esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simple Approach to Dynamic Optimisation of Flexible Optical Networks with Practical Application
## 3 Formulation of the Problem  
In this paper, we use a simple model of the problem, with possible extensions considered in Section 7. Some of these extensions are, in fact, straightforward, but are omitted initially to minimise the requirements of the initial notation. Additionally, in what follows, we use a number of standard logical, set-theoretic, and optimisation-based concepts and abbreviations, namely: there are \(n\) nodes in the ON/FON, numbered \(1\), \(2\),..., \(n-1\), \(n\); the logical operators 'AND' and 'OR' are represented by '\(\wedge\)' and '\(\vee\)' respectively; 'Such that' is abbreviated to '\(s.t.\)' and 'TRUE' and 'FALSE' to '\(T\)' and '\(F\)'; \(|S|\) represents the size ('cardinality') of the set \(S\); and, finally, the universal and existential operators '\(\forall\)' and '\(\exists\)' ('for all' and 'there exists') refer explicitly to the set of nodes \(1\), \(2\),..., \(n-1\), \(n\) (hereafter omitted).  
For a given node set and link configuration (Figure 1), define the _relay vector_, \(\underline{\mathit{L}}\) as: \(\underline{\mathit{r}}=(\mathit{r}_{k})\) where \(\forall k\), \(\mathit{r}_{k}=T\) if the relay at node \(k\) is switched on (powered); \(\mathit{r}_{k}=F\) otherwise. \(\underline{\mathit{r}}\) defines relay use for a given configuration. Initially, we suppose that relays are placed at all nodes but are only activated when required (see Ext1 in Section 7). Let \(m\) be the maximum number of hops for which an optical signal can be transmitted without the need for regeneration by an OEO (relay). A hop is a single link between adjacent nodes (see Ext2 in Section 7). The critical feature of a FON is that changing transmission parameters, to dynamically increase or decrease bandwidth, will cause \(m\) to vary (\(m\) will decrease with higher bitrates and increase with lower bitrates). Initially, we take \(m\) to be a single (but not static) parameter across the FON (see Ext3 in Section 7). As \(m\) varies, different requirements will be placed (dynamically) on relay placement, \(\underline{\mathit{r}}\). In the FON fragment in Figure 1(a), for example, values for \(m\) of \(1\)2, and 3 cannot be sustained, whereas an \(m\) value of 4 can. In Figure 2b, now with \(r_{k}=T\), values of \(m=4\), \(m=3\), and \(m=2\) can all be sustained, and only \(m=1\) cannot.  
The _cost_ of a solution, \(L\) is then the _number of relays switched on_ in any given configuration. \(C(t)=|\{k:r_{k}=T\}|\) (the cardinality of the set of powered relays).  
The optimisation problem 'Dynamically Powered Relays for a Flexible Optical Network' (DRP-FON) is, thus, defined as:  
DRP-FON: Minimise C(r) [objective function] s.t. m [constraint]  
**Theorem.**_DRP-FON is NP-Complete._  
**Proof.** (informal) Intuitively, by direct reduction to the 'Minimum Dominating Set' (MDS) [19] (note, not MCDS [20], since the relays do not, themselves, have to form a connected core) and demonstration of polynomial certification (the full version follows).
## 5 An SOA-Based Platform Example and Case Study Results
### Additional Details on the Results from the Thinguwave AB Case  
ThingWave AB has made its own IoT platform, based on ideas and concepts from the Arrowhead Framework, and uses it to build a number of customer offerings regarding remote monitoring and management of industrial processes and critical infrastructures. Examples of offerings are ground support monitoring (IoT enabled rock bolts and seismic sensing for underground cavities), digital twins, smart ventilation, and condition monitoring for industrial machinery. The investment made in the development of the IoT platform has been significant for an SME, but now it pays off in terms of business development wins where prototypes and integrations between systems can be shown to work with about 25% of the effort expected by customers. The development phases are also considerably faster by using strict service descriptions, microservices, and new own developed communications protocols for fast and efficient sensor communication in order to reduce power consumption and improve data rate. This also enables connection of machines and equipment not yet connected (having communications interfaces) to remote monitoring and management. ThingWave AB uses more and more IT-equipment in OT-environments (production and distribution), which is a benefit due to lower prices, and these are faster/easier to replace than vendor-specific OT-equipment. Thus, ThingWave AB is not interested in going back to traditional hard-coded development and integrations with hard dependencies to vendor-specific OT-equipment or systems. The agile process and microservices are a great benefit, as customers can, after a prototype is working, quickly replace it with a newly tested and verified microservice, which is put into production, due to the isolation and limited dependencies to others. Thus, the test and verification effort (i.e., quality assurance) has also been speeded up, and fewer bugs slip through and are faster to rectify if needed. Regarding the installation/commissioning and operations phases, it is within these areas that ThingWave AB sees the greatest potential to streamline the efforts required in terms of hands-on activities, and to operate and maintain an automation solution and its integrations. Remote monitoring and fleet management functionality, such as: plug-and-play, self-configuration, group configurations, secure upgrading of software, etc., all reduce the need to walk around a mine, mill or factory in order to ensure that all software and configurations are up-to-date and working.
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 4 Proposed Encryption Algorithm
### Encryption Steps
```
\(image\leftarrow\text{readImage}()\) \(hash\leftarrow\text{SHA256}(image)\) \(k1\leftarrow\text{least significant 128 bits of }hash\) \(k2\leftarrow\text{most significant 128 bits of }hash\) \(x0_{k}1,c_{k}1\leftarrow\text{DeriveFromHash}(k1)\) \(x0_{k}2,c_{k}2\leftarrow\text{DeriveFromHash}(k2)\) sequence\(1\gets f(k1,\text{imageDimension})\) permuted\(Image\leftarrow\text{PermutePixels}(image,sequence1)\) sequence\(2\gets f(k2,\text{image.size})\) ciphertext\(\leftarrow\text{confusePixels}(permutedImage,sequence2)\) functionDeriveFromHash(key) \(x0\leftarrow\text{first 64 bits of }key\) \(c\leftarrow\text{last 64 bits of }key\) return\(x0,c\), endfunctionfunctionPermutePixels(image,sequence1) foreachpixelinimagedo \(index\leftarrow\text{getNextIndex(sequence1)}\) \(PImage[index]\gets pixel\) endfor return\(PImage\) endfunctionfunctionConfusePixels(image,sequence2) cipher\(Image\leftarrow\text{createEmptyImage}(image.size)\) foreachpixelinimagedo \(encryptedPixel\gets pixel\oplus sequence2\) cipher\(Image.append(encryptedPixel)\) endfor return\(cipherImage\) endfunction
```  
**Algorithm 1** Proposed Image Encryption Algorithm
## 2 Materials and Methods
### Classification Models
#### 2.3.2 Feature Extraction Based on MAAE  
Although the adversarial method absorbs the advantages of the generative method and the contrast method, it may lead to unstable convergence. Based on adversarial thoughts, adversarial autoencoders (AAE) apply adversarial restrictions to latent codes. When the adversarial network (GAN) is trained with latent codes and samples, which are randomly selected from the normal distribution, they are changed into desired ones. The distribution of latent codes generated by the AAE algorithm differs from that of the VAE algorithm.  
Similar to the MVAE module, the MAAE module first adopts the PCA algorithm to reduce the dimension of the HSI. Figure 4 shows the process of extracting features from the MAAE module for HSI, where the encoder outputs the latent code directly and acts as a  
Figure 3: Feature extraction with MVAE for HSI (h-MVAE).  
GAN generator, and decoder modules are similar to MVAE. In h-MAAE, the latent code is both the input of the discriminant and the output of the generator. In addition, another input to the discriminant is a random variable that draws from a normal distribution of samples.  
The training of MAAE consists of reconstruction and regularization parts. In the part of the reconstruction, the reconstruction loss is used for calculating loss presented in Equation (5), where \(L_{AAE}^{re}\) denotes the reconstruction loss function, which is defined in the same way as \(L_{re}\). In another part, the decoder and encoder are optimized in order. To stabilize the training process of GAN, the Wasserstein GAN (WGAN) loss is used for the discriminator and encoder optimization. The discriminant loss and encoder loss functions are represented by Equations (6) and (7), respectively, where, \(L_{d}\) denotes the discriminator loss and \(L_{g}\) denotes the generator loss. \(P_{g}\) denotes the generated sample distribution, which is represented as latent code, \(P_{r}\) is the actual sample that is drawn from the normal distribution, and \(x\) denotes the random sample of \(P_{g}\) and \(P_{r}\).  
\[L_{AAE}^{re}=\sum_{i=1}^{N}\left(\frac{1}{a\times a\times c}\sum_{x}^{a-1}\sum _{y}^{a-1}\sum_{z}^{c-1}(I-\bar{I})^{2}\right) \tag{5}\]  
\[L_{d}=E_{x\mid P_{g}}(D(x))-E_{x\mid P_{r}}(D(x)) \tag{6}\]  
\[L_{g}=-E_{x\mid P_{r}}(D(x)) \tag{7}\]  
Similarly, HSI and MSI are pre-trained by AAE to generate two coding features, respectively. Images of two sources are mapped to the same subspace to generate their correlation characteristics, and the extracted features are input to the downstream task of tree classification as data augment.
# An Evolutionary Computation Approach for Twitter Bot Detection
## 3 Data Preparation  
There are various datasets that are publicly available on the Internet and that can be adopted for Twitter bot detection task [36; 37; 48; 49; 50; 51]. Apart from _TwiBot-20_, _midterm-18_ is the largest dataset containing Twitter bots. The drawback is that it is focused on a political-related domain and it does not provide data that can be associated with users involved in more general topics. Most of the available datasets provide only property (user account attributes) information while missing semantic (tweets posted) and neighbor (followers and followings) information. Therefore, low user diversity, data scarcity, and limited user information are the main problems encountered in the aforementioned datasets. For these reasons, to build our detection models, we decided to adopt the _TwiBot-20_ dataset, which was kindly granted to us by its authors [PERSON] et al. [6]. _TwiBot-20_ is currently the only dataset that provides property, semantic, and neighbor information at the same time. Specifically, it includes 229,573 users (human and bot accounts), 8,723,736 properties, 33,488,192 tweets, and 455,958 neighbors, and it is by far the largest available dataset for Twitter bot detection. Additionally, it contains user accounts related to a variety of domains (politics, business, entertainment, and sport) and located in various geographical locations, in contrast to other available datasets that focus on collecting information for specific topics and that are limited in size. The dataset of [PERSON] et al. [6] was used as a benchmark for the state-of-the-art methods and therefore it probably represents the best choice for building and evaluating novel detection algorithms as a result of the high number of baselines that it offers. Furthermore, as stated in the aforementioned paper, _TwiBot-20_ is much more challenging than other available datasets, and this allows the building of more robust models. State-of-the-art methods fail to achieve extremely high performance when trained and evaluated on _TwiBot-20_, therefore, this dataset may help to define models with better generalization capabilities.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 6 Trust Model and Calculation Techniques
### Trust Ageing  
Trust models sometimes use historical values of trust to build the reputation of a node or to factor it into the trust calculations. Of course, old trust values should not have as much bearing as more recent values; thus, some models take this into account by using methods such as weighting the old trust values less as they age [20], or using a decay function for the trust values over time [16; 19]. This method allows trust to decay over time if no interactions are being created between the respective nodes.  
The most commonly used form of managing old trust values is the sliding time window method [12; 23; 25; 28; 29]. This method uses a time window which includes the most recent trust values calculated (the number of trust values included can vary; here, we denote it as N). When a new trust value is calculated, it is added as the most recent value, and the time window \"slides\" over to include this value. Then, the N+1 most recent value is dropped from the window and is no longer used in any further calculations.  
An interesting approach to trust ageing is taken in [16]. Along with the use of a decay function, here a trust maturity method is used. This method is based on the number of interactions between the given nodes and determines the point at which only the current direct assessments are necessary for calculating the trust between these two nodes. This means that there is no further need to gather the recommendation trust or rely on previous trust values for the rest of the session. This can reduce overhead in the trust calculation, increasing network efficiency.
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
###### Abstract  
Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in electricity load forecasting. However, LSTM and its variants are not sensitive to the dynamic change of inputs and miss the internal nonperiodic rules of series, due to their discrete observation interval. In this paper, a novel neural ordinary differential equation (NODE) method, which can be seen as a continuous version of residual network (ResNet), is applied to electricity load forecasting to learn dynamics of time series. We design three groups of models based on LSTM and BiLSTM and compare the accuracy between models using NODE and without NODE. The experimental results show that NODE can improve the prediction accuracy of LSTM and BiLSTM. It indicates that NODE is an effective approach to improving the accuracy of electricity load forecasting.  
neural ordinary differential equation; LSTM; bidirectional LSTM; short-term load forecasting +
Footnote †: journal:  
Article  
A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 3 Theoretical Framework
### AE Source Location  
In this study, the linear localisation of Acoustic Emission (AE) sources is performed using the Arrival Time Method [28; 29; 30]. This technique is based on the comparison of the Arrival Times of AE signals at different sensors arranged along the monitored element. The Arrival Time is defined as the instant at which the signal amplitude first exceeds the threshold level. Assuming a constant propagation velocity within the material, the position of the AE source can be estimated by solving a system of equations derived from the time differences between sensors.  
Given the known distance D between two sensors, S1 and S2, the distance L1 from the AE source to sensor S1 can be determined using the following expressions:  
\[\text{L}_{1}\text{= c T}_{1}\text{L}_{2}\text{= c T}_{2};\text{L}_{1}-\text{L}_{2} \text{= c }(\text{T}_{1}\ -\text{T}_{2})=-\Delta\text{T c} \tag{2}\]  
\[\text{L}_{1}+\text{L}_{2}\text{= D};\ \text{L}_{1}\text{= 0.5 }(\text{D}\ -\ \Delta\text{T c}) \tag{3}\]  
where T1 and T2 are the Arrival Times of the signal at sensors S1 and S2, \(\Delta\text{T}\) is the difference T2 \(-\) T1, and c is the acoustic wave velocity.  
Despite its simplifications, this method is particularly effective in masonry structures where full 3D localisation is often unfeasible due to limited sensor accessibility and the heterogeneous nature of the material.
## 4 Simulation and Experiment Results
### Experimental Results in a Realistic Environment
#### 4.2.2 The Calibration Method  
In this experiment, 10 microphones to be positioned (MTP) (m1, m2,... m10) were used as observation landmarks, and they were simultaneously powered, randomly distributed in the environment, and guaranteed to be in the same plane as the sound source array. Since the microphones were remotely controlled by Xshell to collect sound signals, the quality of the network signal and the speed of the signal propagation could lead to measurement errors. In response to this situation, this paper proposed a calibration method to eliminate this error. Take m0 and m1 as an example: we placed a speaker named \"calibration sound source (s0)\" in the middle of m0 and m1. First s0 sounded, and then s1 sounded. The sound signals received by m0 and m1 are shown in Figure 14. Since the distances between s0, m0, and m1 were equal, the starting positions of the first speech segments of m0 and m1 should be the same. However, analyzing Figure 14, it was found that there were errors. An enlarged view is shown in Figure 15. The difference between the starting positions of the two speech segments was taken as calibration value
# Thermal Comfort Applied in Hospital Environments: A Literature Review
###### Abstract
Twenty-three years after the publication of the [PERSON]'s study and nine years after the publication of the first version of ISO 7730 (1984), [PERSON] and [PERSON] [16] conducted a study at the Bologna General Hospital, using the PMV index. It was found that in most of the rooms analyzed, [PERSON]'s index was not in the range of thermal comfort, especially in the summer. [PERSON] et al. [17] examined thermal comfort in an Italian hospital considering the differences between gender and age of staff and patients. In order to do this, the authors collected the actual mean vote (AMV) from 30 patients and 19 medical teams for comparison purposes, in order to determine differences between PMV and AMV. [PERSON], [PERSON] and [PERSON] [18], in a recent study performed in a health center in Italy, compared the thermal sensation vote (TSV) in pregnant women with the predicted mean vote (PMV), showing that the PMV underestimates the real thermal sensation. Not only studies in hospitals have been performed in the past few years. [PERSON] et al. [19], for instance, studied thermal comfort in 19 elderly centers in Hong Kong, with thermal comfort being one of the aspects assessed to determine indoor environmental quality (IEQ) for patients and staff. [PERSON], [PERSON], and [PERSON] [20] explored adaptive behavior to compensate for the lack of thermal comfort in five elderly centers in Australia, as well as to assess the perception and preference of the occupants. Studies in health centers were also performed over the last few years. [PERSON] et al. [21], in Italy, conducted a comparative study between real thermal sensation votes and the sensation calculated by PMV; they concluded that PMV predicted well the real thermal sensation reported by people. Although there is a large number of studies on thermal comfort in the literature, there are only few that focus on the review of thermal comfort literature in hospital environments, which makes it difficult to find studies that directly investigate the effects of thermal comfort on health in this type of environment [22]. Thermal comfort in hospital environments is mandatory, as the nature of patients' sickness directly changes their thermal sensation, metabolic rate, and regulatory response. A good environment contributes a lot toward patient recovery and wellbeing, the primary focus of any hospital. Additionally, the evaluation of thermal conditions and their requirements plays a critical role in verifying which critical settings may affect medical staff performance. Then, with this motivation, this paper performed a literature review with papers published from 1968 until August 2020, which apply the concepts of thermal comfort in hospitals, health centers, and elderly centers, aiming to answer four research questions (RQs) proposed in this paper, as well as to verify the main characteristics of these studies.
## 3 Results and Discussion
### Future Research Directions  
Future studies should validate our findings under application-relevant conditions. Static immersion test, in which battery cells or modules are submerged in coolant to simulate insulation failure scenarios, recommended by Sandia National Laboratories, allows the evaluation of the coolant electrolysis across different battery designs, states of charge, and overpotentials [36]. Additionally, circulating leak simulations can reproduce flow and pressure conditions typical of real-world systems. These dynamic tests have shown that coolant circulation is a major accelerator for hydrogen generation, highlighting the need for further exploration under operational conditions [37]. The growing recognition of coolant electrical conductivity as a safety parameter, as formalized in the forthcoming Chinese standard GB 29743.2, shows the demand to further study dedicated BEV coolants from laboratory to real-world scales [38].
# Numerical Assessment of the Seismic Vulnerability of Bridges within the Italian Road Network
## 2 Case Studies and Modeling
### Characteristics of the Case Studies  
The five case study bridges are located in Italy. The five case study bridges have been selected because they belong to common structural types within the existing bridges of the Italian Road Network. At the same time, the fact that some characteristics of the case studies are different allows for the evaluation of the procedure proposed by the Guidelines in different scenarios. Some common elements are present in the selected case study bridges. All considered bridges were built between 1960 and 1973, like most of the existing bridges in the country [27]. All the considered bridges are beam bridges. The total length ranges from a minimum of 50.00 m (Bridge 5) to a maximum of 421.10 m (Bridge 1). In two cases (Bridge 1 and Bridge 3) the structural scheme involves the presence of Gerber saddles. In terms of piers, Bridge 1 has frame piers with two columns and transverse connecting elements. Bridge 2 has two elements with rectangular sections connected on top by a slab. Each pier of Bridge 3 is characterized by eight columns monolithically connected with the superstructure. Bridge 4 has a scheme in which each pier consists of three columns and a pier cap, and Bridge 5 involves piers having two columns and a pier cap. Table 1 summarizes the main characteristics of the case studies. The available documentation provides information about the structural elements, both in terms of sections and reinforcement, allowing accurate modeling of the structures. For the material properties, the documents report information about the original materials. Results of experimental tests performed on the structures have been taken as a reference for concrete properties when available. This is particularly important for the case of Bridge 3, as the available documentation highlights damage to the structural elements of the bridge. In the cases test results were not available, assumptions about the deterioration of materials have been made. For the steel properties, the study of [PERSON] et al. [28] has been taken as a reference. The available documentation also provides information about the soil category of the sites.