esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 4 ML for Outlier Detection
### Lof  
LOF [35] is an algorithm capable of detecting outliers based on the local deviation of a given point in regard to its positioning to its neighbours.  
It is, therefore, also a density-based algorithm that shares with DBSCAN the concepts of core distance and reachability distance. LOF can consider nearby local neighbourhoods and not the global distribution of the data. Outliers are detected as points that have a low density in comparison to the rest of the neighbourhood.  
LOF is known to perform better when datasets have a variable density (clusters of variable density). LOF depends on how isolated a point is from its neighbours.  
LOF is based on the principles, illustrated in Figure 2, namely, the k-distance of an object p from a point o and the k-distance neighbourhood of p, \(N_{k}\), as provided in Equation (5).  
\[N_{k-distance(p)}(p)=\{q\in D\setminus\{p\}|d(p,q)\leq k-distance(p) \tag{5}\]  
\(N_{k-distance(p)}\) can be greater than k since several objects can have an identical distance to o. The range distance \(rdist(p)\) of p with respect to a point o is provided in Equation (6):  
\[rdist(o,p)=maxdist(o,p),kdist(p) \tag{6}\]  
Then, the local range density of p is provided in Equation (7):  
\[lrd(o)=|R(o)|/(\sum_{p\in R(o)}rdist(o,p)) \tag{7}\]  
where:  
\[R(o)=\{p|dist(o,p)<kdist(o) \tag{8}\]  
The local outlier factor for o is computed via Equation (9):  
\[\mathit{log}(o)=\frac{\sum_{p\in R(o)}lrd(p)}{|R(o)|}/lrd(o) \tag{9}\]  
or better applied to our case:  
\[\mathit{LOF}_{MinPts}(p)=\frac{\sum_{\pi\in N_{MinPts}(p)}\frac{Lrd_{MinPts}( o)}{Lrd_{MinPts}(p)}}{|N_{MinPts}(p)|} \tag{10}\]  
Figure 2: LOF principles: k-distance of an object p from a point 0 and k-distance neighbourhood of p, \(N_{k}\), and range distance (rdist) (p).  
The lower the local reachability density of o and the higher the local reachability density of o's k-NN, the higher the value of LOF. Given a set of points, LOF computes the potential of each point being an outlier.  
LOF is, therefore, capable of capturing local outliers whose local density is relatively low compared to the local densities of its k neighbours.
## 3 Results
### Climatologies and Differences of CLARA-A2 and ISCCP-H
These differences were subsequently reduced as the number of satellites contributing to ISCCP increased. Therefore, it appears that a similar behavior is still present in the ISCCP-H data, as corroborated by global results [22]. Another distinguishable feature of the TCC over Europe is a local minimum in 2003 which has been related to a very hot and dry summer in Central Europe [16] and can be clearly noticed for CLARA-A2, but is less pronounced for ISCCP-H. Even if the 29-year study period is long enough for estimating trends, we do not attempt to do this, because long-term variations should be treated with caution. Most importantly, the satellite constellation changes over the years, and in addition most of the AVHRR-carrying satellites have a changing local overpass time, also referred to as orbital drift. These features can cause misleading artificial trends. Considering the seasonal variation of CLARA-A2 and ISCCP-H, there is a well distinguishable similar seasonality for the two datasets (Figure 3b), which present the largest TCC values during the winter months and the lowest during summer in accordance to the usual meteorological conditions in the European region [16]. However, the amplitude of the annual cycle of ISCCP-H is considerably smaller than the one of CLARA-A2, mainly attributed to seemingly greater minimum summer values (by up to 10%) and secondarily to smaller maximum winter values (by up to 5%). ISCCP-H shows quite constant TCC values during winter and early spring, resulting in a flat pattern in Figure 3b which is not reproduced by CLARA-A2. The average TCC values computed for CLARA-A2 vary from about 41% in August to 69% in December, whereas the corresponding values of ISCCP-H have shorter range from 50% in August to 65% in December. ISCCP-H gives lower TCC values during local winter and higher for the other months of the year with respect to CLARA-A2 causing the overall systematically higher values shown in Figure 3a. In addition, the biggest differences take place during the summer months, where ISCCP-H overestimates CLARA-A2 by about 9% on average. The underestimation of the winter TCC of ISCCP-H with respect to CLARA-A2 is significantly less intense than the summer overestimation, with a mean difference of \(-3\%\). The aforementioned seasonal differences between ISCCP-H and CLARA-A2 are also confirmed by the results obtained on a pixel-level basis (see Supplementary Figure S1) which display widespread negative differences (up to \(-15\) to \(-20\%\)) in winter and positive differences (up to \(+20\) to \(25\%\)) in summer spread over almost the entire study region.
# Single-Shot Temporal Contrast Enhancement Measurement of a Plasma Mirror by a Chirped Pulse
###### Abstract  
We focused on a single-shot method for directly measuring the temporal contrast enhancement of a single plasma mirror by analyzing the spectrum of a chirped pulse spatiotemporally overlapped with the igniting laser used for generating a plasma mirror. Experimentally, temporal contrast enhancement of \(10^{2}\) by one plasma mirror was successfully measured in a hundred picosecond timescale and was consistent with the theory. This single-shot measurement method caused no degradation on the performance of the plasma mirror, which was proved by monitoring the efficiency and far-field pattern of the igniting laser after the plasma mirror. Combined with calorimeters and CCD cameras, this method is expected to realize the single-shot online diagnosis of plasma mirrors. This method is expected to be an efficient approach for measuring the temporal contrast enhancement of the plasma mirrors.  
temporal intensity contrast; plasma mirror technique; pulse chirp; ultrashort pulse 40/1.
# Quality of Service Based NOMA Group D2D Communications
## 2 System Model
### Spatial Distribution of D2D Users  
Consider that D2D users are randomly distributed over \(\mathbb{R}^{2}\). At any time realization, the D2D users are classified as transmitters or receivers. We consider the group D2D scenario, where each DT is communicating with multiple DRs via a NOMA scheme. We refer to a DT communicating to multiple D2D devices as a group transmitter (GT). Any D2D user can take a role of GT. We assume that the selection of GTs is performed by a BS, and it can select multiple GTs in a given cell to improve overall system capacity. We allow multiple GTs to exist in proximity of each other, where each GT is communicating to its own group of receivers. Hence, at any time realization, each selected GT forms a group/cluster containing DRs. In order to capture both inband and device clustering, we model the spatial topology of the GTs by a stationary and isotropic GPP defined on \(\mathbb{R}^{2}\), denoted by \(\Phi_{\text{GT}}\) with parent process intensity \(\lambda_{\text{GT}}\). Furthermore, we model the coverage of each GT by a disc \(D\) with radius \(R_{D}\). We consider that the DRs are clustered around each GT and are uniformly distributed inside coverage of GTs. An illustration of NOMA group D2D communication is presented in Figure 1. For a quick reference, a list of commonly used abbreviations is given in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline D2D & Device-to-device \\ DR & D2D receiver \\ DT & D2D transmitter \\ F-NOMA & Fixed NOMA \\ GPP & Gauss–Poisson process \\ GT & Group transmitter \\ NOMA & Non-orthogonal multiple access \\ PPP & Poisson point process \\ QoS & Quality of service \\ SIC & Successive interference cancellation \\ SNR & Signal to noise ratio \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of commonly used abbreviations.
# Object-Level Semantic Map Construction for Dynamic Scenes
## 5 Discussion
### Object Instance Segmentation and Association  
The object instance segmentation provides the data foundation for 3D object tracking and recognition of the dynamic part in the current frame. Inaccurate instance segmentation will lead to object motion detection errors and inaccurate camera pose estimation. For example, as the geometric shapes of potted plants are irregular, the object mask generated by the Mask-RCNN [6] tends to have the overall outline of the periphery of the object, so the interior contains some pixels that are not the potted plants. As the camera perspective changes, the pixels that are not potted plants will change, and the 3D tracking results will inevitably produce errors. Thus, potted plants may be treated as dynamic objects. Therefore, a fusion segmentation algorithm combining geometric and semantic cues is used to segment the object instance precisely in the current frame, which guarantees reliable object motion detection and camera pose estimation.  
Our strategy for finding the dynamic part in the current frame involves utilizing the raw data for the current frame raw data and 3D object tracking to update the motion status of each object in object database. Then, the correlation between the object instance segmentation results and object database is established by using \(T_{rou}\). Finally, the dynamic part in the current frame is identified accurately according to the object motion information in object database.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm  
In CenTri, the BSs of both macrocell and small-cell layers collaborate to perform the route selection. A _proactive_ link selection mechanism is deployed in the control plane and a _reactive_ route selection mechanism is deployed in the data plane.  
Figure 3 presents the route selection mechanism of the RL model, which serves as the decision-making engine. The CC in the control plane selects routes based on factors with less dynamicity (i.e., a lower number of intermediate nodes and delay); while the nodes in the data plane (i.e., the source node \(fc_{s}\)) select routes based on the priority levels of D2D communication (or with less PU interference). The amount of the delay is higher in D2D routes, so the _distributed RL model_ embedded in the BSs and nodes of small cells aims to improve the routing decision made by the CC and select routes with lower PU activity levels and the number of intermediate nodes.  
Figure 3: Route selection decision made by the source node based on RL.  
The CC establishes backbone routes in order to provide an always available route for small-cell nodes. The backbone routes are shared among BSs in the data plane and, subsequently, the BSs share the backbone routes with source nodes. The distributed RL model determines the traffic intensity of available routes and establishes routes with the least traffic congestion via traffic offloading. BSs in small cells are part of the backbone routes from the macrocell, and so they inform the CC about their usage of white spaces. Routes are assumed to be disjointed in this paper for simplicity. The rest of this section presents the RL models and algorithms for CenTri.
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 5 Evaluation  
We implemented the method of this paper and released it as a tool called ColluDroid. Then, we applied some experiments to evaluate the performance of ColluDroid. Our experiments are performed on 5000 benign apps that we downloaded from Google Play in late 2017 and 5000 malicious apps which is an extended dataset based on Drebin [8].  
As ColluDroid framework can integrate different machine learning methods, we take SVM with Linear Kernel and Sec-SVM (Section 4.1.2) as representatives to evaluate the performance. Thus, under the ColluDroid framework, we have two methods to evaluate, namely ColluDroid-Linear SVM and ColluDroid-Sec-SVM. In order to demonstrate the effectiveness of ColluDroid framework, we also evaluate the original Linear SVM and Sec-SVM for comparison. In the following sections, we performed a few experiments and attempted to answer the following three research questions: (1) Can ColluDroid detect the collusion apps effectively? (2) Among the four tested methods, which one has the best performance against collusion and evasion attack? (3) What is the efficiency of ColluDroid? The next sections address each research question in details.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 3 NOMA Code-Separated Techniques
### Development of NOMA Technology  
Power division (power domain NOMA) and code division (code domain NOMA) are the two basic kinds of NOMA schemes. In the first case, channel users may broadcast at different decibel levels. Code division NOMA does not use orthogonal code sequences to partition subscriber signals, unlike traditional CDMA systems, which have a capacity proportionate to the number of code sequences. Successively adopting non-orthogonal codes increases the number of subscribers, but it requires expensive algorithms for multi-user reception. A network's NOMA code division system divides shared time and frequency resources by subscriber code sequence. In contrast to CDMA, non-orthogonal sequences with a low correlation coefficient and many zero elements are used to separate subscribers [10].  
ANOMA system has a far higher subscriber-to-signal base ratio than a traditional CDMA system. NOMA technology with code division includes orthogonal frequency division multiple access (OFDMA), code division multiple access (CDMA) with low density spreading (LDS), and sparse code multiple access (SCMA). LDS-CDMA uses a lower-density code sequence than CDMA. The information symbols it sends are superimposed on LDS sequences before transport on orthogonal OFDM carriers in low-density OFDM (LDS-OFDM) [10]. By broadcasting more symbols than orthogonal carriers, the communication system's spectral efficiency can be enhanced compared to a typical OFDM system. Huawei's sparse code sequence algorithm (SCMA) improved LDS-CDMA [10]. Unlike LDS-CDMA, SCMA directly converts information bits to sparse code words using sophisticated code sequences. We can expect better characteristics by increasing receiving end processing complexity. The lattice partitions multiple access (LPMA) system, which uses lattice codes, is another NOMA-related multiple access approach [10].  
Figure 1: NOMA system transmitting side structure based on 5G standard.  
Other promising NOMA systems are SDMA and PDMA. To prevent cross-user interference, PDMA systems multiplex and transmit subscriber data streams using non-orthogonal templates. PDMA can be used with other multiple-access methods since it can multiplex in both the spatial and coding domains [10]. Using a software-configurable ad hoc interface for 5G systems, code-separated NOMA can be implemented [11]. This idea ensures 5G network modes and services are supported by diverse access strategies. In the Internet of things mode, shared multiple access (MUSA) schemes enable mass subscriber device connectivity in 5G systems [12]. The idea of a software-configurable radio interface for 5G systems was proposed to accommodate the many modes and services of 5G networks, such as the various types of NOMA with code division detailed below [13]. 5G networks must use multiple access joint (MUSA) technologies to connect subscriber devices in the Internet of things mode [13].
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 3 Results and Analysis
### Kirigami Cuts Density vs. Craving Distance  
#### 3.3.1 Experimental Setup  
To investigate the impact of kirigami structure on locomotion, a systematic experimental study was conducted to evaluate how varying the kirigami cut density influences the crawling efficiency of the soft robot. The density of the kirigami structure was modified by altering the unit lattice length (\(l\)) and adjusting the number of unit cells within the fixedarea. These two parameters are inherently interrelated; a higher density is achieved by reducing the unit length while increasing the number of cuts within a fixed surface area. For this study, four distinct density configurations were fabricated using triangular and trapezoidal kirigami patterns, all constructed from 100 \(\upmu\)m thick PET sheets. The tested configurations included 2 \(\times\) 14 units (I = 10 mm), 3 \(\times\) 15 units (I = 9 mm), 4 \(\times\) 20 units (I = 7 mm), 5 \(\times\) 28 units (I = 5 mm), and 6 \(\times\) 28 units (I = 4.8 mm). Each sample was subjected to five actuation cycles on a compliant polyurethane foam substrate to simulate crawling on soft terrain.
## 4 Evaluation of Experimental Results  
Table 3 contains the improvement factors of the examined surface roughness parameters (Ra, Rq, Sa, Sq) due to burnishing, indicating the technological parameters used for the experiments.  
Figure 4: The applied Kraljic matrices: (**a**) Surface roughness parameter matrix; (**b**) Improvement factor matrix.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & \(f[\frac{mm}{rec}]\) & \(\
u_{v}[\frac{m}{s}]\) & \(F_{v}[N]\) & \(IR_{a}[\%]\) & \(IR_{q}[\%]\) & \(IS_{a}[\%]\) & \(IS_{q}[\%]\) \\ \hline
1-1 & 0.05 & 0.6938 & 120 & 75.40\% & 74.82\% & 72.94\% & 74.00\% \\
1-2 & 0.05 & 0.6938 & 100 & 69.94\% & 68.73\% & 72.24\% & 71.37\% \\
1-3 & 0.05 & 0.6938 & 80 & 60.65\% & 58.07\% & 62.95\% & 59.94\% \\
1-4 & 0.05 & 0.6938 & 60 & 50.73\% & 48.33\% & 47.32\% & 44.88\% \\
2-1 & 0.1 & 0.6938 & 120 & 82.00\% & 80.96\% & 78.82\% & 77.80\% \\
2-2 & 0.1 & 0.6938 & 100 & 78.35\% & 75.53\% & 80.23\% & 77.70\% \\
2-3 & 0.1 & 0.6938 & 80 & 78.37\% & 74.55\% & 78.18\% & 74.61\% \\
2-4 & 0.1 & 0.6938 & 60 & 64.96\% & 63.08\% & 63.97\% & 60.53\% \\
3-1 & 0.05 & 0.9817 & 120 & 50.94\% & 52.37\% & 54.10\% & 50.10\% \\
3-2 & 0.05 & 0.9817 & 100 & 60.71\% & 57.33\% & 64.79\% & 60.53\% \\
3-3 & 0.05 & 0.9817 & 80 & 73.96\% & 72.92\% & 74.69\% & 73.92\% \\
3-4 & 0.05 & 0.9817 & 60 & 55.62\% & 53.90\% & 49.78\% & 45.12\% \\
4-1 & 0.1 & 0.9817 & 120 & 66.49\% & 64.44\% & 61.25\% & 58.31\% \\
4-2 & 0.1 & 0.9817 & 100 & 72.49\% & 69.02\% & 66.36\% & 62.77\% \\
4-3 & 0.1 & 0.9817 & 80 & 66.45\% & 59.85\% & 65.00\% & 57.75\% \\
4-4 & 0.1 & 0.9817 & 60 & 53.25\% & 47.12\% & 45.56\% & 40.18\% \\ \hline \end{tabular}
\end{table}
Table 3: Measurement results of the improvement factors for surface roughness metrics.
# Evaluation of Glottal Inverse Filtering Techniques on
## 3 Experiments  
The analysis carried out on the speech signals was based on a constant frame rate with a 50 ms Hanning window and a 50% overlap across the five considered GIF methods. To ensure accurate results, only the stationary part of the vowels was considered, excluding the initial and final 5% of the audio files. Furthermore, to ensure the continuity of the signal frame-to-frame, a processing method that preserved the initial and final memory state for every filter over the applied methods was applied. On the other hand, the estimation of GCIs, which play a key role in both QCP-based approaches and the calculation of the error metrics (detailed in Section 3.3), was obtained using a speech event detection technique called \(SEDREAMS\), which is based on the residual excitation and mean-based signal [35].  
The code used for the experiments can be found at this public repository: [[https://github.com/SpeechSalleBcn/inverse-filtering-evaluation](https://github.com/SpeechSalleBcn/inverse-filtering-evaluation)]([https://github.com/SpeechSalleBcn/inverse-filtering-evaluation](https://github.com/SpeechSalleBcn/inverse-filtering-evaluation)) (first version published on 23 June 2023).
###### Abstract  
Affective communication, encompassing verbal and non-verbal cues, is crucial for understanding human interactions. This study introduces a novel framework for enhancing emotional understanding by fusing speech emotion recognition (SER) and sentiment analysis (SA). We leverage diverse features and both classical and deep learning models, including Gaussian naive Bayes (GNB), support vector machines (SVMs), random forests (RFs), multilayer perceptron (MLP), and a 1D convolutional neural network (1D-CNN), to accurately discern and categorize emotions in speech. We further extract text sentiment from speech-to-text conversion, analyzing it using pre-trained models like bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 2 (GPT-2), and logistic regression (LR). To improve individual model performance for both SER and SA, we employ an extended dynamic Bayesian mixture model (DBMM) ensemble classifier. Our most significant contribution is the development of a novel two-layered DBMM (2L-DBMM) for multimodal fusion. This model effectively integrates speech emotion and text sentiment, enabling the classification of more nuanced, second-level emotional states. Evaluating our framework on the EmoUERJ (Portuguese) and ESD (English) datasets, the extended DBMM achieves accuracy rates of 96% and 98% for SER, 85% and 95% for SA, and 96% and 98% for combined emotion classification using the 2L-DBMM, respectively. Our findings demonstrate the superior performance of the extended DBMM for individual modalities compared to individual classifiers and the 2L-DBMM for merging different modalities, highlighting the value of ensemble methods and multimodal fusion in affective communication analysis. The results underscore the potential of our approach in enhancing emotional understanding with broad applications in fields like mental health assessment, human-robot interaction, and cross-cultural communication.  
speech emotion recognition; sentiment analysis; affective communication; data fusion; multimodality; machine learning; deep learning; dynamic Bayesian mixture model +
Footnote †: journal: Medical Image and Vision Computing  
**Citation:** [PERSON] [PERSON]; [PERSON]; [PERSON] Multimodal Affective Communication Analysis: Fusing Speech Emotion and Text Sentiment Using Machine Learning. _Appl. Sci._**2024**, 14, 6631. [[https://doi.org/10.3390/app14156631](https://doi.org/10.3390/app14156631)]([https://doi.org/10.3390/app14156631](https://doi.org/10.3390/app14156631))