esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FE Model
### Sensitivity Analysis  
To select the correct finite element size for the metallic core component and to ensure accurate numerical results for the core component modelled with one-dimensional Beam elements, a mesh sensitivity analysis has been carried out. In fact, the energy absorbed by a panel, for various size of the beam elements in the lattice core, has been compared to the energy absorbed by a panel fully modelled with solid elements under an impact event at 20 J. The panel dimensions used for the sensitivity analysis and the applied boundary conditions are illustrated in Figure 8.  
Figure 8: FE model used to perform the sensitivity analysis: (**a**) impactor dimension and mass; (**b**) panel dimensions and BCs.  
Figure 7: K-39 LT—Shock absorbers system integration—bottom view.  
The boundary conditions applied in this section were set by replicating those exposed in [26]. In this reference article it has been demonstrated through comparisons with experimental and literature data, that the developed model subjected to the BCs exposed in Figure 8 is able to represent with high accuracy the real behaviour of the shock absorber.  
According to the conditions shown in Figure 8, four impact analyses at 20 J have been carried out where, as shown in Figure 9, four different sizes (BEAM1: 2.5 mm, BEAM2: 1 mm, BEAM3: 0.5 mm and BEAM4: 0.25 mm) of the beam elements of the lattice core have been used.  
Figure 10 relates the energy absorbed to the four investigated beam configurations and by the solid configuration, while Figure 11 compares the normalised computational times of the four investigated beam configurations to the normalised computational time of the solid configuration. The computational time has been normalised by the computational time of the solid model.  
Figure 9: Unit cell discretization: (**a**) formulation with solid C3D8 elements; (**b**) formulation with beam elements.  
In Figure 10, it can be appreciated that the BEAM 3, BEAM 4 and SOLID configurations are characterised by extremely similar values of absorbed energy. However, as it can be read in the histogram of Figure 11, the BEAM 3 configuration shows a computation time 75% lower with respect to the one of the SOLID configurations.  
Considering the proposed five absorber panel system is characterised by a large number of DoF, the BEAM3 configuration has been chosen for the modelling of the lattice core in the frame of the final impact simulations.
## 4 Results and Discussions
### Generalized vs. Personalized Inertial Data  
In some applications such as rehabilitation, we may be interested in only a subject-specific estimation process to boost the performance of the system. Hence, we have also evaluated the performance of the BLSTM network when trained using subject-specific and the whole dataset. The name assigned to the subject-specific datasets is \"personal.\" The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **hpr\_x*** & **knR\_x***b & **hpl\_x***c & **knL\_x***d & **Average** \\ \hline PID & 5.76 & 6.01 & 6.81 & 6.16 & 6.19 \\ TID & 2.74 & 4.92 & 3.40 & 4.28 & 3.84 \\ SID & 3.51 & 3.73 & 3.02 & 4.33 & 3.65 \\ FID & 4.41 & 4.21 & 3.23 & 5.55 & 4.35 \\ \hline \hline \end{tabular}  
* right hip extension/flexion, \({}^{\mathrm{b}}\) right knee extension/flexion, \({}^{\mathrm{c}}\) left hip extension/flexion, \({}^{\mathrm{d}}\) left knee extension/flexion.  
\end{table}
Table 2: MAE (in \({}^{\circ}\)) of the trained BLSTM network for the first four-joint-angle set using various inertial datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **hpr\_x** & **hpr\_d***a & **knR\_x** & **ankR\_p***b & **hpl\_x** & **hpl\_d***c & **knL\_x** & **ankL\_p***d & **Average** \\ \hline PID & 5.59 & 7.39 & 6.71 & 7.76 & 5.41 & 4.68 & 7.01 & 9.97 & 6.82 \\ \hline TID & 2.28 & 7.91 & 4.66 & 9.33 & 3.34 & 4.76 & 3.99 & 6.23 & 5.31 \\ \hline SID & 3.93 & 7.19 & 3.39 & 10.41 & 3.19 & 5.07 & 5.33 & 4.67 & 5.40 \\ \hline FID & 4.41 & 4.71 & 4.91 result is shown in Table 4. The MAE values of the personal dataset indicated in the table are obtained by averaging the MAE of all subjects. The dataset division for this training was 74%, 16%, and 10% for training, validation, and testing, respectively. As indicated in Table 4, the errors for the individual dataset are noticeably lower than the general (whole) dataset errors. This might be adopted in applications that call for a more accurate personalized joint-angle prediction.
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 6 Experiments and Analysis
### Qualitative Analysis  
#### 6.5.1 Impact of Interpolation Weight  
We first investigate the relationship between the interpolation weight \(\lambda\) employed in our model and the ratio of knowledge utilization in the generated responses. We group the test samples into separate bins according to their \(\lambda\) value and calculate the averaged knowledge BLEU score, BLEU\({}_{K}\), for responses within each bin. The relationship between \(\lambda\) and the averaged BLEU\({}_{K}\) is illustrated in the line plot in Figure 3. The x-axis represents the values of \(\lambda\), ranging from 0 to 1, while the y-axis represents the averaged BLEU score (BLEU\({}_{K}\)). Each data point on the line plot represents the average BLEU\({}_{K}\) for responses falling within a specific range of \(\lambda\) values. When \(\lambda\) = 0, the output is determined solely by the context variable \(z_{c}\), with no knowledge-specific influence; this can be interpreted as a response that relies solely on the immediate context and does not make use of any external knowledge. When \(\lambda\) = 1, the output is determined solely by the knowledge specific variable \(z_{k}\), without any contribution from the context variable \(z_{c}\). This means that the response will be generated solely based on the acquired knowledge, disregarding the context provided. Intermediate values of \(\lambda\) (between 0 and 1) blend both variables, enabling smooth transitions and combinations of knowledge and context. The line plot in Figure 3 shows the relationship between \(\lambda\) and the averaged BLEU\({}_{K}\) (knowledge BLEU score) for different experimental settings. It indicates that as the interpolation weight \(\lambda\) increases, the BLEU\({}_{K}\) also tends to increase, implying a positive correlation between knowledge utilization (represented by \(\lambda\)) and the quality of responses (measured by BLEU\({}_{K}\)). This suggests that the learned interpolation weight can effectively represent the knowledge utilization ratio, as higher values of \(\lambda\) indicate a stronger influence of knowledge in generating responses. The line plot helps visualize the trend that increasing the emphasis on knowledge (higher \(\lambda\)) leads to improved knowledge utilization and better quality responses. It provides evidence that the interpolation weight \(\lambda\) plays a crucial role in controlling the balance between knowledge and context in the model's responses. It demonstrates that, as the model assigns higher weights to the knowledge variable, it generates responses with better utilization of knowledge, leading to improved BLEU\({}_{K}\) scores.
## 4 Mapping Results
For this purpose, we present a classification by the type of study (proposal, formalization, meta-study, implementation, or extension). This classification scheme can be consulted in Table 3 from Section 3.3 of this article. A proposal in this context is a primary study that presents a new methodology, method, technique, etc. According to Table 5, for RE of IoT, there were a total of 12 proposals for IoT software systems development, and these were [30; 32; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. The formalization category refers to an article that contains logical language or operators. Of these primary studies, only one fit in this category, and this was [39]. The research on IoT software systems development considering the RE phases includes meta-studies (surveys, reviews). Of the 24 primary studies, only three were found to provide a significant overview of an existing research study. These were [44; 45; 46]. Another category is implementation, referring to primary studies presenting tools, plugins, or suites. In this regard, seven articles were cataloged as implementations. These were [37; 47; 48; 49; 50; 51; 52]. Figure 2: RE techniques applied in the proposals from the primary studies. Extension studies is the last category from the classification presented in Table 3. These primary studies present new functionalities or proposals derived from previous implementations. In this work, there were two extension studies: [33; 52]. In Table 5, all 24 primary studies are listed. In summary, the scientific literature regarding research on RE in relation to IoT software systems development is focused on proposals (new methods, methodologies, ideas) and
## 5 Conclusions  
In this paper, we proposed a novel distributed algorithm for constructing spanning trees in WSNs. The implementation and efficiency evaluation of some algorithms for constructing spanning trees found in the literature helped us to identify their advantages and drawbacks. The proposed algorithm is a more efficient and suitable solution than its counterparts in the literature, due to several factors. Firstly, EBF constructs the routing tree even in the event of message losses or node failures. This is an important feature, because message losses is a common event in wireless networks, and sensor nodes are prone to failures. Another important feature is scalability, since WSNs are frequently formed by a large number of nodes. Simulations showed that EBF significantly reduces the runtime and communication overhead of the previously proposed solutions without causing an excessive impact on the quality of the tree. In addition, EBF is flexible with respect to the metric used to compute the cost of the links, does not demand a lot of computational resources and also does not require network topology knowledge. Lastly, the constructed structure is resilient, since nodes have alternative routes to be used as mechanisms for fault tolerance and load balancing.  
We identified the following issues for the continuation of this work: the proposition of a protocol for the reorganization of the routing tree, inclusion of a power consumption model, the proposition of new metrics considering the probability of an outage for computing the costs of the links and the evaluation of the network lifetime.
# Motion Compensation for Long Integration Times and DoA Processing in Passive Radars
###### Abstract  
In this work, a multistage target motion compensation solution for long integration times and the direction of arrival processing in geostationary-satellite-based passive radars is presented. Long integration processing intervals are considered to compensate for the associated propagation loss, but during this time target dynamics can extend the backscattering in more than one range or Doppler cell. To control the gain-processing reduction, a combination of detection, tracking, feature extraction, and filtering techniques is designed to provide automatic adaptation to each unknown target dynamic in the area of interest. The proposed methodology is validated with real data acquired by the passive radar demonstrator developed by the University of Alcala (IDEPAR), and the results confirm that target monitoring exploiting digital video broadcasting-satellite (DVB-S) signals is clearly improved.  
passive radar; DVB-S; target motion compensation Article  
[PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON], [PERSON]; Motion Compensation for Long Integration Times and DoA  
Processing in Passive Radars. Remote Sens.2023151031[[https://doi.org/10.3390/s15041031](https://doi.org/10.3390/s15041031)]([https://doi.org/10.3390/s15041031](https://doi.org/10.3390/s15041031))  
1
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
## 2 Related Work
### Mitigation Approaches  
There are mainly two directions in the research of DDoS mitigation approaches [5]. The first aims to mitigate attacks on the victim's side, one of the methods is by blocking automated application requests utilizing user puzzles such as the works proposed in [29; 30], where IP tables are setup for Intrusion Detection Systems (IDS) rules [31]. Although message-level mitigation granularity can be achieved, the mitigation may be less efficient as the malicious packet must be received by the victim before mitigation, and the system is still vulnerable to DDoS attacks. The second direction is by mitigating DDoS attacks in the network. Once the victim has identified the source of the attack, traffic filtering or rerouting schemes such as the works in [5; 32; 33] that can be used to mitigate the attack within the network without consuming resources on the victim's part. However, traffic from benign IPs can also be filtered, which can cause significant collateral damage. Our approach includes both victim-side and in-network mitigation for efficient and effective defense against DDoS attacks.  
To summarize, since the introduction of DDoS attacks, tremendous efforts have been put into trying to find efficient solutions to the problem; however, no specific solution was designed specifically for SYN-Flood attacks. To our knowledge, no optimal solution has emerged so far. This section has presented a summary for a number of proposed solutions that have some but not all of the required characteristics. The aforementioned works have drawbacks in terms of the administrative burden, the extra injected network traffic, backward compatibility with the TCP/IP protocol suite, and the overhead on the victim itself.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation
### Evaluation Methodology
#### 6.1.1 Experimental Design  
Our study quantifies the goodput achieved by two clients uploading traffic to a server over a joint bottleneck link with 50 Mb/s. Client 0 is mostly a heavy user in our experiments and Client 1 a light user. We utilize the experimental setup depicted in Figure 3. Clients with different IP addresses send traffic over fast access links with 100 Mb/s via a switch to a server behind a slow bottleneck link. Traffic from each client constitutes a traffic aggregate identified by its source IP address. In this specific experiment, only the client side of the bottleneck link requires ABC functionality. It meters the activity of packets coming from the clients and drops them depending on that value. We renouce ABC on the return path because it carries only little traffic, e.g., TCP acknowledgements in some experiment series.
# Efficient Massive MIMO Detection for M-QAM Symbols
## 2 Preliminaries
### Problem Formulation  
We consider an uplink massive MIMO system in which the BS is equipped with \(K_{r}\) receiving antennas and \(K_{t}\) transmitting antennas. The received signal \(\mathbf{z}\in\mathbb{C}^{K_{r}}\) at the BS can be expressed as  
\[\mathbf{z}=\mathbf{H}\mathbf{s}+\mathbf{w}, \tag{1}\]  
where \(\mathbf{H}\in\mathbb{C}^{K_{r}\times K_{t}}\) denotes a complex channel matrix whose elements are independent and identically distributed (i.i.d.) zero mean Gaussian random numbers, \(\mathbf{s}\) is a \(K_{t}\times 1\) transmitted complex vector from a \(2^{2x}(x\) is an integer)-order quadrature amplitude modulation (QAM) constellation \(\mathcal{A}\) (e.g., 4 QAM, 16 QAM), and \(\mathbf{w}\) denotes a \(K_{r}\times 1\) i.i.d. complex additive white Gaussian noise (AWGN) vector with zero-mean and covariance matrix \(\sigma^{2}\mathbf{I}\).  
The ML detector solves (1) with the best detection performance. Assuming \(\mathbf{H}\) is known at the receiver, the ML detector is equivalent to solving the problem below:  
\[\mathbf{\hat{s}}_{\text{ML}} = \arg\min_{\mathbf{s}\in\mathcal{A}^{\text{T}_{t}}}||\mathbf{z}- \mathbf{H}\mathbf{s}||^{2}\] \[= \arg\min_{\mathbf{s}\in\mathcal{A}^{\text{R}_{t}}}\Bigl{\{} \mathbf{s}^{H}\mathbf{R}\mathbf{s}-2\Re\{\mathbf{y}^{H}\mathbf{s}\}\Bigr{\}},\]  
where \(\mathbf{R}=\mathbf{H}^{H}\mathbf{H}\) and \(\mathbf{y}=\mathbf{H}^{H}\mathbf{z}\). Unfortunately, the fact that the ML signal detector is NP-hard (nondeterministic polynomial time) prevents the use of the method that provides a perfect solution (2).
## 8 Outcomes and Simulated Results
### Outcomes from the Amplified Audio System  
The simulations for the amplified audio were carried out using K-framework3 software, which is open-source and available online [50].  
For Sisto V Hall, the selected column array is the GF162 model by K-array (Florence, Italy), placed at 4.5 m intervals, with a total of four units per side to ensure full audience coverage. To assess the uniformity of sound pressure level (SPL), four virtual microphones were placed at the corners of an audience block, as shown in Figure 10. Each speaker was set with a gain of \(-15\) dB and oriented \(30^{\circ}\) outward from the wall. The audience area was modeled at a height of 1.3 m.  
The same column array (GF162 by K-array) was selected for Aula Magna, but with a spacing of 5.0 m between units, resulting in three speakers per side. Similarly, four virtual microphones were placed at the corners of an audience block to analyze SPL uniformity, as shown in Figure 11. Each speaker was set with a gain of \(-10\) dB and oriented \(30^{\circ}\) outwards from the wall. The gain in Aula Magna was set lower to achieve the same SPL range, as its walls are slightly larger than those of Sisto V Hall. The audience area was also modelled at a height of 1.3 m.  
Figures 10 and 11 show that, with the amplified audio system and the applied gain adjustment, SPL values range between 90 dB and 93 dB. While low frequencies are not well covered by this speaker array, the octaves relevant to speech are effectively reproduced [52].  
Figure 11: Spatial distribution within the Aula Magna of SPL in dBA (**a**) and frequency response of virtual microphones deployed in one audience block (**b**).  
Figure 10: Spatial distribution within Sisto V Hall of SPL in dBA (**a**) and frequency response of virtual microphones deployed in one audience block (**b**).  
This additional analysis of the amplified audio system was carried out to complete the acoustic design for these two vaulted halls, which are used for conferences and celebrations.
# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
###### Abstract  
In the era of the Internet of Things (IoT), IoT conversational agents (IoT-CAs) have become the gateways for smart spaces. Users will inevitably self-disclose some types of personal information while interacting with IoT-CAs. In this study, users' willingness to disclose different types of information to IoT-CAs in two smart spaces (living space and workspace) and two user contexts (one user or two users) was investigated. One living space and one workspace were built for users to experience interactions with IoT-CAs. Subsequently, users' willingness to self-disclose six types of personal information was measured. Two experiments were separately conducted for a single user (N = 36) and two users (N = 48). The results indicated that users were most willing to disclose information about their tastes and interests and least willing to disclose money information. Users in the living space were willing to disclose more information than those in the workspace, which was mediated by users' expectations for the reciprocal services of IoT-CAs rather than the awareness of other persons or external factors. Participants had a high private self-awareness in the living space and workspace; their attention was focused on themselves rather than on external factors in smart spaces.  
IoT-CA; self-disclosure; personal information types; space +
Footnote †: journal: Applied Sciences
## 1 Introduction  
The _ALK_ oncogene encodes ALK, a membrane-bound receptor tyrosine kinase, which activates multiple downstream signaling pathways that are keys in cell proliferation and survival [1]. It was first identified in 1994 as a fusion partner in a t(2;5) chromosomal translocation in anaplastic large cell lymphoma, which is how it got its name (Anaplastic Lymphoma Kinase) [2]. Many other fusion partners and activating gene alterations (point mutations, deletions, and rearrangements) have since been identified as oncogenic drivers in a variety of malignancies, including non-small-cell lung cancer (NSCLC), inflammatory myofibplastic tumor (IMT), neuroblastoma, and renal cancers [3; 4; 5]. In the past two decades, the landscape of treatment for these tumors has been changed by targeted therapy with small-molecule ALK tyrosine kinase inhibitors (TKI), of which several are currently FDA approved for use in adults, and one (crizotinib) for use in children [6; 7].  
More recently, oncogenic _ALK_ fusions have been identified in a small subset of patients with acute myeloid leukemia (AML) and other myeloid-lineage hematologic malignancies (Table 1). Recent advances in genomic and transcriptomic technologies have allowed us to perform risk stratification and adjust treatment for patients with AML based on cytogenetic and molecular features, but certain alterations continue to confer poor prognoses despite treatment intensification. Patients with the monosomy 7 subtype AML, a subset with a poor prognosis, were recently retrospectively evaluated via RNA sequencing by [PERSON] et al. (2020), and 14% of the patients were found to have cryptic _ALK_ fusions [8; 9]. _ALK_ fusions have been described in adults with the monosomy 7 subtype of AML as well [10; 11; 12]. Importantly, cells transformed by _ALK_ fusions demonstrate cytokine-independent proliferation and are sensitive in vitro to crizotinib [9].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Literature Source** & **Falitit Age and** & \multicolumn{2}{c}{**Peripheral Blood and Marrow Description**} & \multicolumn{2}{c}{**Flow Cytometry Markeen**} & **ISCN** & **RT-PCR** \\ \hline  & & WEC 143.6 \(\times\) 10\({}^{7}\)/all & & & \\  & & 88.6\(\  
In this paper, we describe the case of an 18-year-old female with AML, who was found to have a somatic _RANBP2::ALK_ fusion. This is the first reported case of AML with this _ALK_ fusion _without monosomy_ 7. We describe her treatment and clinical course, including the use of crizotinib. Just as patients with _FLT3_-mutated AML have shown improved overall survival from the addition of targeted therapy (sorafenib and other kinase inhibitors), both with upfront chemotherapy and as post-HSCT maintenance therapy [14; 15], we believe crizotinib used at these same timepoints will benefit these rare patients with _ALK_-fusion AML.