of the network. During the inference phase, the decoder is called in a loop. The initial input of the decoder is always the start token. The loop breaks when the end token is executed by the decoder. Figure 7 illustrates the overall workflow of the proposed language model.
## 3 Analysis of Effects of Co-channel Interference on EBPSK Waveform  
The modulation waveform corresponding to code \"1\" can be regarded as the superposition of all-zero sequence (_i.e.,_ pure sine wave) and jumping sequence (_i.e.,_ just existing signal in EBPSK jumping point), as shown in Figure 1. In addition, the special detection filter used in EBPSK system is a linear filter, so superposition principle can be used. For simplicity, this paper takes the filter having one conjugate pole and one conjugate zero points as an example to analyze. Letting the decomposed sequences pass the filter, we can obtain the response shown in Figure 2. Obviously, the jumping response and all-zero response have almost the same phases, which enhance each other, so that the amplitude impulse is produced.  
Reference [8] proposed the IIR narrowband band-pass filter with multi-poles and one zero, which can produce even much higher impact in phase jumping point than that with one pole and one zero as analyzed above, much narrower bandwidth, and greatly improve output SNR. Especially, in the case of signal submerged by noise, _i.e.,_ SNR \(<0\), the modulation information is emphasized with impacting shape, therefore, in the following real simulation, the filter having three conjugate poles and one conjugate zero point is taken as an example, and the transfer function can be written as following:\[H(z)=\frac{1-1.61817331\ 85991785\ z^{-1}+z^{-2}}{1+\sum_{i=1}^{6}a_{i}\cdot z^{-i}} \tag{2}\]  
where \(a_{\rm i}=-4.578193\,\)B92746454, \(a_{\rm 2}=9.6546659241157258\), \(a_{\rm 3}=-11.692079\ 480819313\), \(a_{\rm 4}=8.575634157768217\), \(a_{\rm 5}=-3.6121554\ 794765309\), \(a_{\rm 6}=0.70084076\ 007371199\)
## 2 Materials and Methods
### MTInSAR Time Series Analysis
The polynomial model selected by the NLTA procedure generally improves the fit with respect to the model provided by the MTInSAR algorithm that generated the time series, leading to higher values of the output coherence \(\gamma_{t,\;OUT}\) with respect to the input coherence \(\gamma_{t,\;IN}\). According to this, a subset of coherent targets showing interesting temporal behavior (\(PS_{\text{NLTA}}\)), can be derived by exploring the values of \(\gamma_{t,\;OUT}\) and \(\gamma_{t,\;IN}\) as, for instance, in the following:  
\[PS_{\text{NLTA}}=\left\{\gamma_{t,\;IN}<\gamma_{t,IN}^{Th}\;\bigwedge\;\gamma_ {t,\;OUT}\geq\gamma_{t,OUT}^{Th}\;\bigwedge\;(\gamma_{t,\;OUT}-\gamma_{t,\; IN})\geq\Delta\gamma^{Th}\right\} \tag{2}\]  
where \(\gamma_{t,IN}^{Th}\), \(\gamma_{t,OUT}^{Th}\), and \(\Delta\gamma^{Th}\) are threshold values that for the present study are set respectively to 0.7, 0.8, and 0.1. Furthermore, recently, an index based on the fuzzy entropy (\(FE\)) has been used [35] for measuring the degree of regularity of a given time series; the \(FE\) basically evaluates the gain in \"information\" by comparing segments of \(m+1\) samples with segments of \(m\) samples, according to some distance measure. The \(FE\) index, without postulating any a priori model, allows highlighting time series which show \"interesting\", i.e., locally smooth, trends, including strong non linearities, jumps related to phase unwrapping errors, and the so-called partially coherent scatterers. Also in this case, a subset of interesting coherent targets (\(PS_{FE}\)) can be selected through the \(FE\) index values according to a threshold value derived in [35] for time series of the type used in this work:  
\[PS_{FE}=\left\{FE<0.5\right\}\quad\text{ with }\;m=2 \tag{3}\]  
Both NLTA and the \(FE\) index were adopted for analyzing the MTInSAR displacement time series described in Section 2.2. In particular, the MTInSAR targets were selected through a model-independent procedure based on \(FE\)[35] and classified according to the polynomial order selected through the NLTA procedure [34]. By identifying and correcting phase unwrapping errors through \(FE\), and by refining the displacement model through NLTA, the proposed post-processing scheme allows decreasing the residual processing errors and thus also the noise affecting the final displacement estimation. This is proved by the final temporal coherence values, which are generally higher than those coming from the standard MTInSAR processing [34; 35]. This estimation refinement leads to an increase in the accuracy of the derived displacement products. Moreover, this post-processing scheme allows a focus on a smaller set of coherent targets affected by nonlinear displacements, and potentially deserving further geophysical or geotechnical analysis. Finally, it is well suited for catching warning signals such as, for example, those preceding the failure of natural and artificial structures.
## 4 Data Analysis in Maritime IoT Applications
### Role of Data Analysis in Enhancing Maritime Operations
#### 4.2.5 Ensure Regulatory Compliance  
In the face of stringent environmental regulations and standards, monitoring and analyzing data related to emissions and waste is crucial. Data analysis can provide accurate and real-time insights into a vessel's environmental impact, ensuring compliance with international and national regulations.  
Avoiding non-compliance penalties is not the only benefit; preserving the reputation of the company in an increasingly environmentally conscious market can have long-term advantages. It also drives companies to adopt cleaner technologies and fuels, contributing to the global efforts to mitigate climate change [83].
# A Configurable Dependency Model of a SCADA System for Goal-Oriented Risk Assessment
## 4 Research Methodology
### Why Mind Maps?  
Expert knowledge could be captured using a variety of techniques ranging from interviews and surveys to concept maps and mind maps. In this research, we chose _mind mapping_ guided by the reasons explained below.  
Mind mapping is based on the natural structure of a human mind [21]. A mind map is a radial diagram that represents semantic or other connections between portions of learned material hierarchically [22]. Mind mapping finds its application in learning, research and business where it may be used for knowledge capture and sharing, brainstorming and problem solving. It is a convenient tool for the quick capture of opinion and summary of knowledge. Mind maps accelerate the accumulation of information, its structuring and systematisation. They assist reflective thinking and enable a user to link the knowledge about a topic to the broader body of knowledge [23].  
Mind mapping was preferred to other knowledge capture techniques because  
* It is easy to learn and use so that all experts were able to produce detailed mind maps during a one-hour workshop. Previous work reports the results of a comparative analysis of four knowledge sharing techniques (mind maps, concept maps, conceptual diagrams and visual metaphors). The comparison indicates that mind mapping is the easiest technique to use and learn in comparison to other methods [22].
* It offers a more time-efficient data collection for the researchers. In particular, using this method, we were able to elicit opinions from a group of experts at the same time. It would not be possible with interviews, for example.  
* It allows optimising the data analysis by requesting the participants to produce mind maps themselves rather than the researchers producing a mind map based on the analysis of in-depth interviews or observations. According to [24], _\"mind mapping can allow researchers to make rapid and valid transcriptions of qualitative interviews without the need for interviews to be transcribed verbatim. It may also aid the researcher in the analysis of qualitative data by helping her or him to 'bracket' their own preconceptions, which is fundamental in phenomenological research\"_.
* The similarity of a tree-like structure between a mind map and a dependency model guarantees that the raw data are already well structured and easier to analyse and translate into a dependency model (than for example data from in-depth interviews). In addition, mind maps are association maps [23]--mind maps allow making meaningful connections and associations between various concepts and between different parts of related knowledge--and as such, they could efficiently depict dependency associations.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 2 Related Work
Existing studies have also considered strengthening storage resources with unique authentication schemes using blockchain, and one such study was carried out by [PERSON] et al. [38]. The authors provided a unique authentication scheme in which a storage system is designed using a Merkle tree and blockchain to achieve authentication efficiency. The scheme resists internal and external attacks using two-way validation performed with the servers hosted in the cloud ecosystem. [PERSON] et al. [39] also presented a study on authentication in which a permission-based blockchain was used to simplify the authentication method and optimize the data storage system in a large-scale network. Data encryption was performed using homomorphic encryption, and the outcome exhibited practical privacy preservation. Various authentication schemes using blockchain were also presented by [PERSON] et al. [40], [PERSON] et al. [41], and [PERSON] et al. [42]. Existing blockchain schemes also alleviate various internal IoT operations, indirectly contributing to security. [PERSON] et al. [43] presented a unique architectural design for a gateway node to resist malicious activities in data using the Ethereum blockchain. [PERSON] et al. [44] presented a task-offloading scheme using blockchain and bio-inspired algorithms that targeted optimal resource allocation in edge networks. [PERSON] and [PERSON] [45] discussed an access control scheme in a blockchain free from any dependencies of trusted third parties. Distributed and open-source software frameworks have been adopted for data provenance in extensive-scale networks, as noted by [PERSON] et al. [46]. This study contributes to distributed data storage using Hyperledger fabric to retain extensive metadata and verification tags. [PERSON] and [PERSON] [47] presented a computation offloading mechanism in which a search optimization algorithm was integrated with a bio-inspired algorithm implemented on fog computing characterized by blockchain properties. The existing system also adopts a machine learning approach to improvise blockchain-based operations on a large scale. [PERSON] et al. [48] used attention-based gated recurrent units to predict the inbound tasks. Federated learning was used by [PERSON] et al. [49] to strengthen trust in IoT. In addition to predictive resource management, this model performs attacker detection using a Support Vector Machine. Further use of machine learning approaches in blockchain has been reported by [PERSON] et al. [50], [PERSON] et al. [51], [PERSON] et al. [52], and [PERSON] et al. [53]. [PERSON] et al. [54] presented a secure blockchain model for controlling and monitoring events in smart grids. Table 1 summarizes the existing studies discussed in this section.
# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 3 System Model
### Time-Dependent Task Model  
The heterogeneity, networking, and complexity of applications in IoT scenarios make the task offloading problem increasingly complex. To improve the task response rate, abstract modeling becomes an important tool and the first step to solving this problem. In practical applications, because the timing dependency constraints between tasks need to be considered when responding to user service requests, a directed acyclic graph (DAG) can represent such dependencies more clearly. Therefore, existing studies usually use DAG to model tasks with timing dependency constraints.  
In this paper, we use DAG \(G_{d}\) to denote the application program of the \(d\)th terminal device, \(G_{d}=(V_{d},E_{i,j})\), in which \(V_{d}\) denotes the set of subtasks in the graph and \(E_{i,j}\) denotes the dependency between tasks if \(E_{i,j}=1\) denotes that the execution of task \(j\) requires the computation result of task \(i\). On the contrary, if \(E_{i,j}=0\), it means that there is no dependency between task \(i\) and task \(j\) in terms of the order of sequential execution. For each subtask in the task set, \(V_{d}\) denoted as \(V_{i}=(a_{i}c_{i},T_{i}^{tol})\), \(a_{i}\) is the size of the input data of task \(i\), \(c_{i}\) is the number of CPU cycles required to compute each bit of data for the task, and \(T_{i}^{tol}\) is the maximum tolerable delay for computing the task.  
Tasks in the DAG can choose whether to offload or execute locally based on their offloading delay and global resource utilization. If the task is offloaded to the MEC server, the offloading process is divided into three stages: sending data, executing the task, and returning the results. If the task is executed locally, the delay only needs to consider the time of local execution.
# Mapping Processing Elements of Custom Virtual CGRAs onto Reconfigurable Partitions
## 2 Related Work
### Relocatable Partitions in FPGAs  
Modern FPGAs are highly heterogeneous and modules can only be relocated between partitions using compatible sets of resources. Reusing the placement and routing result at another location by applying them as fixed design constraints can speed up implementation and hence shorten the development cycle [29; 30]. Nevertheless, relocating entire partial bitstreams is far more advantageous. Even though many studies on this subject were published over the years, a vast majority (i.e., [31; 32; 33; 34; 35; 36]) have become outdated with the emergence of new FPGA architectures and changes in design tools.  
[PERSON] et al. [37] established a great foundation for bitstream relocation in Xilinx 7-Series devices. Their work presents design requirements and methods that can be used to enforce relative placement and routing of modules in order to achieve bitstream compatibility. Unfortunately, the work oversees potential global routing feed-through and can lead to breaking routing connections if it occurs. [PERSON] et al. [38] proposed a very similar approach extended by a feed-through prevention obtained by module isolation. Both studies are presented as proof-of-concept and address solely the relocation between two compatible partitions.  
For the purpose of relocating virtual coarse-grained block implementation results (as constraints or bitstreams) to multiple locations within the same or across multiple designs--as presented in this study--the authors proposed an improved technique based on the aforementioned work. Its distinctive features are:  
* Separated workflows for structural static design and reconfigurable module functionalities;
* Nested sub-PBlock based constraining of partition-to-static port buffers;
* Cross region routing prevention (as adjacent regions can be unavailable at different positions);
* Local routing loop-back prevention (as loop-back resources replacing connections in the available direction occur only in boundary located switchboxes).  
This study focuses on different aspects of Virtual CGRA implementation in FPGAs; partition relocatability is only presented to the extent required to comprehend the proposed solutions. Technical details of all methods and tools used to obtain reconfigurable module relocatability are described in full in a previous publication by the main author [29; 39].  
Another similar relocation technique has been recently proposed as a part of the IMPRESS design automation tool [14; 15]. The main difference between the approaches is in the global routing feed-through prevention method. Instead of applying the vendor provided module isolation mechanisms, IMPRESS generates blocker nets around reconfigurable regions.