esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Send Rate and Throughput  
_Send rate_ refers to the rate at which transactions can be submitted to the network for processing and it is measured in transactions per second (TPS). In contrast, _throughput_ refers to the rate at which transactions can be successfully processed by a blockchain network within a given time period, typically measured in transactions per second (TPS).  
In the case of the number of \"assets\", we can observe a decrease in the send rate as well as in the throughput proportional to the increase in the number of \"assets\" in both functions (i.e., _create_asset_, _read_asset_) (Figures 17a and 18a). However, even in the maximum value of 150 assets (Figure 18a), the simulation performs with a send rate of 91 TPS and a throughput of 89 TPS which can be considered an acceptable value.  
Figure 16: Network Latency (s), (**a**) per “asset” (**b**) per “worker”, for _read_asset_.  
In the case of the \"workers\" parameter, we observe that the value of the send rate and throughput is increased up to 110 TPS and then reached the value of 105 TPS, in the maximum number of the \"workers\" parameter (i.e., 20), in the case of _read_asset_ function (Figure 18b). On the other hand, we observe a decrease in throughput while increasing the number of \"workers\" in the _create_asset_ function (Figure 17b). We can conclude that, while reading the assets on the ledger, by increasing the number of the \"workers\" parameter, we can increase the throughput, while distributing the workload more evenly and increasing the total number of transactions that can be processed in a given time period.  
Overall, the performance evaluation has demonstrated satisfactory results in terms of send rate and throughput, compared to a work on the performance evaluation of HF for use in IoT [38], in both cases (i.e., _create_asset_, _read_asset_) and we can conclude that the proposed HF architecture is capable of handling a significant amount of traffic without any distinguishable performance degradation and, thus, being suitable for the case of IoMT-based health monitoring systems.  
Figure 17: Send rate (TPS) and throughput (TPS), (a) per “asset” (b) per “worker”, for _create_asset_.  
Figure 18: Send rate (TPS) and throughput (TPS), (a) per “asset” (b) per “worker”, for _read_asset_.
## 4 Results and Discussions
### Experimental Results  
In this chapter, we first performed ablation experiments to verify the effectiveness of each component of the model. Then, we compared the performance of DRFENet with traditional image denoising methods and different convolutional neural network models in gray datasets and color datasets, respectively. The traditional denoising method was BM3D [6]. The CNN-based methods we compared against were DnCNN [10], IRCNN [43], BRDNet [44], ECNDNet [31], and ADNet [29]. For all comparison methods, the model was derived from the code provided by the original paper or according to the original paper. We used the same dataset to train all networks to ensure fairness. Finally, in order to verify the difference between the model and other models, we used the analysis of variance (ANOVA) for statistical analysis of the proposed results.
## 1 Introduction  
Endoscopic endonasal surgery (EES) has become a common procedure for the removal of pituitary adenomas and tumors at the skull base [1]. Traditional open approaches, either transcranial or transfacial, require traumatic access through the patient's forehead or cheek. EES can significantly reduce invasiveness and improve surgery and recovery times with the benefits of less resultant trauma and fewer complications for the patient. EES is a mononostril or binosrtil technique performed via the insertion of an endoscope and long surgical instruments through the nostril orifices into the sphenoid sinus cavity (Figure 0(a)). One of the major limitations is the anatomic constraint imposed by the nasal cavity, which limits the access and maneuverability of these instruments. At the nostril, the range of motion is reduced from six to four degrees-of-freedom (DOF): translation along the shaft of the instrument (1 DOF), rotation around the translational axis (1 DOF), and limited inclination of the shaft pivoted through the nostril (2 DOF). As a result, some areas at the skull base would be difficult or almost impossible to reach. Furthermore, hand-eye coordination is another major challenge with a two-dimensional endoscopic view that is not aligned with the surgeon's axis of view and mirrored movement of the shaft of the surgical tools against the surgeon's hand motion. As a consequence, surgeons experience a significant steep learning curve to master EES techniques [2].  
Current advances in the development of robotic-assisted technologies for surgery have proven to be effective in reducing the patient trauma and hospitalization time by preventing the risk of complications [3]. Robotic systems can enhance the surgeon's capabilities with improved precision and safety during the operation, improving ergonomics and decreasing surgeon's effort. However, EES remains a challenge in surgical robotic applications [4] with few systems targeting EES.
# Sentence-Level Insights from the Martian Literature: A Natural Language Processing Approach
## 3 Results
### Topic Modeling  
Figure 5 presents the results of further topic modeling using _BERTopic_, building on the top ten clusters identified in Figure 3.  
The initial clustering divided the corpus into ten relatively distinct topic groups. To further explore the internal structure of each cluster, this study adopts a unit-based approach, treating each cluster as an independent analytical unit and applying additional topic modeling within each group. This approach effectively compensates for potential omissions of secondary topics in the overall summary and enhances the hierarchical structure and completeness of the topic analysis. For example, within Cluster 1, \"Impact of Solar Events on the Martian Ionosphere\", more specific subthemes were identified, including \"Martian Ionosphere and Solar Interaction and Solar Flares\" and \"Their Impact on Mars\". Additionally, important subtopics that were not highlighted in the original summary, such as \"Global Dust Storms and Atmospheric Effects\" and \"Dust Storms and Their Atmospheric Effects\", were also uncovered. Finally, word clouds were generated based on keyword frequency to visually present the core vocabulary and their relative importance within each topic, further enhancing the interpretability of the modeling results.  
The modeling results indicate that Cluster 1 and Cluster 4, \"Mineralogical History and Past Environments of Mars\", exhibit the most complex internal structures, characterized by a high degree of subtheme diversity and semantic differentiation. Specifically, Cluster 1 encompasses multiple subfields, including the ionospheric response to solar activity, ion escape and electron density variations, plasma dynamics, and atmospheric disturbance processes. Collectively, these studies highlight the multi-scale linkages between microscopic particle interactions and macroscopic climate disturbances. Cluster 4 is organized into subthemes addressing crustal mineral composition, sedimentary environment evolution, regional analyses (e.g., Jezero Crater), and meteorite correlation studies, emphasizing the importance of mineral genesis and paleoenvironmental reconstruction in assessing early habitability.
# Antiviral Filtering Capacity of GO-Coated Textiles
## 3 Results
### Anti-Viral Effect of GO Covered Tissues  
To study if tissues coated with different concentrations of GO (GO-TxT) were able to shield viruses, we evaluated the filtering effect of GO-covered textiles on a model of infection of U373 glioblastoma cells by HHV-6A. This cell line has previously been shown in our laboratory to be efficiently infected by HHV-6A [19]. Textiles coated with different amounts of GO were able to reduce HHV-6A infection of U373 cells in a dose-dependent fashion. This was evaluated by a q-RT-PCR, a quantitative assay showing a slight reduction of viral copy number infecting U373 cells with the tissue without GO (Figure 6 bar 2). The viral copy number decreased by 90% with GO-T 0.5%, compared to the un-coated control (Figure 6 bar 3), and by 98% with GO-T 5% and 95% with GO-T 10% (Figure 6, bars 4 and 5, respectively). Therefore, GO was able to prevent the virus from infecting cells placed in the lower wells of plates also at low concentrations. It should be noted that the antiviral capacity of the GO-treated textile was even more efficient than 0.2 \(\upmu\)m filters (Figure 6, red bar 6) that are routinely used to filter viruses of this size. These results mirror SEM observations shown in Figure 5. No significant toxicity, as determined by the trypan blue exclusion test (data not shown) was found in U373 cells, suggesting that GO/TxT could be safe and used to increase the protection capacity of DPI. The GO-TxT textile was used soon after its production and future studies will clarify the stability of GO bound to this type of textile.  
Figure 5: SEM microscopy of GO coated textiles. (**A,B**) Upper filter surface, (**C,D**) lower filter surface (**A,C**) SEM image of 5% GO coated fibers, arrows point at scattered viral particles aggregates on the upper and lower surfaces of the filters. (**B,D**) SEM image of 10% GO coated fibers, arrows point at viral aggregates bound on the upper and lower surfaces of the fibers.
# Design and Implementation of A Shape Shifting Rolling-Crawling-Wall-Climbing Robot
## 3 Mathematical Model
### Quaternion  
In this section, the quaternion [16; 17] is defined as  
\[q_{0}+q_{1}\mathrm{i}+q_{2}\mathrm{j}+q_{3}\mathrm{\hat{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{   }}}}}}}}}}}}}}= \left[q_{0},q_{1},q_{2},q_{3}}\right]^{T}\] \[= \left[q_{0},\mathbf{u}\right]^{T}\] \[= \boldsymbol{\theta}\] \[\mathbf{u}:= \left[q_{1},q_{2},q_{3}}\right]^{T}\]  
and the quaternion norm is defined as \(|\boldsymbol{\theta}|=\sqrt{{q_{0}}^{2}+{q_{1}}^{2}+{q_{2}}^{2}+{q_{3}}^{2}}\).  
The product of quaternions \(\boldsymbol{\vartheta}_{a}\) and \(\boldsymbol{\vartheta}_{b}\) can be represented as,  
\[\boldsymbol{\vartheta}_{a}\circ\boldsymbol{\vartheta}_{b}=\left\{q_{0a}q_{0b }-\mathbf{u}_{a}\cdot\mathbf{u}_{b},q_{0a}\mathbf{u}_{b}+q_{0b}\mathbf{u}_{a} +\mathbf{u}_{a}\times\mathbf{u}_{b}\right\}\]  
where the operator \(\circ\) means the product for the quaternions. The operators \(\cdot\) and \(\times\) represent the inner product and outer product of the quaternions.  
The conjugate quaternion \(\boldsymbol{\vartheta}^{*}\) is represented as,  
\[\boldsymbol{\vartheta}^{*} = \left\{q_{0},-\mathbf{u}\right\}\] \[= \left[q_{0},-q_{1},-q_{2},-q_{3}}\right]^{T}\]  
and the inverse quaternion \(\boldsymbol{\vartheta}^{-1}\) is represented as  
\[\boldsymbol{\vartheta}^{-1}=\frac{\boldsymbol{\vartheta}^{*}}{|\boldsymbol{ \vartheta}|^{2}},\]  
The inverse quaternion gives \(\boldsymbol{\vartheta}\circ\boldsymbol{\vartheta}^{-1}=\boldsymbol{\vartheta }^{-1}\circ\boldsymbol{\vartheta}=\boldsymbol{I}_{q}\), where \(\boldsymbol{I}_{q}\) can be in the form of \([1,0,0,0]^{T}\).  
A quaternion for expressing rotation can be represented as,  
\[\boldsymbol{\vartheta}(\theta,\boldsymbol{n})=\left\{\cos(\theta/2), \boldsymbol{n}\sin(\theta/2)\right\}\]  
where \(\boldsymbol{n}\) can be a unit vector of the rotation axis, \(\boldsymbol{n}\in\mathbb{R}^{3}\), \(|\boldsymbol{n}|=1\), and \(\theta\) is a rotation angle of the unit vector.  
A transformed position vector is derived by performing the following calculations.  
\[\boldsymbol{x}^{\prime}=\boldsymbol{\vartheta}\circ\boldsymbol{x}\circ \boldsymbol{\vartheta}^{-1}\]  
where \(\boldsymbol{x}\) is a position vector that is written as \(\boldsymbol{x}=[0\;x\;y\;z]^{T}\). Note that the inverse of a quaternion for rotation yields \(\boldsymbol{q}^{-1}=\boldsymbol{q}^{*}\). On the other hand, the transformed coordinate space is derived by performing calculations as follows:  
\[\boldsymbol{x}=\boldsymbol{\vartheta}^{-1}\circ\boldsymbol{x}^{\prime}\circ \boldsymbol{\vartheta}.\]
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 4 The Immersive Video ME App
### The i-EVS ME App
#### 4.1.1 VTU VNFC  
The VTU, shown in Figure 8, provides three basic functionalities:  
* Video transcoding capabilities;
* Video streaming capabilities;
* System monitoring functionalities, to support the system maintenance process.  
The VTU can convert video streams from one format to another, through transcoding, transrating, and scaling operations (below, the term \"transcoding\" includes transrating and scaling operations) [24]. Depending on the required service, the source video sequence could reside as a pre-recorded file on a storage system, or come as a real-time packet stream. The requested transcoding service could be mono-directional, as in video stream distribution applications, or bi-directional, as in real-time videoconferencing [24].  
The VTU supports the most popular video codecs, such as H.264, H.265, and VP8/VP9. The VTU takes advantage of the potential presence of NVIDIA GPUs to improve overall performance and efficiency.  
Video streams published by users are recorded by the VTU and saved in a distributed storage system (eSTORE), to be shared at a later time. In this way, pre-recorded video contents can be streamed to single users, to an entire group, or to all the users.  
Figure 8: The i-EVS VNF architecture.  
The VTU can also offer system managers monitoring data, to monitor the VTU status and performance during service, collecting data to a local database [24].
# Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations
## 1 Introduction
An MCU establishes a single TCP connection (sub-flow) through each interface, forming two simultaneous TCP connections to exchange data among the substations (where different data packets are sent via each sub-flow). The objective behind utilizing MPTCP and hybrid communication technologies is to improve the overall throughput, reduce latency, and increase reliability. Indeed, aggregating the bandwidth of hybrid communication networks (e.g., WiFi and cellular 3G/4G) increases throughput and reduces the overall time needed to deliver a certain amount of data. Furthermore, in case high throughput is not a requirement, reliability is ensured as one interface can act as a redundant link at the lower communication layers, whereas the TCP protocol acknowledgment mechanism guarantees packet delivery at the upper layers. The contributions of this paper are two-fold. First, we propose an HWN-based communication architecture for smart grid distribution networks that can achieve reliable and fast communication between primary and medium/low-voltage secondary distribution substations. We are interested in a hybrid communication architecture that relies on sending time critical grid information distributed over two different wireless network technologies. One is a contention-free licensed network that is based on a per-use cost structure such as 3G/4G networks. The other is a contention-based network running in a license-free industrial-scientific-medical (ISM) band that can be owned by the utility operator. Second, the performance of the proposed architecture is comprehensively studied by extensive computer simulations in realistic scenarios. The study addresses the performance gain and the effect of essential parameters (such as MPTCP maximum congestion window size, packet loss ratio,data rate, and burst size) on the overall link throughput and data transfer latency. It is worth noting that we do not address the tuning of MPTCP's internal mechanisms; rather, we evaluate the overall HWN performance under different operational parameters to validate its suitability for distribution substations' inter-communication requirements. The rest of the paper is organized as follows. Section 2 reviews existing research works that are related to reliable communication architectures for distribution systems in smart grids. Section 3 introduces our system model from the viewpoint of future power distribution systems. Section 4 presents the proposed communication architecture and provides a brief overview of the MPTCP protocol. The performance of the proposed architecture is investigated in Section 5. Finally, Section 6 concludes the paper.
# Security Audit of a Blockchain-Based Industrial Application Platform
## 3 Attacker Goals, Resources, and Capabilities
### Attack Classification
#### 3.3.1 Infrastructure Attacks  
This section describes attacks on infrastructures and divides them into the subcategories of software and hardware and the attack groups.  
Infrastructure--Hardware  
* _Node tampering_ refers to the act of physically modifying a device [36] or communication link. The business logic of an application is a target for parameter manipulation [37]. In this attack, the hidden or fixed file is used by programmers for a specific operation. Data manipulation is the practice of intentionally changing (destroying, manipulating, or editing) data.
* _Malicious code injection_: code injection is the exploitation of vulnerabilities in a computer system that leads to the execution of unwanted code in the system. The intruder can use injection attacks to change the behavior of the program in any direction [38].
* _Sleep denial attack_: the attacker keeps the battery-powered devices awake by feeding them with false inputs. This leads to the exhaustion of their batteries, causing them to shut down [39].  
Infrastructure--Software
* _Malware: adware, spyware, Trojan, virus, and worms_: the IoT devices can be infected with malware that can spread to the cloud or data centers [40]. Using malware, an attacker can infect the system to manipulate data or steal information or even launch flooding attacks.
* _Timejacking_: an attacker typically changes the node's network time by including as many peers as possible and sending false timestamps on the network. This causes other peers to speed up and isolates the target from the network without interference from authentic nodes [41].
## 4 Multiple-Beam Interferometer for _Cep Drift_ Measurement
Practically, the spacing of the spectral fringes, or more precisely their deviation from equidistance, is affected by the _GDD_ of the multilayer mirrors [99], the ambient air of the interferometer [100] and beam splitters forming the cavity. Small residual _GDD_ contributions can be removed from the phase extraction by suitable calibration as long as they are static. Therefore, linear interference in a single ring resonator suffices, in principle, to completely characterize the _CEP drift_ of a pulse train. All required information is readily retrieved from the measured spectral interference pattern via Fourier processing [64]. Due to the independent thermal drift of the laser oscillator and the _MBI_, at least the optical path length of _MBI_ has to be carefully stabilized. In the developed device [101], it is achieved with a frequency-stabilized He-Ne laser. The half of _cw_ beam follows the exact same path like the ultrashort pulses (but in the opposite direction) and interferes with the other half of the beam. The interference fringes are deflected directly to the _CCD_ detector. The recording and fast evaluation of the pattern drives the piezo translator to compensate for the eventual change of the optical path. The recorded path length variation is showed in Fig 12.b. For the sake of an easy comparison with _CEP noise_ of the ultrashort pulses, the actual length values are converted into optical phase noise at 800 nm, and over an 8-h period, a noise of 68 mrad _RMS_ was measured. This type of stabilization can dramatically decrease the noise in _CEP drift_ detection. A comparative study showed exceptional agreement with the results obtained from the _f_-to-_2\(f\) technique, while demonstrated 120 mrad _RMS_ noise of the _CEP drift_ provided by the _MBI_ method [98]. Figure 12: Schematic layout for the linear detection of the _CEP drift_ (**a**). The variation of the actively stabilized path length provides the accuracy of the _CEP_ measurement at 800 nm (**b**). The significance of this technique was emphasized by the _CEP drift_ measurement of a picosecond laser with less than 0.2 nm bandwidth [102]. An ultrahigh finesse, long Fabry-Perot cavity, designed for Compton backscattering applications, exhibited severe sensitivity to the instabilities of the frequency comb. Its error signal was related to the carrier-envelope offset frequency, the equivalent quantity for _CEP drift_ in the frequency domain. The _MBI_ was equipped with an ultrahigh resolution spectrograph, and recorded the _CEP drift_ simultaneously. When the repetition rate of the oscillator was manually varied, the error signal for the Fabry-Perot cavity and the _CEP drift_ measured by the _MBI_ showed very good agreement, which is an undeniable proof of the capability of the _MBI_ method to detect the _CEP drift_ independently from the spectral bandwidth of the laser.
# Chord Recognition Based on Temporal Correlation Support Vector Machine
## 4 Automatic Chord Recognition
### Viterbi Algorithm in SVM  
The SVM method recognizes the chord based on frame-level classification without considering the inter-frame temporal correlation of chord features. For multiple frames corresponding to the same chord, the recognition results of traditional SVM are independent and fluctuate. Accounting for the inter-frame temporal correlation in the recognition procedure can improve the overall chord recognition rate. Our system combines SVM with the Viterbi algorithm to introduce the temporal correlation prior to a chord. Suppose the system has hidden \(K\) states, and we denote each state as \(S_{i},\ i\in[1:K]\) where the state refers to the chord type. The observed events are \(Q_{t},\ t\in[1:T]\), which are PCP features. The current observed chord feature is \(Q=\{Q_{1},Q_{2}\ldots,Q_{T}\}\,,\ t\in[1:T]\). \(A_{ij}\) represents the transition probability from chord \(S_{i}\)  
In our method, we set the initialization observation probability \(\Pi_{i}\) to \(1/24\). The observed events are PCP features \(y_{t}\), where \(y_{t}\) is the PCP feature of the \(t^{\text{th}}\) frame. Generally, SVM predicts only the class label without probability information. The LIBSVM implementation extends SVM to output the probability estimates. The current observation probability corresponds to the probability estimates of SVM and replaces the \(P(Q_{t}|S_{i})\) in the Viterbi algorithm. \(S_{i}\) represents the chord \(i\in[1:K]\), where \(K\) is the number of chords and is set to 24.  
Figure 6 is the comparison of the ground truth chord and estimated chord for the Beatles song \"Baby It\"s You\". The top figure shows the result of using the SVM method to recognize the chord and the bottom figure uses TCSVM. The ground truth chord is represented in pink and the estimated chord labels are in blue. Figure 6 indicates that the estimation is more stable when using TCSVM.  
Figure 6: Comparison of ground truth and estimated chords using SVM (**top**) and TCSVM (**bottom**).