esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 2 Connectivity in Tactical MANETs
### Routing Schemes  
The ultimate goal in mission critical tactical MANETs is to maintain connectivity between all nodes. Tactical MANETs are usually larger than commercial MANETs including the number of hops the data need to pass from the source to a destination. The problem can be formulated as determining the maximum size of MANET which can be sustained for given mobility and routing strategy. Proactive routing protocols preserve the route information for all paths which can be critical to maintain network topology in bandwidth-constrained environments. Proactive routing protocols can also better address security vulnerabilities [13]. In contrast, reactive routing protocols are better suited for smaller networks, for example, at battalion level in tactical MANETs with rapid movements [14]. In addition, MANETs containing several hundred nodes suffer from latency constrains [15]. Another concern is to limit data exchange through the network, particularly during some phase of tactical missions.  
Minimum hop-count is the most common metric used for route selection in many existing routing protocols. There are also other metrics used for finding the optimum path in MANETs with other objectives [16]. Routing protocols can also assume geographical distances, channel availability, traffic load and link error rates as metrics to make routing decisions and select end-to-end path. In tactical context, the main concern is to provide robust routing for reliable delivery of mission-critical information within ad-hoc network topology. It is then not sufficient to only consider only the number of hops without regard for the quality of each link along the path. In order to determine the optimum network path in tactical MANETs, it is important to incorporate link-quality into routing metrics to make reliable selection of links and of the end-to-end path under all circumstances. This leads to cross-layer protocol design with improved efficiency and reliability of data delivery [17; 18].
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 6 Implementation and Evaluation
### Evaluation
#### 6.3.6 OOD vs. OODC  
The previous experiment demonstrates that both OOD and OODC approaches have similar reliability performance owing to the packet loss recovery strategy. The OOD mechanism allows the relay to retransmit a packet that is missing in the feedback message. Conversely, the OODC mechanism recovers the packet loss by applying network coding. Although their reliability is similar, the network coding preserves the reliability efficiently. We conducted an experiment based on topology 1 with the ADR mechanism to investigate it.  
As shown in Figure 11, the left-axis is the amount of packet forwarding, and the right-axis is the finish time. Basically, the OODC mechanism forwards an additional encoded packet to guarantee successful decoding. However, the OODC forwards the packet effectively as it can recover the encoded packet during the feedback period. Conversely, the OOD failed to work properly when the end-devices increase. The bottleneck in the relay makes it challenging to finish the current transmission before the next feedback arrives. Those packets are transmitted again in the next feedback because they are late to be included in the feedback message. From this evaluation, we can conclude that network coding makes our mechanism increase reliability while preserving the network capacity.
## 11 Applications of the Integration of Blockchain and Cloud Computing
### Smart Manufacturing
#### 11.4.1 Data De-Duplication Scheme Using Blockchain Technology for Cloud Storage Services  
The data deduplication strategy is used to reduce the amount of redundant data in the cloud and to save space. This method keeps one duplicate of the indistinguishable information to save storage space. In other words, it may improve data efficiency while reducing the need for physical equipment, but it also has the potential to worsen the issue of data dependability. Data deduplication is used to distribute data across several servers, and the storage information is stored on the blockchain. As a result [29], the data deduplication method and the blockchain approach may ensure system secrecy and data integrity. Distributed storage systems may also benefit from it. CSP and data owners should join the blockchain network as nodes for associated services. To ensure the integrity of the data, all duplications and transactions should be recorded on the blockchain. Based on the data unit, location, and disc placement, deduplication methods are categorized into three categories: Data unit deduplication, location deduplication, and disc placement deduplication are the three types of deduplication techniques.  
File-level and chunk-level deduplication are the two subcategories of data unit deduplication [33]. The hash values of the two files are used to compare them in the deduplication process. One copy is kept if both hash values are identical. Files may be divided into fixed or variable-length blocks and then checked for duplicate material in deduplication at the chunk level. Source and target deduplication processes are subdivided into two subcategories. After the client sends the files, the target deduplication process works alongside a receiver and discards any extra data. The storage device does deduplication without affecting the client's operation. Customers do not know what is going on in the deduplication process. Source deduplication is performed before data transmission. Because it makes use of the client's resources, this kind of deduplication conserves network traffic capacity. Forward and backward reference deduplication are two subcategories of disk-level deduplication [46].
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 2 Probabilistic Forecasting Models
### Artificial Neural Network-Based Probabilistic Forecasting  
Artificial neural network (ANN)-based models can be also used to develop probabilistic forecasting models. NN architectures consist of a network of neurons as processing units. The neurons connect to each other through synapses, which are weighted connections. In the training stage, the optimal weights are determined.  
In general, an NN has three layers--an input layer, an output layer and one or several hidden layers. A feedforward ANN passes the data forward from input to output. On the other hand, a recurrent NN (RNN) can connect some neurons in a backward direction as well as the forward direction for further processing. In this way, RNNs have the ability to consider the autocorrelation or time dependencies of data [22]. Figure 2 compares the architecture of a feedforward NN with that of an RNN. The literature proposed various architectures and developed probabilistic forecasting models for both feedforward and recurrent ANN-based models. In the following, some important models are reviewed.
# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 2 Related Work
They employ a semi-supervised Sammon embedding algorithm to modify semantic space prototypes to better align with visual space prototypes, making it possible to synthesize unseen class prototypes effectively in the visual space. This allows for the use of a nearest-neighbor method with these unseen class prototypes to accomplish the classification task. [PERSON] et al. [15] present a method for classifying RS scene images using the natural language processing model Word2 Vec to map the names of seen and unseen scene classes to semantic vectors. They construct a semantic-directed graph based on these vectors to describe relationships between unseen and seen classes. Knowledge transfer from seen to unseen classes is facilitated through initial label predictions using an unsupervised domain adaptation model. A label-propagation algorithm, aided by the semantic-directed graph and initial predictions, is then employed for zero-shot scene classification. To mitigate noise in zero-shot classification results, a label refinement approach based on sparse learning is used, leveraging visual similarities among images within the same scene class. Finally, [PERSON] et al. [16] undertake a zero-shot investigation with a specific emphasis on fine-grained recognition of RS images. They create a matching function and illustrate the process of transferring knowledge to unfamiliar categories. However, it is noteworthy that most of these methods primarily revolve around devising visual-semantic embedding models that exclusively consider known classes. This limitation makes it challenging to ensure effective extension to unseen classes. Additionally, models trained solely on known data tend to misclassify unseen test instances into known categories, leading to a pronounced imbalanced classification problem.
## 4 Network Architecture
### A Novel Network Architecture
The third case infers that two clouds that are not physically linked cannot perform a handover. Another consideration when selecting the cloud is the corresponding available computational capacity since a heavy-burdened edge center may reduce the processing efficiency of the Hyperloop communication tasks. As such, the total handover cost should be a sum of these two costs, which can be marked as \(\eta g_{cc^{\prime}}+(g_{c}+g_{c^{\prime}})\), where \(\eta\in(0,1)\) is a constant used to tune the primary of these two costs. Finally, our goal is to minimize the total group handover cost during the whole Hyperloop line by selecting a certain edge center at different logical cells, which can be expressed as  
\[\min_{\varphi_{l,c},\varphi_{l(l+1),c^{\prime}}}\{\sum_{l}\eta\varphi_{l,c} \varphi_{(l+1),c^{\prime}}g_{cc^{\prime}}+(\varphi_{l,c}g_{c}+\varphi_{(l+1),c ^{\prime}}g_{c^{\prime}})\} \tag{7}\]  
where \(\varphi_{l,c}\in[0,1]\) denotes the selection variable that the logical cell \(l\) selects the cloud \(c\) to communicate as the Hyperloop travels inside it and \(\varphi_{(l+1),c^{\prime}}\) represents the selection of the next logical cell (\(l+1\)). The selection variables are constraint by following formulas  
\[\sum_{c}\varphi_{l,c}=1, \tag{8}\]  
\[\sum_{c^{\prime}}\varphi_{(l+1),c^{\prime}}=1, \tag{9}\]  
Figure 7: The schematic diagram of graph theory. which means that one logical cell can only select one edge cloud to communicate as the Hyperloop travels inside it. Optimization problem (7) together with Equations (8) and (9) can be regarded as an NP-hard problem and it can be solved by the Floyd-Warshall algorithm. Details solution can be found in our previous work [16; 17]. Based on the proposal, the group handover cost can be alleviated evidently to ensure a high on-board communication QoS together with reliable and safe operation of the Hyperloop.
# An In-Line Coaxial-to-Waveguide Transition for Q-Band Single-Feed-Per-Beam Antenna Systems
## Appendix A  
A further analysis of the device performance have been carried out by using another full-wave analysis simulation software, CST Microwave Studio, to compare the results provided by two different software which use different numerical methods to solve the electromagnetic problem and to provide another contribute to the result validation. Two different solvers have been tested in CST, the Time Domain Solver which is based on the Finite Integration Technique (FIT) to solve the Maxwell equations, and the Frequency Domain Solver which is based of the Finite Element Method (FEM). The simulation settings for both the tests are as follows in the Table A1.  
The Figure A1 shows the Return Loss for the aforementioned CST simulations and the ANSYS simulation presented in the Result section (Figure 7), which has been tested with a Maximum Delta S equal to 0.001 and Discrete Sweep Type. The curves highlight a good matching between the three different simulations (all the curves keep higher than 24.25 dB) and increases the confidence on the simulated results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**CST-MS - Time Domain Solver** & **CST-MS - Frequency Domain Solver** \\ \hline Mesh Type: Hexaedral & Mesh Type: Tetrahedral \\ \hline Accuracy: \(-50\) dB & \\ \hline Mesh properties & Mesh properties \\ (a) 40 cell per wavelength & (a) Adaptive Mesh refinement \\ (b) Volume refinement: 6 additional cells around box & (b) Maximum 15 passes \\ (c) Face refinement: minimum 6 cell across face & (c) S-parameter Threshold: 0.0025 \\ (d) Edge refinement: 6 additional cells around edges & \\ \hline \hline \end{tabular}
\end{table}
Table A1: Simulation settings in CST Microwave Studio.
# EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
## 2 Reference System
### Pre-Processing  
For this type of system, the automatic detection should be accomplished by just analyzing a small portion of a signal and classifying it as quickly as possible, particularly in an online scenario. By splitting the signal into small packets of data (commonly referred to as trials), training, and testing, a supervised learning system is possible.  
However, the length of a trial cannot be defined a priori thus, a quick analysis to determine an appropriate size for the trials is required. In [9], different trial lengths were evaluated and the optimal one was found based on the classification accuracy. Lengths of 1024, 2048, and 4096 samples were analyzed, and a value of 2048 samples (eight seconds) was found to be the best, resulting in 22 trials per subject/class. Consequently, this value was also used in this work. Therefore, our dataset is stored in a matrix \(\mathbf{R}_{58\times(26\times 22)\times 2048}\).  
Recorded EEG signals usually contain noise and mixed frequencies. These mixed frequencies are partly due the oscillatory rhythms present in normal brain activity [47]. The most studied rhythms are alpha (8-12 Hz), beta (19-26 Hz), gamma (1-3.5 Hz), and theta (4-8 Hz), which are associated with different psycho-physiological states. The alpha waves are characteristic of a diffuse awake state for healthy persons, and can be used to discern between the normal and relaxed states. Actually, in some recordings, alpha waves begin to appear when the subject is starting to relax.  
In this work, band-pass filtering is applied to discriminate frequencies outside the alpha and beta bands. Again, an analysis is required to find out which cutoff frequencies are useful to build the filter by sweeping a range of candidate values. In [9], we presented such an analysis, with the resulting values of 7 and 30 Hz corresponding to the low and high cutoff frequencies using a fifth-order Butterworth filter, matching what other researchers have found as well [48; 49]. The filter response is illustrated in Figure 2.
## 1 Introduction
Next, for fine-tuning the downstream NER network model, we use concatenation of character-level embedding and POS tag features to fine-tune MenuNER, in addition to domain-adapted BERT embeddings (as shown in Figure 3), which replaces the traditional word embeddings used in [21] and adopt a popular NER task network model 'Bi-LSTM+CRF' (as shown in Figure 4). First, we perform ablation test to investigate the impacts of each input feature vector on the adopted NER task network model. It reveals that the POS tag and character-level embedding features and word embedding from the domain-adapted BERT (BERT-domain) have a synergistic effect. Later, the performance of the NER task network model with BERT-domain is compared with the BERT base model (BERT-base-cased) for the food menu NER task. Our findings based on experiments show that the proposed NER task approach based on model adaptation and extended feature vector with Bi-LSTM+CRF network model produces reasonably good performance on an application domain where a large dataset is hardly available and performs significantly better than the baseline BERT-base model. Our main contributions of this paper are summarized as follows:  
* Proposing a simple yet effective NER task approach based on domain adaptation technique with extended input features for an application domain where a large dataset is scarce. * Evaluating the advantage and effectiveness of using domain adaptation and transfer learning techniques for NER task to recognize rich dishes' names in the food domain. * Evaluating the impact of using domain-adapted BERT embedding along with character-level and POS tag feature representation to train the NER task network model with limited datasets. * Achieving better results on the proposed NER task approach compared with baseline models for food menu NER. * Preparing carefully annotated dataset for menu dishes. In the remainder of the paper, we first present an overview of the related work. Then, we present the implementation of transfer learning approach followed by extended feature vectors representation with downstream Bi-LSTM+CRF model for the food menu named-entity recognition. Next, the data used for evaluation is described, followed by the experimental results. Finally, the conclusions of the paper are presented.
## 1 Introduction
In [14], an approach to localization using trilateration is proposed using a UWB radar with an antenna array that supports trilateration for indoor short-range positioning. Other systems integrate other technologies, such as Bluetooth [15], Wi-Fi [10; 16], or radio frequency identification (RFID) [17], although these may require specialized sensors or a thorough mapping of the environment to understand the spatial distribution of signal characteristics [3]. In this work, a solution that uses ToF measurements is presented to determine the relative distances between a robot and optimally placed ultrasound beacons, facilitating accurate absolute positioning within a predefined map. By eliminating the need for synchronization pulses, the system setup is significantly simplified, which reduces the overall complexity. Furthermore, the proposed approach improves real-time performance by achieving a higher sampling rate compared to conventional synchronization-based systems. The remainder of this paper is structured as follows. In Section 2, a localization solution is presented, detailing the principles and implementation of the proposed ultrasound-based system. In Section 3, the implementation details are provided, including the hardware setup and the measurement results. Section 4 presents the comprehensive simulation results and analysis that demonstrate the effectiveness of the proposed method. In Section 5, a discussion of the results obtained and their implications in the field of mobile robot localization is provided. Finally, Section 6 concludes the paper, summarizing the key findings.
## 3 InSAR Adopted Tecnique
### InSAR Results  
In the following, we report the deformation pattern of SP at a large scale retrieved by InSAR processing and discuss the results in detail using available tectonic, stratigraphic, geomorphological and hydrogeological information. We investigate natural and/or human causes of the vertical and horizontal surface displacements.  
The distribution of Vertical component shows a coastal area (from Villapiana Lido to Marina di Schiavonea) characterized by subsidence with velocity up to \(\sim\)-20 mm/yr (Envisat and COSMO-SkyMed sensors); on the contrary, boundary areas of SP show \(\sim\)0 mm/yr on the vertical velocity component (Figure 4).  
The horizontal Eat-West component shows low displacement values that are inside the errors range and are not significant.  
In the archaeological area, the subsidence rate is \(\sim\)2 mm/yr (Envisat) and \(\sim\)3 mm/yr (COSMO-SkyMed) for the Parco del Cavallo site and \(\sim\)2 mm/yr (Envisat) for the Casa Bianca site. These values are comparable to the velocities calculated by previous authors [61; 67].
## 4 Discussion
The ANN-based model developed in our study offers a more cost-effective and user-friendly alternative compared to advanced techniques like Raman spectroscopy. However, methods like Raman spectroscopy will continue to be suitable for detailed analyses with high accuracy rates in laboratory settings. In this context, the ANN-based model can be considered a suitable option for situations requiring faster and more economical solutions [31]. In conclusion, the ANN-based starch adulteration detection model presented in this study stands out for its advantages such as portability, cost-effectiveness, and real-time analysis capability. However, the high accuracy and detail levels provided by spectroscopic and radiographic methods highlight the limitations of this model. Nonetheless, considering the shortcomings of chemical tests, the ANN-based model fills a gap by offering rapid and reliable detection in the field. Future studies could involve the integration of these different methods and testing on larger datasets, leading to more comprehensive and effective solutions for detecting adulteration in dairy products. It is also recommended that the Limit of Quantification (LOQ) be determined to establish the minimal detectable concentration of starch, enhancing the model's applicability across various testing scenarios [18]. It is recommended that tests be conducted on different starch concentrations and milk samples to enhance the model's generalization capacity. Additionally, working on model optimization and integration strategies with alternative detection techniques could further improve the model's effectiveness.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 3 Efficient Fully Homomorphic Encryption Scheme
### The Correctness of the Scheme  
First, correctly analyze the homomorphic addition and multiplication of the scheme. For the homomorphic addition, there will be \(\mathbf{C}=\text{mbFlatten}((\mu_{1}+\mu_{2})\cdot\mathbf{I}_{N}+\text{mbDmp}(C_{1}+C_{2}))\) and \(\text{NFHE.Dec}(\mathbf{sk},\mathbf{C})=(\mu_{1}+\mu_{2})\text{mod}2\). After homomorphic addition is performed on each scheme, the noise will not exceed twice the original ciphertext. For homomorphic multiplication, there will be \(\mathbf{C}\cdot\mathbf{sk}=\mu_{1}\cdot\mu_{2}\cdot\mathbf{sk}+\mu_{2}\cdot\mathbf{e}_{1}+C_{ 1}\cdot\mathbf{e}_{2}\) and \(\text{NFHE.Dec}(\mathbf{sk},\mathbf{C})=\mu_{1}\cdot\mu_{2}\) (\(\mathbf{e}_{1}\) and \(\mathbf{e}_{2}\) refers to the noise in ciphertext \(C_{1}\) and \(C_{2}\)). Since the coefficient of \(\mu_{2}\) is \(\{0,1\}\), and the coefficient of \(C_{1}\) is limited to \(\mathbb{Z}_{p}\), the noise will not exceed \(pN+1\) times the original ciphertext after each homomorphic multiplication.  
For the NFHE scheme, \(L\) represents the maximum depth of the homomorphic operation circuit. In the case of no homomorphic operation, if \(\mathbf{C}\) is the cipher-text obtained by encrypting 0, when \(|\langle C_{m-1},s^{\prime}\rangle|<q/\left[4p(pN+1)^{L}\right]\), the scheme will be correct.  
According to the analysis of the correctness of homomorphic addition and multiplication, after each homomorphic operation, the noise does not exceed pN+1 times of the original ciphertext. Therefore, when \(|\langle C_{m-1},s^{\prime}\rangle|<q/\left[4p(pN+1)^{L}\right]\), after performing no more than L homomorphic operations, \(|\langle C_{m-1},s^{\prime}\rangle|<q/\left(4p\right)\). According to the decryption algorithm, when \(|\langle C_{m-1},s^{\prime}\rangle|<q/\left(4p\right)\), there is \(\langle s^{\prime},C_{m-1}\rangle/\left(q/2p\right)<1/2\). Therefore, if the encrypted message is 0, then \(\langle C_{m-1},s^{\prime}\rangle\) is closer to 0 than \(q/\left(2p\right)\), \(\mu=\lfloor\langle s^{\prime},C_{m-1}\rangle/\left(q/2p\right)+1/2\rfloor \text{mod}2<\lfloor 1/2+1/2\rfloor\text{mod}2=0\). Otherwise, the situation is reversed, and the correctness of the scheme can be guaranteed.  
For the ciphertext obtained by encrypting 0, there are  
\[\left\langle C_{m-1},s^{\prime}\right\rangle=\langle\mathbf{r},\mathbf{s}\rangle+e_{m -1,2}-\left\langle e_{m-1,1},\mathbf{e}\right\rangle\]  
Therefore, as long as the appropriate parameter q is selected, the correctness can be satisfied by making it large enough. \(\square\)