esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Works and Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **Classification** & **Feature Selection** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline \multirow{8}{*}{Gabor} & & PS & 0.93750 & 0.94107 & 0.94866 & 0.93750 \\  & & PV & **0.95982** & **0.96339** & **0.96875** & **0.95982** \\  & SVM & FS & 0.94196 & 0.94628 & 0.95759 & 0.94196 \\  & & FV & 0.93750 & 0.94286 & 0.95089 & 0.93750 \\  & & VLPSO & 0.92857 & 0.93289 & 0.94420 & 0.92857 \\ \cline{2-6}  & & PS & 0.99554 & 0.99583 & 0.99777 & 0.99554 \\  & & PV & 0.99554 & 0.99583 & 0.99777 & 0.99554 \\  & LR & FS & 0.99554 & 0.99583 & 0.99777 & 0.99554 \\  & & FV & 0.99554 & 0.99583 & 0.99777 & 0.99554 \\  & & VLPSO & 0.99554 & 0.99583 & 0.99777 & 0.99554 \\ \hline \multirow{8}{*}{LBP} & & PS & **0.95982** & **0.95685** & **0.95848** & **0.95982** \\  & & PV & 0.92411 & 0.92217 & 0.93095 & 0.92411 \\ \cline{1-1}  & SVM & FS & 0.92411 & 0.92381 & 0.93973 & 0.92411 \\ \cline{1-1}  & & FV & 0.91964 & 0.92054 & 0.93192 & 0.91964 \\ \cline{1-1}  & & VLPSO & 0.80804 & 0.80045 & 0.81920 & 0.80804 \\ \cline{1-1} \cline{2-6}  & & PS & **0.99107** & **0.99137** & 0.99330 & **0.99107** \\ \cline{1-1}  & & PV & 0.95982 & 0.95714 & **0.95848** & 0.95982 \\ \cline{1-1}  & LR & FS & 0.98661 & 0.98869 & 0.99554 & 0.98661 \\ \cline{1-1}  & & FV & 0.94196 & 0.94449 & 0.95238 & 0.94196 \\ \cline{1-1}  & & VLPSO & 0.81696 & 0.81101 & 0.82813 & 0.81696 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification metrics of our model compared with benchmarking VLPSO.
## 5 Parametric Studies on Energy Dissipation
### Excitation Force Effects  
Excitation forces of 10 kN, 50 kN, 100 kN, and 500 kN were applied at a constant 35 Hz frequency. Figure 11 presents void ratio-time histories under the different forces. All samples progressively compacted but the compaction degree increased with higher forces, yielding lower final void ratios.  
Figure 12 illustrates the evolutions of energy dissipation components over time. It was observed that all values increased with increasing excitation, with cumulative dashpot, sliding, and rolling slip energy exhibiting a monotonous growth over time. Strain energy, on the other hand, experienced a rapid initial increase before descending to steady-state levels as the void ratio stabilized. When comparing the rate of increments, it can be noted that dashpot, sliding, and rolling slip energy exhibited greater growth in the zone of rapid void ratio change, which gradually slowed down and declined as the system approached stability. However, dashpot and sliding energies maintained small constant increases after reaching certain levels, while the convergence of rolling slip energy flattened completely.  
With regard to the degree of compaction, the effectiveness and efficiency improved significantly with higher forces, up to a certain limit. While the void ratio differences gradually decreased from 50 kN to 100 kN and from 100 kN to 500 kN due to diminishing marginal returns, the energy consumption of the specimen continued to increase approximately linearly with the increase in excitation.  
Figure 11: Changes in void ratio with time under different excitation forces.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 4 Optimization Algorithms Used for IRS-Assisted UAV Communications  
Most of the previous works introduced in Section 3 involve optimization to maximize the gains from using UAVs and IRSs. In this section, we introduce methods used to optimize the IRS-assisted UAV communications systems. The approaches for optimization are largely divided into traditional optimization algorithms and ML algorithms. Both the traditional optimization and ML are sophisticated technologies that are used to analyze and draw meaningful inference from data. Basically, both methods are tools to solve complex problems using mathematics with data and computers. However, there are some differences between traditional optimization and ML. The traditional optimization approach leverages mathematical models and resorts to algorithms to find optimal or suboptimal solutions for given problems. ML fundamentally gives a complex and interconnected set of decisions, although the predictions resulting from ML can be used to guide specific decisions. Therefore, when establishing a model based on limited data, traditional optimization can provide the best solution and can easily be adapted to changes in the problem. The ML approach can be more efficient when prediction is needed for outcomes outside the data range [92]. When ML learns based on insufficient or inaccurate data, its performance should always be evaluated using the loss function due to the deterioration of prediction accuracy or overfitting [93].
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
###### Abstract  
In recent years, 5G networks and services become progressively popular among telecommunication providers. Simultaneously, the growth in the usage and deployment of smartphone platforms and mobile applications have been seen as phenomenal. Therefore, this paper discusses the current state of the art of 5G technology in the merger of unconditional security requirements referred to as Quantum Cryptography. The various domain of Quantum Cryptography is illustrated including the protocols available, their functionality and previous implementation in real networks. This paper further identifies research gaps covering critical aspects of how Quantum Cryptography can be realized and effectively utilized in 5G networks. These include improving the current technique in Quantum Cryptography through efficient key distribution and message sharing between users in 5G networks.  
quantum communication; QSDC; QKD; 5G
## 4 Our Proposed Routing Algorithm
### Route Steady Phase  
In our proposed routing algorithm, sensor nodes monitor the sensing field and transmit their monitored data to their relay nodes and finally to the mobile sink node. To reduce energy consumption and improve data quality, data fusion can be used in each neighbor node of the mobile sink nodes.  
On the basis of the nodes' distribution and transmission range, the selection of parking positions, which will be used for the mobile sink nodes to sojourn, was described in a previous section and the corresponding location information is kept in the table PP_TABLE.  
The process of our routing algorithm is described in Figure 4. In a certain round \(r\), each mobile sink will select one parking position as its sojourn location to collect monitored data collaboratively. Then, node \(i\) will calculate the distance to the mobile sink to judge whether it is in the transmission range of mobile sink.  
Figure 3: Multiple sink mobility strategy.  
Figure 4: Flowchart of our proposed routing algorithm.  
If yes, these neighbor sensor nodes will broadcast a NEIGHBOR_STATUS message, and wait for JOIN_REQUEST from the other sensor nodes. The neighbor sensor nodes of mobile sink are selected primarily based on the distance \(d_{sn}\). The neighbor sensors transmit monitored data to their mobile sink nodes directly, and they will keep awake to receive JOIN_REQUEST. A time division multiple access (TDMA) schedule is used to allocate transmission time slot for each neighboring sensor node, as is depicted in the left part of Figure 4.  
If no, the mobile sink will wait for the next neighbor sensor nodes announcement as it moves to the next position. Then, it will repeat the process of judging its neighboring sensor nodes and sending JOIN_REQUEST messages, _etc._, as isshown in the right part of Figure 4.  
To reduce the energy consumption and guarantee network connectivity, each sensor can use power control to set its transmission power based on the distance to the receiver. The multi-hop route can be set up once all sensor nodes find relay sensor nodes. In order to avoid data packet collisions and reduce energy consumption, each neighbor sensor node of the mobile sink nodes will use a unique spreading code [3]. This unique spreading code will be sent from each mobile sink node to the corresponding next hop sensor nodes. That is, this code is spread to source sensor nodes from destination nodes. Thus each transmission path has a unique code and all sensor nodes will transmit their monitored data to neighbor sensor nodes using this spreading code. Data communication will be conducted in each link and the monitored data packets will be transmitted to mobile sink nodes in a single-hop or a multi-hop manner.
# Novel Thermal Management Strategy for Improved Inverter Reliability in Electric Vehicles
## 5 Conclusions  
Maximizing the life-cycle of modern EVs and their constituting elements is a relevant research topic. Due to the increasingly demanding requirements of the automotive industry, EV traction inverters must operate under high power semiconductor junction temperatures and stringent thermal cycles. Under these extreme working conditions it is difficult to ensure the power converter reliability while not excessively reducing the performance of the drive. In this context, a novel online thermal management algorithm based on a TCT switching frequency regulator that maintains the semiconductors' junction temperature below excessive temperatures has been proposed in this paper. The algorithm has been designed to be easily fitted in automotive microcontrollers in order to meet the always existent computational requirements of the automotive industry.  
A realistic 75 kW in-wheel direct drive IPMSM fed by an automotive grade SiC power converter has been accurately modelled and used to validate the proposal through numerous and precise simulations, which also include standardized and therefore representative driving cycles. From the results obtained, it can be concluded that the introduction of the proposed TCT algorithm improves the thermal behaviour of the drive power converter without jeopardizing the system controllability and performance. As a final remark, it is worth mentioning that for some applications where a much lower temperature constraint or more severe load conditions are imposed, only regulating the switching  
Figure 9: Simulation results of the SiC based IPMSM drive under standardized WLTP driving cycle when using TCT regulation.  
frequency could not be sufficient to track the temperature limit. In such cases, the proposed thermal management strategy could be extended by incorporating a torque limiting regulator.  
E.T.: conceptualization, thermal management algorithm implementation, analysis, original draft preparation; E.I.: RT-Lab simulation, original draft preparation; P.P.: Vehicular model implementation, review and editing; A.A.: Torque regulation algorithm, review and editing; A.L.: SiC power module characterization; A.P.P.: SiC power module characterization and document review. All authors have read and agreed to the published version of the manuscript.  
This work has been supported in part by the European Commission through ECSEL Joint Undertaking (JU) under Grant Agreement No. 783174 (HiPERFORM project), by the Government of the Basque Country within the research program ELKARTEK as the projects ELIVE (KK-2019/0006) and ENSOL 2 (KK-2020/00077), by the Government of the Basque Country within the fund for research groups of the Basque University system IT978-16, by the Government of Spain through the Agencia Estatal de Investigacion Project DPI2017-85404-P, and by the Generalitat de Catalunya through the Project 2017 SGR 872.  
The authors declare no conflicts of interest.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 4 Implementation  
We implement ECDedup (Section 3) in Golang on Ubuntu-22.04 with about 3.9K SLoC. We use OpenSSL (version 1.0) [26] to implement cryptographic operations. Specifically, we use SHA-256 and AES-256 for the hash operations in matrix generation (Section 3.3).  
**Chunking and fingerprinting:** We first implement content-defined chunking based on Rabin fingerprinting [12] on the client, which takes the minimum, average, and maximum chunk sizes as input (by default as [2, 3, 4, 5, 6, 14], we set them as 4 KB, 8 KB, and 16 KB, respectively) and computes rolling hashes over chunks via a fixed-size sliding window to identify the boundaries of each chunk. To implement coding-based encryption, the client further generates a content-based key for each data chunk via SHA-256.  
**Encoding and decoding:** We implement the encoding and decoding operation based on Klauspost Reed-Solomon [27], an open-source Reed-Solomon library for Golang which supports SIMD instruction sets [21] (including AVX2, SSE2, and NEON) that can accelerate the encoding and decoding speed. We use the Cauchy matrix as the coding matrix, such that the matrix can be generated based on the chunk's content.  
**Server-aided matrix generation:** Recall that a client generates a content-based coding matrix for a original data chunk by generating the elements of the Cauchy matrix with the chunk's content-based key, as in Section 3.3. For each key server, we implement a matrix generation module, which receives the vectors from clients and generates the server-aided encrypted vectors to form the content-based coding matrix.  
**Deduplication:** Each server implements a fingerprint index to map the fingerprint of each ciphertext chunk to the physical address where the ciphertext chunk is stored, using a key-value store based on LevelDB [28]. We also use the unique ciphertext chunks (average in KBs) as a fixed-size container (e.g., 4 MB [9]) based on the locality within those chunks, such that the chunks with locality can be prefetched to mitigate the disk I/O overhead.  
**Blockchain integration:** We integrate blockchain into ECDedup to improve the security of the system. We manage the decentralized key servers using a private blockchain and employ zero-knowledge proofs [29] to ensure that the secret key in the key servers cannot be tampered with. We also implement hierarchical classification access control via the smart contracts of blockchain to manage the access control of the data chunks.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 4 Network Model  
We assume that sensors are deployed randomly in a circular area around the sink node with radius \(l\) and each node knows its position by using localization techniques [19; 20]. In this paper, we denote \(N\) and \(R\) as the total number of nodes and the transmission range of a sensor node, respectively. Nodes are partitioned into multiple groups according to the distance between node and sink. Let \(c\) denote the width of a group. Node \(A\) obtains its group number using \(d_{A}\), the distance between node \(A\) and the sink. Figure 1 illustrates node deployment in the total area. Node \(A\) belongs to Group 1 if \(0\leq d_{A}\leq R\) or if it can transmit packets directly to the sink. Node \(A\) belongs to group \(j\) if \(d_{A}\) satisfies:  
\[R+c(j-2)<d_{A}\leq R+c*(j-1) \tag{4}\]  
where \(0\leq d_{A}\leq l\). The total number of groups, denoted by \(k\), is calculated as follows:  
\[k=\left\lceil\frac{l-R}{c}\right\rceil+1 \tag{5}\]  
In order to reduce energy consumption, duty cycling is considered in the network. We denote \(T\) and \(T_{a}\) as the duty cycle interval and the active period, respectively. Sensor nodes independently choose a random wake-up time and stay awake for \(T_{a}\). Then, they switch the radio off for the rest of time of the duty cycle interval. Whenever nodes switch the radio on, they broadcast beacon messages that include only the node ID and group number. Time synchronization is not required between nodes.
# Personalized Context-Aware Authentication Protocols in IoT
## 3 Results
### Digital Signature and Steganography--Steganographic CPRA [21; 22]  
Apart from the authentication of the user for the purpose of granting access, the addition of context from the identity vector might be used to sign the visual messages or files and data streams coming from an already authenticated individual, thus forming a steganographic CPRA. During a video capture session or a photo shoot, a random dataset is chosen from the LAAP/CAAP and communicated during the handshake protocol as a session key. Priority is given to the characteristics that are already measurable during the session (hand movements, involuntary tics, gestures, stutters, facial expressions, surroundings, GPS tags, etc.), similar to the semi-random vector procedure from Section 3.1. All of these individual qualities present in a picture or video being sent are quantified according to their values, given a sanity check of being in the acceptable range for the authenticated user (to weed out anomalous readings or unusual behavior), and merged into a digital stream that is ordered according to the negotiated protocol set (as shown on Figure 1, but only a subset is chosen). A hash of such a stream is made. The hash is then used as a mask for a steganography process. A secret message or a digital signature is encoded into the picture (by changing the least significant bits of pixels), only utilizing some of the pixels according to the mask and leaving the rest of the values untouched (see Figure 4 for a graphical representation). On the receiver end, a similar hash is computed from a received picture (according to the negotiated qualities), and the picture's steganography secret or watermark is then read (basically, the sent picture is used as an encoded message, and the calculated hash is used as a one-time pad). If the broadcast has been altered in any way during the transport (changing the biometrics, timestamps, coordinates, or voice or using AI-generated deep fakes), the quantities computed according to the key will deviate from the original material, and even a tiny deviation in these data will provide a fundamentally different hash value that cannot be successfully used as a pad to read the steganography part of the picture, as only a random sample of the bits will be pointed at, providing only white noise. In such a way, there are no personal data of the sender sent over (only what can already be inferred from a picture), and the data are tamper-resistant (even a slight change will provide only random noise and might destroy the secret part entirely). If the session key is compromised, the attacker might be able to read the signature/secret message, but would not be able to re-encode/change the message itself, as it would possess fundamentally different personal characteristics (Figure 4).  
Figure 4: Secret message/watermark encoded with personal data.
## 2 Results and Discussion  
The precision of DI-SPME was tested using biological sources and analysis of variation, to determine the analytical variability of the data generated when adult flies were sampled at different stages. In this experiment, the DI-SPME samples of mature medfly adults at three different mating stages (before, during and after), were analyzed using a GC-MS. To examine the effectiveness of DI- SPME, two different solvent extractions were used to compare the DI-SPME, which indicated quantitative and qualitative differences between these solvents in the type and peak areas of compounds. For further testing of DI-SPME, a GC-MS was used to compare the composition of two extracts solvents after directly immersing the SPME fiber in the extract. Comparison of these compound profiles revealed that DI-SPME had higher levels of the lighter chemicals and lower levels of ponderous chemicals. Firstly, the choice of the sealing and desorption time was carried out by fixing the time (2, 4, 8 and 16 h of sealing times). The best results were obtained with the recently developed 50/30 \(\upmu\)m Carboxen/DVB/PDMS and, thus, 16 h sealing time was selected for further method development. Overall, DI-SPME detected 110 compounds using the acetonitrile/water solvent and 86 compounds using the methanol/acetonitrile/water solvent at three different stages. In the first solvent extraction, 47, 26 and 37 compounds were identified from samples taken before, during and after mating, respectively. In the second solvent, 33, 31 and 22 compounds were identified from the samples taken before, during and after mating, respectively. The method has developed a strategy for rapid comparison of non-processed MS data files. To explain the differences between the samples, the method includes the following: baseline correction; alignment; time window determinations; alternating regression; PLS-DA. The identification of the retention time, the retention index, and mass spectral, MS structurally ordered separation windows in the chromatograms. For understanding the trends in analytical variability of our data set generated when different sides of solvents were sampled, chemically and functionally distinct metabolites were tentatively identified with retention index, the aid of mass spectral similarity, injection of authentic standards (C\({}_{7}\)-C\({}_{30}\)), and structurally ordered separations. The results showed significant correlations between metabolite molecular weight, the retention index and metabolites. The main compounds identified were ethyl glycolate, \(\alpha\)-farnesene; decanoic acid octyl ester; 2,6,10,15-tetramethylheptadecane, 11-tricosene, 9,12-(Z,Z)-octadecadienoic acid, methyl scaerate; 9-(Z)-tricosene, 9,11-didehydrolumisterol acetate; 1,54-dibromotetrapentacontane, 9-(Z)-hexadecenoic acid hexadecyl ester, 9-(_E_)-octadecenoic acid, and 9-(_Z_)-hexadecenoic acid octadecyl ester, (Tables 1 and 2).
# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 3 Materials and Methods
### Privacy-Preserving Training  
To ensure data privacy, we apply Differential Privacy Stochastic Gradient Descent (DP-SGD) to the unimodal encoders while preserving standard SGD for the shared head. The implementation details are as follows:  
(1) Gradient Clipping. The L2 norm of each sample's gradient is clipped to a predefined threshold \(C\): \[g^{\prime}=g/\text{max}\left(1,\frac{\|g\|_{2}}{C}\right)\] (13) where \(g\) is the original gradient and \(g^{\prime}\) is the clipped gradient. (2) Noise Injection. Gaussian noise is added to the clipped gradient to satisfy differential privacy: \[\widetilde{g}=g^{\prime}+\mathcal{N}\left(0,\sigma^{2}C^{2}I\right)\] (14) where \(\sigma\) is the noise standard deviation, and \(I\) is the identity matrix. (3) Differential Privacy. DP-SGD [15] ensures data privacy in PAMRL by injecting noise into the gradients of unimodal encoders during training: \[Pr[M(D_{1})\in S]\leq e^{\epsilon}\cdot Pr[M(D_{2})\in S]+\delta\] (15) where \(D_{1}\) and \(D_{2}\) are datasets differing by one sample, \(S\) is any output set, and \(\epsilon\) denotes the privacy parameter. The privacy parameters, including the clipping threshold \(C\)are determined through an analysis of the gradient L2 norms in the datasets, while the noise standard deviation \(\sigma\) is computed using the RDP [13] accounting method based on dataset sizes, batch size, and training epochs, with \(\delta=10^{-5}\) set by referencing the classic DP-SGD parameter [13].