esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Recompression Scheme for VQ Index Tables
## 2 Related Work
### Vector Quantization Algorithm  
VQ is a common and popular technique used in image compression. VQ was first introduced in 1984 [9]. There were many studies and much research conducted using VQ afterwards. The proposed scheme uses VQ as its fundamental compression method. VQ compression involves a few basic steps:  
Step 1: Codebook Training  
A well-trained codebook is constructed by the most representative codewords from a large-enough training set and according to a specified codebook size. There are many codebook training algorithms, but the most famous one is the Linde, Buzo and Gray (LBG) algorithm presented in 1980 [11]. The LBG algorithm partitions the training set into subsets and iteratively refines the desired codebooks by utilizing an initial guess and achieving fast convergence, ultimately leading to the discovery of the most characteristic codewords.  
Step 2: Encoding  
As shown in Figure 1, a mapped index table is generated using Euclidean distance calculation. After obtaining a well-trained codebook, the input image \(I\) is divided into non overlapping square blocks, with each block consisting of \(p\times p\) pixels and representing a \(p^{2}\) vector. These blocks are then replaced by codewords to achieve the purpose of compression.  
As shown in Equation (1), \(i\) represents the vector dimension being processed. The set of vectors is denoted as follows: \(V=\{\ v_{0},\ v_{1},\ \cdots,\ v_{m-1}\ \}\), where \(m\) represents the total number of vectors in the input image \(I\), and \(j\) represents the vector being processed. If \(CB\) is a codebook of size \(n\), \(CB=\{\ cw_{0},\ cw_{1},\ \cdots,\ cw_{n-1}\ \}\), and \(k\) represents the codeword being processed; each codeword \(cw_{k}\) can be represented as \(cw_{k}=\left(p_{j1},\ p_{j2},\ \cdots,\ p_{jp^{2}}\right)\). The Euclidean distance \(d_{VQ}\) between an input vector \(v_{j}\) and \(cw_{k}\) needs to be minimized in order to keep the distortion of the input image to a minimum.  
\[d_{VQ}=min_{k=1}^{n}\sqrt{\sum_{i=1}^{p\times p}\left(v_{ji}-cw_{ki}\right)^{2}} \tag{1}\]  
Through the VQ algorithm, the index of the codeword with the minimum Euclidean distance is recorded in the index table and transmitted to the decoder instead of the pixel values in a \(p\times p\) square block. This approach significantly reduces the image size since the bit size of a codeword is much smaller than that of a 16-dimensional vector. As a result, a high compression rate is achieved.  
**Step 3:** Decoding  
The well-trained codebook and the index table generated by the encoder are sent to the decoder. The codebook used for decoding is exactly the same as the one used for encoding. Therefore, a simple table look-up allows for the retrieval of the original image with minor distortion. The computational load is light, and the image quality can be controlled by using a larger codebook size. The larger the codebook size, the higher is the recovered image quality. However, as with many principles in life, there is a drawback: the larger the codebook, the lower the compression rate.
# Impact of Chirp in High-Capacity Optical Metro Networks Employing Directly-Modulated VCSELs
## 4 Discussion and Conclusions

Table 3: Transmitted capacities and SSMF reach for DSB DMT as a function of: span length, VCSEL type/chirp, and number of crossed WSS. chirp-free condition, leading to an enhancement of the system performance. A trade-off between the amount of chirp and the SSB filter bandwidth to reach the optimal condition could be found, so further theoretical, simulative, and experimental analyses are ongoing. Transmitters hosting devices characterized by chirp parameters as the tunable VCSEL and exploiting 20-GHz SSB DMT modulation would thus support higher capacities; however, such a device is not currently available, since only the short-cavity technology is now able to guarantee modulation bandwidths higher than 10 GHz, which are needed to fully take advantage of the SSB spectral efficiency. Finally, the maximum reach achieved according to the received OSNR and the fiber span length was discussed. The results confirm the possibility of using directly-modulated long-wavelength VCSELs for the realization of transmitters targeting 50-Gb/s capacity per polarization, also in presence of 5 crossed WSSs for reaches of hundreds of kilometers in multi-span EDFA-amplified metro links, supported by coherent detection. The reported simulative analysis shows that the VCSEL-based optical communication system envisaged for metro area networks could represent a suitable solution to reach multi Tb/s capacities, simultaneously decreasing costs, footprint, and power consumption with respect to traditional approaches based on I/Q modulation and standard coherent detection. Moreover, the proposed architecture can be easily adapted to short-reach and datacenters scenarios using the O-band of the optical communications. In this wavelength range, thanks to the limited impact of chromatic dispersion, even direct detection could be a suitable option, further reducing the costs, footprint, power consumption, and complexity of the transmission system. Funding acquisition, P.B. ; Investigation, A.G. and P.M.; Methodology, M.S.M., J.M.F. and L.N. ; Resources, C.N. ; Software, M.R. ; Supervision, P.B. ; Visualization, P.P. ; Writing--original draft, M.R. and A.G.; Writing--review & editing, P.P., M.S.M., and P.B. This paper is part of the PASISON project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 780326. The authors would like to thank [PERSON] and [PERSON] for lending the Vector Analyzer. The authors declare no conflict of interest.
# An Intelligent Radomic Approach for Lung Cancer Screening
## 2 Materials and Methods
### Network Optimization  
In order to select the optimal architectures for malignancy diagnosis, we have defined a _criteria for selection of best models_ that uses 3 metrics assessing the diagnostic capability of the system at nodule and slice levels. Taking the malignant nodules as the positive cases, the metrics used to validate the clinical diagnostic accuracy are:  
1. **Diagnostic Sensitivity.** This measures the percentage of correctly diagnosed malign nodules: \[Sens_{Diagrossi}:=100\frac{TP_{Nodules}}{TP_{Nodules}+FN_{Nodules}}\] (7) for \(TP_{Nodules}\), \(FN_{Nodules}\) denoting, respectively, true positives and false negatives for malignancy detection at nodule level.
2. **Diagnostic Specificity.** This measures the percentage of correctly diagnosed benign nodules: \[Spec_{Diagrossi}:=100\frac{TN_{Nodules}}{TN_{Nodules}+FP_{Nodules}}\] (8) for \(TN_{Nodules}\), \(FP_{Nodules}\) denoting, respectively, true negatives and false positives for benign detection at nodule level.
3. **Slice Diagnostic Index.** This index is an adaptation of the well-known F1-score to measure the percentage of correctly diagnosed slices: \[F_{Nodule}:=\frac{Sens_{Nodule}Spec_{Nodule}}{Sens_{Nodule}+Spec_{Nodule}}\] (9) being \(Sens_{Nodule}\) and \(Spec_{Nodule}\), the average sensitivity and specificity for the classification of 2D slices at nodule level. Sensitivity is given by: \[Sens_{Nodule}:=\frac{1}{N_{Malign}}\sum_{i}\frac{TP_{Slice}^{i}}{NSlice^{i}}\] (10) for \(N_{Malign}\) the number of malign nodules, \(TP_{Slice}^{i}\) the true positives for the i-th malign nodule and \(NSlice^{i}\) its number of slices. Specificity is given by: \[Spec_{Nodule}:=\frac{1}{N_{Benign}}\sum_{i}\frac{TN_{Slice}^{i}}{NSlice^{i}}\] (11) for \(N_{Benign}\) the number of benign nodules, \(TN_{Slice}^{i}\) the true positives for the i-th benign nodule and \(NSlice^{i}\) its number of slices. The \(F_{Nodule}\) score measures the trade-off between benign and malign accuracy at nodule level.  
Our _criteria for selection of best models_ is applied in the next cascade sequence: (1) select the models with highest Diagnostic Sensitivity. If there is only one model, then it is selected. Otherwise, (2) select the models with the highest Diagnostic Specificity. If there is only one model, then it is selected. Otherwise, (3) select the model with highest Slice Diagnostic Index.
## 3 Results
### Device Performance
Moreover, the current densities of devices with all-carbon electrodes are comparable to those of devices based on an organic active layer with electrodes made of carbon-based and standard materials [29; 31; 33; 45; 48; 56]. These results suggest that all-carbon electrodes composed of CNT and graphene films could efficiently collect and transport charge carriers towards an external circuit. The best photovoltaic response for devices with all-carbon electrodes (G-G) displays a \(J_{\text{sc}}\) of 3.6 mA/cm\({}^{2}\), a \(V_{\text{oc}}\) of 0.62 V, and an FF of 0.28, with a consequent PCE of 0.63%. Although the overall efficiencies are not competitive with those of the ITO controls, they are comparable to the performance of OPVs using pristine CVD graphene films as anodes on a smoother rigid substrate (glass) [45]. Therefore, the above results supply feasible evidence for applying all-carbon electrodes in carbon-based solar cells. It is believed that these carbon-based electrodes can be further improved for potentially high-efficient, low-cost, and green all-carbon solar cells.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 5 Simulation Results
### Network Performance for Different Vehicle Densities  
Figure 11 simulates the variation in network performance with different numbers of vehicles. As the quantity and density of vehicles rise, the number of nodes initiating tasks increases correspondingly, leading to an overall enhancement in the network's total QoS. Consequently, this results in heightened packet loss rates and diminished task arrival rates. However, when QoS = 60 Mbps, the increase in the number of vehicles has less impact on these two metrics. This phenomenon arises from the network's capacity to efficiently accommodate a greater number of task-initiating nodes under lower ingress QoS levels. In contrast, at higher ingress QoS values, the rapid consumption of limited cache resources by the augmented total packet count hampers network efficiency. As a consequence, there is a notable surge in packet loss rates.  
Analysis of Figure 11 indicates that prior to approximately 150 time slots, networks with a high number of vehicles at the same QoS exhibit a lower link load rate than those with fewer vehicles; however, post 150 time slots, the pattern reverses. This shift occurs because as vehicle density rises, the number of links in the network topology also increases correspondingly. According to Equation (9), this results in a decrease in the link load rate. Conversely, as time increases, the impact of an increase in the total number of packets in the network outweighs the effect of an increase in the number of links, leading to a higher volume of packets being forwarded by the nodes and consequently an escalation in the link load rate compared to periods of low vehicle density.  
For the total network traffic metric, the value of the metric for QoS = 60 Mbps and Vehicle = 32 is higher in the early phase than QoS = 180 Mbps and Vehicle = 20; however, the values become nearly identical in the later phase. The reason is that in the early stage of the network, the network with high vehicle density has enough node caches and links to manage packet storage and forwarding. As time progresses and the node caches reach capacity, the disparity diminishes due to packet loss. The reason for the trend in node load rate is similar to the analysis in Sections 5.1 and 5.2. When QoS = 60 Mbps, the metric  
Figure 10: Network performance for different caching capacities.  
displays a decreasing trend in the later stage as the number of vehicles increases. According to Figure 11d and Equation (8), at this time, the total network traffic gap is smaller and the number of nodes is larger, leading to a decrease in the node load rate.
## 3 Results and Discussion
### Numerical Calculations  
#### 3.2.1 Initial Data for Calculations  
Numerical calculations were conducted using dimensionless variables and parameters. Then they were transformed into dimensional ones for easy understanding. The following data were selected as initial ones:  
(i) For the underground pipeline:  
\(D_{H}\ =\ 0.2\ \text{m}\); \(D_{B}\ =\ 0.18\ \text{m}\); \(\rho_{\alpha c}\ =\ 7800\ \text{kg}/\text{m}^{3}\); \(\mu_{c}\ =\ 10^{4}\ \text{s}^{-1}\); \(C_{c}\ =\ 5000\ \text{m}/\text{s}\); \(\gamma_{c}\ =\ 1.02\); \(E_{Dc}\ =\ \frac{C_{c}^{2}}{\rho_{\alpha}}\rho_{\alpha c}\); \(E_{Sc}\ =\ \frac{E_{Dc}}{\gamma_{c}}\);(ii) For soil:  
\(\rho_{0g}\ =\ 1800\ \mathrm{kg/m^{3}}\); \(C_{g}\ =\ 1000\ \mathrm{m/s}\); \(K_{\sigma}\ =\ 0.3\); \(\mu_{g}\ =\ 1000\ \mathrm{s}^{-1}\); \(\gamma_{g}\ =\ 1.1\); \(E_{Dg}\ =\ C_{c}^{2}\rho_{0g}\); \(E_{Sg}\ =\ \frac{E_{Dg}}{\gamma_{g}}\);  
(iii) For seismic load parameters:  
\(T\ =\ 0.01\ \mathrm{s}\); \(\theta\ =\ 5\ \mathrm{s}\); \(\sigma_{max}\ \mathrm{MPa}\);  
(iv) For interaction parameters:  
\(H\ =\ 1\ \mathrm{m}\); \(K_{N}\ =\ 100\ \mathrm{m}^{-1}\); \(\beta\ =\ 2\); \(f\ =\ 0.5\); \(u_{*}\ =\ 0.005\ \mathrm{m}\).  
Note that from the interaction Equation (24) at \(u\ =\ u_{*}\), we obtained  
\[u_{*}\ =\ \frac{f}{K_{N}} \tag{42}\]  
From this, it follows that with the known values of the coefficient of friction \(f\) and the coefficient of soil rigidity \(K_{N}\), it is possible to determine the corresponding value of \(u_{*}\)[14].  
If we assume that in Equation (26) \(u_{*}\rightarrow\infty\), then in the calculations we obtained the interaction law of the type (14), and if we assume that \(u_{*}\to 0\), (7) is taken as the interaction law. In the calculations, the cases are considered when the component \(\sigma_{N}^{D}\) is not taken into account in (9), and \(I_{S}\ =\ 0\) in (15) is taken in the process of the interaction. When, in the calculations, the value of \(u_{*}\) is set as a significant one (\(u_{*}\ =\ 100\ \mathrm{m}\)), the soil structure practically does not change since \(I_{S}\ =\ \frac{u}{u_{*}\to 0}\). As can be seen, by varying the initial data parameters, it is possible to obtain various options of the law of underground pipeline interaction with soil.  
Let us consider these options.
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 2 T/R Module Scheme
### Tile-Type T/R Module  
The tile-type architecture is a multi-layer planar structure. The chips in the tile-type transceiver modules are no longer arranged horizontally but arranged vertically in different layers according to function decomposition. Different functional layers are connected through RF vertical interconnection, as shown in Figure 2. Vertical interconnection is the key to the realization of tile-type transceiver components [24; 25]. The traditional tile-type architecture relies on structural components and radio frequency connectors for vertical interconnection, making it challenging to ensure high-frequency transmission performance. The external interfaces of the tile-type T/R modules are in the form of connectors, which are not size-efficient. Limited by the antenna array element spacing, the tile-type T/R module design is still challenging. Nowadays, tile-type transceiver modules are designed with multi-layer substrates and three-dimensional integration. The substrates and chips of the tile-type T/R modules are assembled parallel to the array plane, and the signal interconnection between layers is achieved by the quasi-coaxial transition. Usually, chips or circuits with the same functions in multiple channels are integrated in one substrate board. To achieve more complicated functionality, more substrate boards can be used and fastened together to form a complex tile-type sub-array.  
It can be seen from the architecture of the active phased array antenna system that tile-type transceiver modules are more suitable for high integration density and low-profile phased array antennas. Compared to brick-type architectures, tile-type systems utilize highly integrated multifunctional chips and micro-assembly techniques to assemble transceiver components in layers and achieve high-density integration, exhibiting the advantages of low profile, light weight, small volume, modularity, and easy scalability. Tile-type architectures are more conducive to system assembly and expansion, especially when the dimensions of the systems are restricted.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 3 The Proposed Scheme: RSNA
### The Description of RSNA
#### 3.2.1 Delivery Competency  
An appropriate wireless channel is fundamental to construct efficient communication, which is one of the strongest guarantees for data transmission. In our previous work [21], we have been proposed link availability prediction based on machine learning for opportunistic networks in oceans. Therefore, probabilistic link availability is directly used in this paper to qualify the probability for packet transmissions. If the node \(r\) can receive a message from node \(l\), the link availability (\(p_{la}\)) can be defined by  
\[p_{la}=\left\{\begin{array}{ll}p_{lr}&\text{node r has received the message m}\\ 0&\text{otherwise}\end{array}\right.\quad. \tag{1}\]  
Moreover, the encountering probability is used to describe the delivery relationship between node pairs, which can be given by  
Figure 2: Relay node selection example.  
\[p_{l,r}=\frac{e_{l,r}}{\sum\limits_{c=1,c\
eq l}^{n}e_{l,c}} \tag{2}\]  
where \(e_{l,r}\) is the number of encounters between nodes \(l\) and \(r\), and \(e_{l,c}\) is the total number of encounters between nodes in a time period \(T\).  
The encountering process can be viewed as a binary set including discrete node and time period, whose stochastic process \(\{x(t),teT\}\) can be defined by the Markov chain, the state transfer for any state node \(\{1,2,\cdots,l,r,\cdots,n\}\) at any time \(\{1,2,\cdots,T\}\), there is  
\[p_{l,r} =\{x(t+1)=r|x(t)=l,x(t-1)=l-1,\cdots,x(1)=1\}\] \[=\{x(t+1)=r|x(t)=l\}. \tag{3}\]  
Delivery competency of nodes can be described by  
\[P_{FC}=\alpha p_{la}+\beta p, \tag{4}\]  
where \(p_{la}\) is the link availability, \(p\) is the encountering probability, and \(\alpha\) and \(\beta\) are weights on delivery competency, respectively. In addition, \(\alpha+\beta=1\).
###### Abstract  
We address in this paper the problem of abnormal event detection in video-surveillance. In this context, we use only normal events as training samples. We propose to use a modified version of pretrained 3D residual convolutional network to extract spatio-temporal features, and we develop a robust classifier based on the selection of vectors of interest. It is able to learn the normal behavior model and detect potentially dangerous abnormal events. This unsupervised method prevents the marginalization of normal events that occur rarely during the training phase since it minimizes redundancy information, and adapt to the appearance of new normal events that occur during the testing phase. Experimental results on challenging datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level in anomaly detection task.  
Keywords:abnormal event detection; deep learning; Convolutional Neural Network; unsupervised learning; online; adaptive systems +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
This research has been very successful with the Investigation of Detecting Position of Partial Discharge in Cast-Resin Transformer using Frequency and Acoustic Sensors. The four AE sensors were used with one HFCT sensor for analysis of PD in cast-resin transformers by PowerPD software. All these devices could be installed while the transformer was working, which does not necessarily require to turn off or shutdown the equipment and separate it from the electrical part system. This system could detect and analyze problems occurring within the transformers in the initial conditions and notify them of severe problems. This PD analysis method could accurately detect and locate the PD position within the transformer, which enables timely and efficient maintenance.  
This research uses the AE and HFCT sensors to measure and determine the position of partial discharge in the transformers. The experimental has been three parts was the primary and insightful examination of partial discharge, for Identify the possibility of partial discharge, which includes the position of partial discharge in first part and second part. The final part was the position examination of partial discharge source which it was the verify the position of the PD source of cast-resin transformer to confirm the inspection results and analyze from AE sensor and HFCT sensor detection in the first two parts. The AE and HFCT sensors could certainly be used for detecting the location of the PD source, which confirmed by calculation  
to transformers. Furthermore, the data could also be used for planning the maintenance of planned transformers. To solve the problem of electrical instability which causes the electrical insulation of the transformer to be damaged as a result of the partial discharge in the transformer system.  
Conceptualization, N.T. and S.A.; methodology, S.A., N.M. and K.T. (Khanchai Tunlasakun); validation, N.T., A.S. and K.T. (Kittimasak Tikakosol); formal analysis, N.T., S.N. and K.B.; investigation, T.Y. and H.K.; writing--original draft preparation, N.T. and S.A.; writing--review and editing, N.T.; visualization, N.T., A.S.; supervision, N.M. and S.A.; project administration, N.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by [PERSON]'s University of Technology Thonburi (KMUTT), Thailand, and under the project of the Research, Innovation, and Partnerships Office (RIPO) with Faculty of Industrial Education and Technology Research Funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Design of A Parallel Decoding Method for LDPC Code Generated via Primitive Polynomial
## 5 Conclusions  
In this paper, we had improved the BER performance of m-sequences code by applying parallel decoding method. Firstly, to construct parity-check matrices for sub-decoders, we investigated the parity-check constraints from sampling sequence of m-sequence and used them to construct the parity-check matrices by cyclic shift. Then, to overcome the influence of short cycles of parity-check matrices, we had proposed a new parallel decoding method with two types of sub-decoders. Simulation results show that the proposed decoding method improved about 0.4 dB compared with the original single-decoder decoding method at a BER of \(10^{-5}\).  
**Author Contributions:** Conceptualization, Z.Z. and L.Z.; methodology, Z.Z. and Z.H.Z.; software, Z.Z. and Z.H.Z.; validation, Z.Z., L.Z. and Z.H.Z.; formal analysis, Z.Z. and Z.H.Z.; investigation, Z.Z.; resources, L.Z.; data curation, Z.H.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z., L.Z., Z.H.Z.; visualization, Z.Z. and Z.H.Z.; supervision, L.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.