esa-sceva/satcom-chunk-collection huggingface dataset:
# Unfolded Algorithms for Deep Phase Retrieval
## 2 System Model and Problem Formulation
Nonetheless, finding an optimal \(\phi^{\star}\in\Phi\) such that  
\[\mathds{E}\big{\{}\mathrm{D}(\mathbf{x},\mathcal{D}_{\phi^{\star}}(\mathbf{y};\mathbf{A}) )\big{\}}\leq\mathds{E}\big{\{}\mathrm{D}(\mathbf{x},\mathcal{D}_{\phi}(\mathbf{y}; \mathbf{A}))\big{\}},\quad\forall\phi\in\Phi\]  
is a particularly challenging task due to the non-convex nature of the problem. If such a characterization (\(\phi^{\star}\)) is known, the design of the sensing matrix can easily be carried out. The above observation is further evidence for the paramount importance of developing a unified framework that allows for a joint optimization of the reconstruction algorithm (i.e., finding an optimal or sub-optimal \(\phi^{\star}\)) and the sensing matrix. In the following, we consider the joint design of the sensing matrix and the signal processing algorithm and propose a novel framework based on the deep unfolding technique that allows for an optimization over the set of both design parameters (\(\{\Phi,\mathbf{A}\}\)). The resulting framework can be viewed as a hybrid data-driven, model-based deep neural network that achieves far superior performance when compared to existing model-based and data-driven methodologies. Furthermore, the obtained deep architecture is interpretable due to the incorporation of domain knowledge and assumes far fewer parameters when compared to existing black-box neural networks. Due to the fewer training parameters, the proposed architecture requires far fewer training samples for optimization of the network when compared to existing data-driven methodologies. We provide a high-level description of the proposed methodology and proceed deeper into the details of each module in the next section. We conclude this section by mathematically formalizing the problem at hand. We cast the problem of jointly designing the sensing matrix and the reconstruction algorithm as the following optimization problem:  
\[\min_{\{\mathbf{A}\in\mathbb{C}^{m\times n},\phi\in\mathbf{\Phi}\}}\ \ \mathds{E}\big{\{} \mathrm{D}\big{(}\mathbf{x},\mathcal{D}_{\phi}(|\mathbf{A}\mathbf{x}|;\mathbf{A}))\big{\}}. \tag{6}\]Our preliminary step is to mathematically formulate a proper parameterized class of the decoder and encoder modules that facilitates the incorporation of domain knowledge. To this end, we consider the realization of the decoder module by first formulating the problem of phase retrieval as an optimization problem; then, we resort to first-order optimization techniques that lay the groundwork for obtaining a rich class of parameterized decoder functions. Once such a class is formalized, we make the connection between deep neural networks and first-order optimization techniques and propose two novel model-based deep architectures that encode domain knowledge in their respective architectures and task-specific computations. Next, we show how to tackle the above program using prominent deep learning techniques by taking advantage of the available data.
## 4 Speed Guidance Strategy Considering the Uncertainty of Leading Vehicles
### Guidance Mechanism  
Based on vehicle speed, signal phase, signal cycle, and surrounding traffic environment information, recommended speeds or acceleration are provided to achieve speed guidance. The principles for improving traffic efficiency in this study are as follows:  
1. If the guided vehicle cannot pass through the intersection at its current speed (Figure 5, Principle 1), it should be accelerated if it can pass the intersection within the current green light phase by accelerating. Conversely, if the vehicle cannot pass through the intersection even at the maximum speed limit, it should be guided to decelerate and wait for the next green light phase. The vehicle should decelerate to an appropriate speed to ensure it can pass the intersection at the beginning of the next green light phase.
2. If the guided vehicle can pass through the intersection at its current speed (Figure 5, Principle 2), it should be accelerated to pass through the intersection at the maximum speed, ensuring driving safety. This acceleration guidance aims to maximize the intersection's throughput and allocate as much passing time as possible for subsequent vehicles.
# Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method
## 1 Introduction
In this approach, not only can the nonlinear data structure be preserved by applying a kernelized nonlinear mapping method, but also both \"locality\" and \"discrimination\" of data in an integrated modeling environment are considered simultaneously. It should be noted that this technique is supervised through direct connection with classification in order to properly guide the procedure of dimensionality reduction. In order to have a reliable and powerful comparison, the efficiency of the proposed SKLDNE technique was compared with PCA, KPCA, LDA, UDP, LPP, DNE, and LDNE by a broad range of experiments with different publicly available face datasets, i.e., Yale face, Olivetti Research Laboratory (ORL) face, Head Pose, and Sheffield. Moreover, Finger Vein and Finger Knuckle databases were also applied to investigate the implementation of our algorithm in other types of databases rather than faces. It is worthwhile to highlight several characteristics of the proposed approach here:  
1. SKLDNE has been successfully designed to retain local geometric relations of the within-class samples, which are very important for image recognition. Generally, the categorization strength of methods with a linear learning algorithm is restricted. They fail to deal with complicated problems. Many effective nonlinear data features may be lost during the classification progress using linear techniques such as LDNE, LDA, DNE, and LPP. Therefore, applying a nonlinear method can effectively improve the classification performance. 2. This technique is a supervised learning method, as the data scholar acts as a guide to instruct the main algorithm whose conclusion should be found. SKLDNE considers class label information of neighbors in which there is a direct connection with classification, in order to enhance final recognition performance. 3. It benefits from the advantages of \"locality\" in LPP in which, due to the prior class-label information, geometric relations are preserved. 4. Not only can it build a compact submanifold by minimizing the distance between the same points in the same class, but it also expands the gaps among submanifolds of distinct classes simultaneously, which is called \"discrimination.\"
5. SKLDNE can resolve the SSS problem that is mostly faced by other aforementioned techniques such as PCA, LDA, UDP, and LPP, and the \"overlearning of locality\" problem in the manifold learning. 6. Due to its kernel weighting, it is very efficient in reducing the negative influence of the outliers on the projection directions, which effectively handles the drawbacks of linear models and makes it more robust to outliers. The rest of this study is organized as follows: Section 2 categorizes LPP, DNE, and LDNE. Section 3 is devoted to describing the proposed SKLDNE method and the pertinent algorithm and mathematics. Section 4 focuses on the experiments and analyses carried out. Section 5 elucidates conclusions and future research.
# A Local Feature Engineering Strategy to Improve Network Anomaly Detection +
## 3 Background
### Intrusion Detection Systems
#### 3.1.2. Operative Placement  
A further classification of the IDSs has been made by taking into account their placement in the network to be protected. In this case, the literature classifies the IDSs into the following four categories:  
* _Host-based_[63]: A Host-based Intrusion Detection System (HIDS) operates by exploiting several hosts (Figure 2) with the aim to capture the network activity. Each new network event is compared to the information stored in a database (signatures), and, when an intrusion is detected, the system responds with a series of countermeasures. The main advantage of such a configuration is related to the system scalability in terms of possibility to add other hosts to improve the detection performance. The latency between an intrusion and its detection represents the main disadvantages, as well as the high number of misidentifications of normal (false positive) and intrusions (false negative) events.
* _Network-based_[64]: A Network-based Intrusion Detection System (NIDS) operates by capturing and analyzing the whole network activities (Figure 3). Each activity is then compared to the signatures stored in a database and related to known events (normal and intrusion), and only the unknown signatures will be analyzed. This hybrid approach (signature-based and analysis-based) allows us to deal both with known and unknown intrusions (e.g., zero-days attacks). The main disadvantage in this approach is instead related to its inability to operate proactively, together with that to operate with encrypted data.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Approach** & **Pros** & **Cons** \\ \hline \multirow{4}{*}{_Specification-based_} & Able to investigate and trace on the & Inability to detect attacks similar to normal \\  & protocol states; capability to detect abuses & network activities; high cost in terms of \\  & in the protocol usage & resources consumed to inspect and trace \\  & in the protocol state; it can not work by using \\  & devoted operating systems or devices \\ \hline \multirow{4}{*}{_Hybrid-based_} & It depends on the combination and & It depends on the combination and \\  & implementation of the approaches & implementation of the approaches \\  & described above & described above \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont_.  
Figure 2: IDS host-based modality.  
* _Network-node-based_[65]: A Network-Node-based Intrusion Detection System (NNIDS) operates by positioning the system in a strategic location of the network (Figure 4), and its operative way is a combination of the HIDS and NIDS strategies; thus, it shares with them the same advantages and disadvantages.
* _Distributed-based_[66]: The Distributed-based intrusion detection systems represent hybrid solutions aimed to combine the aforementioned strategies in order to improve the performance in a specific network context.
## 5 Statistical Analysis of the Protocols Based on Subsets of Responses and ECC
### Error Management in the Cryptographic Keys
#### 5.3.1 Error Management with Response-Based Cryptography  
The Response-Based Cryptographic (RBC) is a search mechanism allowing the recovery of a key when the ciphertext of the key is known [63; 64]. In the protocol of Algorithms  
Figure 9: Modeling the rate of collisions with longer responses. With 512-bit long responses, noise injection techniques containing BERs as high as 40% in the subset of responses are considered since they only induce small rates of collisions (on the order of \(10^{-6}\)).  
6 and 7 with the subset of responses, the key K decrypts M* (the ciphertext of the key Pk) followed by the decryption of C and the ciphertext of the file F. This can be written as:  
\[F=Decrypt(C,Pk)=Decrypt(C,Decrypt(M^{*},K)) \tag{7}\]  
\([Decrypt(C,Pk)\): decrypt \"C\" with key \"Pk\"]  
Algorithm 8 verifies authenticity and decrypts Pk and F, see as follows:  
```
1: Retrieve \(K\) with CRP mechanism and from file \(C\)
2:\(x\gets 0\)
3: Find all keys Km, \(n\in\left\{1,\binom{N}{x}\right\}\), with hamming distance of \(x\) from \(K\)
4: Decrypt all files \(Fn=Decrypt(C,Decrypt(M^{*},Kn))\)
5: If at least one of files \(Fn\) is readable; \(Pk=Decrypt(M^{*},Kn)\); \(F=Fn=Decrypt(C,Pk)\)
6: Else: \(\bigcirc\)  If x \(<\) 3: Increment \(x\) by 1 and go back to step 3 \(\bigcirc\)  If x = 3: Try again (go back to 1)
```  
**Algorithm 8**Managing erratic key \(K\) with RBC  
Using our computing system, we experimentally measured the throughput of the RBC at \(2.0\times 10^{8}\) cycles per second for AES and at \(2.0\times 10^{6}\) cycles/second for CRYSTALS-Dilithium. The code tested for Dilithium is freely available online, while AES is implemented in hardware within the computing element of our system [65]. The latencies to retrieve F through Algorithm 7 are listed in Table 4. The latencies to retrieve F are acceptable with AES, while managing 3 bad bits with Dilithium is slow. We anticipate that the hardware implementations of Dilithium will be available in the near future.
# MIDOM--A DICOM-Based Medical Image Communication System
## 2 Background
### Cbpc
#### 2.2.1 Predictive Model  
Prediction error is being calculated for each image element sequentially, starting from the leftmost element in the first row, to the last one, which is the rightmost element in the last row. To predict values of the current image element, a small number of neighbouring elements called prediction context is being used. An example of such context can be observed in Figure 1. The current element whose value is being predicted is marked with \(I(x,y)\).  
CBPC compression methods defines and implements multiple prediction functions, ranging from simpler to more complex ones [7]. Simpler functions usually return one of the element from the context:  
* **N**--returns value of the image element positioned **north** (up) from the current element.
* **W**--returns value of the image element positioned **west** (left) from the current element.
* **NW**--returns value of the image element positioned **northwest** (up-left) from the current element.
* **NE**--returns value of the image element positioned **northeast** (up-right) from the current element.  
On the contrary, more complex functions return non-trivial values calculated by using individual values from the context:  
* **GradW**--returns result of calculating \(2N-NN\).
* **GradN**--returns result of calculating \(2W-WW\).
* **L**-JPEG4--returns result of calculating \(N+W-NW\).
* **MED**--returns result according to the function displayed in Equation (1).  
\[\hat{I}(x,y)=\begin{cases}min(N,W)&\text{if }NW\geq max(N,W)\\ min(N,W)&\text{if }NW\leq min(N,W)\\ N+W+NW&\text{otherwise}\end{cases} \tag{1}\]
* **GAP**--chooses one of the previously mentioned functions based on the horizontal and vertical gradient around the current element.
* **FPG**--combines neighbouring elements based on the approximation of the gradients from the context.  
These functions are used in a complex process of image elements classification and correction of the predicted values. Final prediction error is acquired at the end of the modelling, finally being sent to the Golomb-Rice entropy coder.
## 7 Conclusions  
Following our previous studies concerning GeoSurf satellite constellations, in the present paper, we have studied the interference due to amplitude and phase distortions produced by rain in ultra-wideband communication systems designed for using double sideband-suppressed carrier (DSB-SC) modulation.  
For illustrating the general characteristics of linear interference due to rain in ultra-wideband channels, we have reported and discussed the results concerning radio links simulated with SST at Spino d'Adda (Italy), Madrid (Spain) and Tampa (Florida), which are sites located in different climatic regions. Numerical results have been obtained by means of time-domain simulations of the system.  
After reviewing the classical theory of passband (radio frequency) and baseband equivalent transfer functions of direct and orthogonal channels in DSB-SC modulation, we have calculated them when radio links are faded by rain.  
We defined the self-channel (ISI) interference due to the tails of the previous and following pulses and the interference coming from the orthogonal channel (QI). We have reached the following general conclusions:  
1. The three sites considered, although in different climatic zones, are practically indistinguishable in all cases.
2. The channel factor \(\mu\) can be either \(\mu<0\) (dB), therefore, \(\gamma<\Gamma\); or \(\mu>0\) (dB), therefore, \(\gamma>\Gamma\) with equal probability.
3. Histograms with only ISI or with ISI + QI are diverse.
4. With ISI only, the lowest range of attenuation (10 dB) shows more marked peaks in the relative frequency histograms at \(\mu=0\) dB and \(\mu\approx\pm 0.3\) dB. These peaks are largely smoothed when QI is added.
5. The roll-off factor \(r\) plays a small role because the peaks change only for \(r=1\). We have proposed a theoretical approach which can justify these findings.  
Figure 12: Capacity factor versus \(\Gamma\) (dB) at the extreme values of \(\mu\); namely, \(\pm 1\) dB. The experimental values of Figures 10–12 are within these bounds.  
In conclusion, the channel theoretical capacity loss, even in the worst case, is very limited and rain, therefore, does not cause significant linear distortions in ultra-wideband channels at millimeter waves in QPSK schemes.  
Future work should be conducted in simulating higher-order QAM ultra-wideband channels to assess whether these channels are also affected a little by linear distortions due to rain.  
Conceptualization, E.M., M.M. and C.R.; methodology, E.M., M.M. and C.R.; software, E.M., M.M. and C.R.; investigation, E.M., M.M. and C.R.; data curation, E.M., M.M. and C.R.; writing--original draft preparation, E.M., M.M. and C.R.; writing--review and editing, E.M., M.M. and C.R.; visualization, E.M., M.M. and C.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We wish to thank [PERSON], at NASA years ago, for providing the rain rate data of Tampa and we also wish to thank [PERSON], at the Universidad Politecnica de Madrid, for providing the rain rate data of Madrid.  
The authors declare no conflict of interest.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Resilience Capacity Decomposition for the Arctic Northeast Route
#### 3.3.3 Restorative Capacity  
The restorative capacity can be defined as the extent to which a specified system is able to recover from or repair a destroyed or failed function due to the impact of a disruption. Based on Figure 6, the restorative capacity acts as the last line of defense for the system to resist a disruption; that is, in the case that the absorptive and adaptive capacities fail to tolerate the impact caused by a disruption, the restorative capacity remains. Generally, it takes a relatively long time for the restorative capacity to recover the normal function of the system, and various human resources, services, tools, and so on are essential. Three aspects of the restorative capacity pertaining to the Northeast Route security system are described in Table 6.
## 2 Materials and Methods
### Quantitative Evaluation Factors  
#### 2.4.1 Evaluation Factors for the Noise Level  
In this study, noise leveling factors such as the coefficient of variation (COV) and contrast-to-noise ratio (CNR) were employed [23; 24]. ROIs were set to calculate the COV (using the target ROI) and CNR (using the target and background ROIs). The COV is an evaluation factor that quantitatively represents the noise in an image. A superior image has a low COV. In addition, the CNR is a factor that evaluates the contrast and noise of images in combination. A superior image has a high CNR. The COV and CNR are defined as follows:  
\[COV=\frac{\sigma}{\mu}, \tag{6}\]  
\[CNR=\frac{|S_{A}-S_{B}|}{\sqrt{\frac{1}{2}\big{(}\sigma_{A}^{2}+\sigma_{B}^{2} \big{)}}}, \tag{7}\]where \(\sigma\) is the standard deviation of the signal ; \(\mu\) is the mean value of the signal ; \(S_{A}\) and \(\sigma_{A}\) are the mean and standard deviation of the signal in the target ROI, respectively; and \(S_{B}\) and \(\sigma_{B}\) are the average and standard deviation of the background signal, respectively.
# Recognizing Teachers' Hand Gestures for Effective Non-Verbal Interaction
## 4 Experiments
### Experimental Results
#### 4.4.3 A Case Study in the Classroom Environment  
In order to verify the actual detection effect of the framework proposed in this study on small target gestures in the classroom environment, the teacher made different hand gestures according to normal habits, and a video was recorded when half of the body was visible. The teacher was required to make three different gestures on a freely movable pulpit according to categories of hand gesture in the classroom, respectively, symbolic gesture, descriptive gesture, and emotional gesture, as we mentioned before. We collected 60 images in total with 20 images for each gesture, and the image size was cropped to 320 \(\times\) 240 pixels. We used the framework proposed in this study to first detect the shape of hand gestures, and then to recognize the meaning of the gestures. The experimental results are shown in Figure 13. These figures are the running results of the algorithm proposed in this paper. When the gesture size in the image was small, the detection of the proposed framework was workable. The proposed framework can detect the shape of the gesture and generate a virtual gesture that is nearly identical to the real one, even if there is a skin-like background in the image. There were two images in the emotional gesture category incorrectly classified into the descriptive gesture category, whereas images of symbolic gesture and descriptive gesture categories were classified correctly. This is probably because it is not easy to distinguish the specific pedagogical meaning between a descriptive gesture and an emotional gesture. However, this preliminary result indicates that the framework proposed in this study can effectively detect gestures with different image background and gesture changes, which shows that the proposed framework has better usability and robustness for small target gestures.  
Given the recognition results of the proposed framework, we asked the teacher to what extent the results indicate their intention with respect to communication with students when they taught in the class. The teacher thought that the results were nearly consistent with his intentions, and felt that this kind of non-verbal cue seemed to improve the level of class management and teacher-student interaction. He could more fully describe class material and deliver his emotions, which could lead to more engagement in students.  
Figure 12: Confusion matrix of Marcel dataset.  
Figure 13: A sample extracted from the video.