esa-sceva/satcom-chunk-collection huggingface dataset:
# Universally Composable Oblivious Transfer with Low Communication
## 2 Preliminaries
### Universally Composable Security Model  
To describe and analyze the security of cryptography protocols in parallel concurrency, [PERSON] proposed the universally composable security framework, which specifies the existence of \(\mathcal{A}\) (adversary), \(\mathcal{S}\) (simulator), and \(\mathcal{Z}\) (environment) in addition to the parties of the protocol. The protocol can prove universally composable security by showing the indistinguishability between the ideal environment and real execution.  
In the real execution, \(\pi\) denotes the running protocol, and \(\mathcal{A}\) denotes the adversary who corrupts the participant to attack the protocol. In the ideal environment, \(\mathcal{F}\) is the corresponding function of \(\pi\), and \(\mathcal{S}\) is the corresponding simulator of \(\mathcal{A}\). Besides, \(\mathcal{Z}\) can be considered as the external environment that can communicate directly with the protocol participants, the adversary, and the simulator except for the ideal function.  
**Definition 2 (UC-Secure).**_If, for any adversary in the real execution, there exists a corresponding simulator in the ideal environment such that the environment cannot distinguish between interacting with a protocol in the real execution or interacting with an ideal function in the ideal environment, and the formula is as follows:_  
\[IDEAL_{\mathcal{F},\mathcal{S},\mathcal{Z}}\underset{\equiv}{ ^{c}}EXEC_{\pi,\mathcal{A},\mathcal{Z}} \tag{4}\]  
Thus, it can be proven that the protocol \(\pi\) can safely simulate the function \(\mathcal{F}\).
## 1 Introduction  
Modern society is not imaginable without personal virtual assistants and conversational chatbots such as, e.g., Siri, Alexa, Google assistant, Cortana; they are completely changing our communication habits and interactions with technology. Intelligent chatbots are fast and can substitute some human functions; however, artificial intelligence (AI) and natural language processing (NLP) technologies are still limited with respect to replacing humans completely.  
The first chatbot ELIZA [1] invented in 1966 was based on keyword recognition in the text and acted more as a psychotherapist. ELIZA did not answer questions; it asked questions instead, and these questions were based on the keywords in human responses. Currently, there exist different types of chatbots according to communication channels (voice-enabled or text-based), knowledge domain (closed, general, or open domain), provided service (interpersonal or intrapersonal), used learning methods (rule-based, retrieval-based, or machine learning-based), and provided answers (extracted or generated answers).  
Chatbot features such as its appearance/design or friendly human-like behavior are important, but not as important as adequate reactions to human requests and correct fluent answers. Therefore, a focus of our research is on natural language understanding (NLU) (responsible for comprehension of user questions) and natural language generation (NLG) (responsible for producing answers in the natural language) modules. Accurate generative-based chatbots are trained on large datasets; unfortunately, such datasets are not available for some languages. Despite this, companies/institutions often own small question-answer datasets (at least in the form of frequently asked questions (FAQs)) and want to have chatbot technology on their websites.  
In this research, we explore seq2 seq-based deep learning (DL) architecture solutions by training generative chatbots on extremely small datasets. Furthermore, our experiments are performed with two languages: English and morphologically complex Lithuanian. The generative chatbot creation task for the Lithuanian language is more challenging due to the following language characteristics: high inflection, almost twice larger vocabulary (compared to English), rich word-derivation system, and relatively free word-order in a sentence.
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 4 Q-RPL: Q-Learning-Based Routing Protocol Design
### State-Action Space Design  
The definition of state(s) and action(s) is crucial in Reinforcement Learning, particularly when applied to routing in network environments. The state represents the current configuration of the network, and the action signifies the possible decisions a node (an RL agent) can make. We observed in Section 2 that existing studies such as [39; 40] have formulated states using routing metrics from neighboring nodes. Although this approach is intuitive, it introduces significant challenges, particularly in the dimensionality of the state space. If the routing metrics are continuous or exhibit high variability, discretizing the state space becomes necessary to prevent an unmanageably large Q-table. The size of the state space increases with each added metric. For instance, if each of \(m\) metrics can take \(x\) values (either because they are inherently discrete or were discretized), the total state space size would be \(x^{m}\). Subsequently, the state-action space size would be determined by  
Figure 2: The interaction between the agent and its environment in the context of networking.  
multiplying the state space size by the number of potential actions available at each state. This requires careful consideration, especially for resource-constrained devices.  
In our protocol design, we conceptualize states differently. Here, states represent the destination nodes that a node aims to reach, typically the DODAG root in RPL. So, for node \(i\), the state \(s^{i}\in\mathcal{S}^{i}=\{s^{i}_{1},s^{i}_{2},...,s^{i}_{n}\}\), where \(n\) represents the number of destination nodes in the network. This approach is particularly efficient when nodes operate in non-storing mode, where all traffic is directed towards the DODAG root, significantly simplifying the state space. Actions in our model correspond to the choice of the next hop to reach the destination. Thus, actions for a node \(i\) can be expressed as \(a^{i}\in\mathcal{A}^{i}=\{a^{i}_{1},a^{i}_{2},...,a^{i}_{k}\}\), where \(k\) is the number of possible next-hop candidates for this node. Therefore, the dimension of our Q-table, which has a Q-value for each pair of state-action, is proportional to the size of the neighbor table. This design not only addresses the dimensionality and discretization issues but also aligns well with the operational modes of RPL. Table 2 shows a typical Q-table for a node \(i\), where the Q-value for each entry in the Q-table is calculated with the Q-learning formula introduced in Equation (1).
# Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
## 5 A Case of Use of Meteorological Information Service Modeling
### Hardware  
For the development of this case study, a hardware device of the Thinger.io platform will be used, called ClimaStick, which integrates both the sensors and Internet connectivity. This device will allow us to collect weather information over time, without having to have knowledge of electronics, data buses, sensors, etc. The device is based on an Espressif ESP8266 microcontroller, which has a Cortex M0 at 80 Mhz, 4 MB of flash, and 160 KB of RAM. This board also has 802.11 b/g/n WiFi connectivity for its Internet connection, as well as serial port interfaces through USB that allows its programming through the Arduino development environment. The development plate has small dimensions of 37 \(\times\) 17 \(\times\) 5 mm (Figures 16 and 17).  
Figure 16: Thinger.io logical interface.  
This board integrates sensors that allow measuring the temperature, relative humidity, barometric pressure, or brightness, necessary for the development of the practical case. It also integrates other sensors, such as a magnetometer, an accelerometer, and a gyroscope, which can be useful for other practical cases where it is necessary to have inertial sensors. The sensors used by this device, as well as its operating ranges, resolution, and accuracy, are specified in Figure 18.
## 3 Results
### Dry Matter Intake, In Vitro Degradability Parameters of Dry Matter and Neutral Detergent Fiber  
Dry matter intake (DMI) was higher for T1 compared to the other treatments (\(p<0.05\)) (Table 5). Differences were observed between treatments in the values of rapidly degradable fraction of dry matter (aDM), slowly degradable fraction of dry matter (bDM), potential degradability of the dry matter (PDDM), effective degradability of dry matter (EDDM) and degradation rate constant of dry matter (kdDM) (\(p<0.05\)), except to rate passage of dry matter (kpDM) in T2, T3, and T4 (\(p>0.05\)). Treatment control (T1) showed an increase of 37.0% in PDDM and 68.7% in EDDM (\(p<0.05\)), respectively. Differences were observed between treatments in the values of rapidly degradable fraction of neutral detergent fiber (aNDF) (\(p<0.05\)). Furthermore, the slowly degradable fraction of neutral detergent fiber (bNDF), potential degradability of neutral detergent fiber (PDNDF), and effective degradability of neutral detergent fiber (EDNDF) values were affected by treatments (\(p<0.05\)). The highest value of the degradation rate constant of neutral detergent fiber (KdNDF) was recorded in T1 and the lowest in T2 (\(p<0.05\)). LLL and PP did not induce any effect on the rate passage of neutral detergent fiber (KpNDF) (\(p>0.05\)). Additionally, the highest value of KpNDF was recorded in T1 and the lowest in T2 and T4 (\(p<0.05\)). The longer values of the mean rumen retention time of dry matter (MRRTDM) and mean rumen retention time of neutral detergent fiber (MRRTNDF) were observed in T2, and the slowest values in T1 (\(p<0.05\)). True Digestibility In Vitro of Dry Matter (TDIVDM)), Ruminal Fermentation Patterns and Microbial Biomass Synthesis  
There were differences among treatments in the TDIVDM values (\(p<0.05\)) (Table 6). The highest values of TDIVDM were recorded in T1 and the lowest in T2 (\(p<0.05\)). At the same time, the concentrations of NH3-N were significantly affected by the treatments (\(p<0.05\)). In our study, the concentrations of NH3-N in T2 and T4 decreased when LLL replaced alfalfa hay by 20 and 10%, respectively. Moreover, the concentration of total volatile fatty acids (TVFA) in the rumen liquor was statistically similar between T3
## 6 System Evaluation
### Experimental Results  
To evaluate the performance of our SEPIM construction in terms of search efficiency, search precision, effectiveness, and invulnerability to adversaries, we report the experimental results of the proposed scheme on a real image database containing 10,000 color images from the Corel dataset [37]. Our experiments are conducted on a 2.2 GHz Intel i7- 4702 MQ processor, with a Windows 7 operating system of 64-bits, and 8 GB RAM (Lenovo PC HK limited, Hong Kong, China). We use MATLAB R2008a to implement our experiments. We used Java class to implement the Paillier cryptosystem.  
The \(R\) of the index tree is set as 50 in our implementation. For the SURF descriptors, the size of each descriptor is 4096 elements, i.e., \(n=4096\). We also apply PCA transform on SURF vectors as in [22] to achieve dimension reduction. Specifically, we use PCA-2048, PCA-1024, PCA-512, PCA-256, and PCA-128 to denote PCA transforms that reduce the dimension of a SURF vector from \(n=4096\) to \(n=2048\), \(n=1024\), \(n=512\), \(n=256\), and \(n=128\), respectively. The normalized vectors are scaled by a user specific factor to convert the normalization (between 0 and 1) into integer numbers because the encryption function is applied only on integer values.
## 2 Antenna Arrays
### Linearly Arranged Circular Planet Antenna Array (LCPAA)  
We present another linear beamformer in Figure 1c. The beamformer contains a total of 108 elements, divided into three circular arrays of 36 elements each. Each LCPAA has seven rows, with the upper three and lower three being symmetrical. The first, second, and third rows each have three, five, and seven items, respectively, while the middle row has six.
# Robust Federated Learning for Mitigating Advanced Persistent Threats in Cyber-Physical Systems
## 2 Background
### Federated Learning  
FL is a machine learning paradigm that collaboratively trains a global model over several nodes or edge devices while preserving privacy [18; 31; 32]. User privacy in FL is ensured through data decentralization, as each edge device forbids access to the local data it contains [33; 34; 35]. Instead, nodes train a model on their local data and communicate the locally optimized parameters to an aggregator server. The aggregator receives these updates from edge devices and aggregates them into a global model [36]. Then, the parameters of this newly obtained model are sent to the edge devices so that they can update their local models to reflect the latest state of the global model. As this cycle continues, edge devices share the knowledge obtained from the data they contain and can improve their local models based on the information gathered from other nodes through parameter aggregation.  
In the context of CPSs, FL can be used to build a global model to detect intrusions and cyber-attacks without the need to share private data [37; 38; 39]. Each organization or node of the network that has a local malware detection model can constantly improve its model based on the information that other nodes have acquired from their data, without having access to such data. However, employing FL creates an extra communication surface between the nodes of the network. As a result, if a node in this network becomes rogue, it can jeopardize the network through different adversaries [19; 21; 40; 41; 42]. This problem, however, is addressed by employing robust aggregation principles in our network, as explained in Section 3.
## 6 Experimental Evaluation
### Experiment Setup  
The entire project was carried out in the open-source scientific environment Spyder IDE [46], which is a Python-based native application. The entire Fetal Health dataset is randomly split and distributed into six datasets, corresponding to the six edge nodes, for the experimental configuration. The approach divides the dataset into subsets in the current context solely for the purpose of assigning each subset to an edge node and evaluating the associated resource profiles, with no FL involved.
## 8 Conclusions  
As a result of digital transformation, healthcare is becoming decentralized, and reaching out to the network's edge will allow us to better capture data, meet the demands of patients, and foster innovation.The research highlights the importance of optimizing node collaboration and resource utilization, with a specific focus on resource profile federation and resource optimization evaluation. FWP combined with node reputation are two effective measures to ensure optimum edge node selection. In order to analyze and optimize the resources, and select appropriate edge nodes, edge workload profiling and Federated Workload Profiling are proposed to support the deployment of FPSN. Time is often a deciding factor in FPSN and PSN deployments; consequently, our proposed model was successful in selecting the nodes with the quickest response times by facilitating faster convergence of the ML algorithms through resource optimizations. The experimentation conducted on various scenarios validates the effectiveness and performance of the proposed FWP model, showcasing its potential for improving resource utilization in a distributed edge computing environment through reputation-based node selection and a resource-aware federated hybrid profiling strategy. Overall, the proposed model successfully selected edge nodes with quick response times, facilitating faster convergence of machine learning algorithms, and enabling more effective, time-critical, and decision-intensive health applications. Although the computational overhead may increase with an increasing number of nodes, our approach prioritizes a lightweight resource profile federation, ensuring that this potential increase does not pose a significant concern given our focus on efficient resource utilization.  
**Author Contributions:** [PERSON] conceived the main conceptual ideas related to the resource aware FPSN, the architecture, the literature review, and the overall implementation and execution of the experiments. [PERSON] contributed to the formal modeling, the literature review, and the analysis of the results. [PERSON] contributed to the architecture of the model and the edge resources classification, and he ensured that the study was carried out with utmost care and attention to detail, while overseeing the overall direction and planning. [PERSON] was involved in the general evaluation of the proposed model. All authors contributed to the writing of the manuscript and the revision and proofreading of the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the Zayed Health Science Center under fund # 12R005.
## 4 Proposed Scheme
### Scheme Definition  
In this section, we present the scheme definitions. The notations and their meanings, as used in this paper, are presented in Table 1. Our scheme consists of eight algorithms, defined as follows:  
1. \(Init(\lambda)\longmapsto(PK,\ MK_{i},\mathbb{U}_{mix})\): The AA devices participating in the communication of groups \(a,\ b,\ \dots,\ n\) communicate with each other based on the principle of mutual trust. They generate public parameters \(PK\) and master keys \(MK_{i}\) then combine \(\mathbb{U}_{u},\ \mathbb{U}_{b},\ \dots,\ \mathbb{U}_{z}\) to generate the attribute set \(\mathbb{U}_{mix}\).
2. \(AA.KeyGen(PK,\ MK_{i},\ \mathbb{U}_{mix})\longmapsto\left(SK_{CR_{j}}\right)\): This algorithm is executed by the AA device in each group, with inputs including the public parameters \(PK\), the master key \(MK_{i}\) for group \(i\), and the attribute set \(\mathbb{U}_{mix}\). The output is the attribute key \(SK_{CR_{j}}\) for \(CR_{j}\).
3. \(CR.Encryption\left(PK,\ M,\ \mathcal{T},SK_{CR_{j}}\right)\longmapsto(CT_{1})\): This algorithm is executed by the CR, with inputs including the public parameters \(PK\), the plaintext \(M\), and the access tree \(\mathcal{T}\). The output is the first stage of ciphertext \(CT_{1}\).
4. \(CN.Encryption(PK,CT_{1})\longmapsto(CT_{2})\): This algorithm is executed by the CN device in the group, with inputs including the public parameters \(PK\) and the first stage of ciphertext \(CT_{1}\). The output is the second stage of ciphertext \(CT_{2}\).
5. \(CN.VMS(gpk_{t},\ b)\longmapsto true\) or \(\bot\): This step ensures that the message originates from an authorised source, reducing the risks of tampering or forgery. CN selects a random number \(b\), embeds it in the group public key \(gpk_{t}\), and sends it to the recipient for verification. Conversely, if verification fails, the message is rejected.
6. \(CR.token\left(SK_{CR_{j}},\ t\right)\longmapsto\left(tk_{CR_{j}}\right)\): After verifying the message source, the CR executes this algorithm with inputs consisting of its attribute key \(SK_{CR_{j}}\) and a random number \(t\in\mathbb{Z}_{p}\). The output is a one-time proxy key \(tk_{CR_{j}}\).
7. \(CN.Decryption\left(CT_{2},\ tk_{CR_{j}}\right)\longmapsto(IDM)\): The CN executes this algorithm using the second stage of ciphertext \(CT_{2}\) and the one-time proxy key \(tk_{CR_{j}}\) generated by the CR as inputs. The output is a partial decryption message \(Mkey\).  
\(CR.Decryption(CT_{2},\;IDM,\;t)\longmapsto(M)\): The CR executes this algorithm with inputs consisting of the second stage of ciphertext \(CT_{2}\), the partial decryption message \(IDM\), and the random number \(t\) used to generate the one-time proxy key \(tk_{CR_{j}}\). The output is the plaintext \(M\).
## 2 Description of the Method
According to Matrix Theory [28], the last \(N-r\) columns of \(\mathbf{V}\) represent a basis for the null space of matrix \(\mathbf{A}\), therefore can be used to constitute \(\mathbf{F}\). In addition, \(\mathbf{u}\in[-\gamma\mathbf{x_{max}},\gamma\mathbf{x_{max}}]\) are random variables subject to uniform distribution with zero mean, where \(\mathbf{x_{max}}\) is the maximum absolute entity of the current solution vector and \(\gamma\) is an empirically determined non-negative number. The procedure of the proposed array diagnosis method of random search-convex local minimizer is summarized in Algorithm 1. ```
input: measurement matrix \(\mathbf{A}\), far-field data \(\mathbf{y}\) output: recovered sparse vector \(\hat{\mathbf{x}}\) initialization: the number of random starting point \(Q\), counter \(j\)=0, non-negative number \(\gamma=2\) for\(j=0\) to \(Q\)-1 do
1. set an initial starting point \(\mathbf{s}_{j}\)
2: generate an initial solution vector \(\mathbf{x}_{j}\) via MCCA using \(\mathbf{s}_{j}\)
3: perturb \(\mathbf{x}_{j}\) to get a new starting point \(\mathbf{s}_{j+1}=\mathbf{x}_{j}+\mathbf{n}_{j}\), where \(\mathbf{n}_{j}=\mathbf{Fu}_{j}\)
4: get an updated solution vector \(\mathbf{x}_{j+1}\) via MCCA using the new starting point \(\mathbf{s}_{j+1}\)
5: if \(||\mathbf{x}_{j+1}||_{p}\leq||\mathbf{x}_{j}||_{p}\), accept \(\mathbf{x}_{j+1}\), otherwise accept \(\mathbf{x}_{j}\) to replace \(\mathbf{s}_{j}\)
6:\(j=j+1\) endfor
```  
**Algorithm 1** The Hybrid Array Diagnosis Method of Random Search-Convex Local Minimizer