esa-sceva/satcom-chunk-collection huggingface dataset:
# Users Empowered in Smart Grid Development? Assumptions and Up-To-Date Knowledge
## 3 Results
### Demand Shifting
[PERSON] [12] nicely showed how, in addition to individual learning, community interaction in the form of workshops and informal encounters with neighbors contributes to effective demand shifting. In conclusion, it can be stated that the insights about demand shifting have considerably increased in the past years due to a large number of studies. It has been shown that there is certainly some room for triggering active responses from users, even though the relationship between incentive and energy consumption is mediated by practices and motives other than that intended with the measure. The findings of the studies provide some clear rules of thumb. However, whether and how measures for automation, financial incentives, and feedback mechanisms are effective depends ultimately on the specific features of the local energy system and the local setting, such as the relationships between residents and the project managers, conventions embedded in culture, weather conditions, etc. This was well illustrated in the comparative study by [PERSON] [21], which showed the outcomes of different settings and interventions. The first group of users had photovoltaic (PV) panels and a display showing their domestic consumption. This group was stimulated to do financial calculations of the revenues of possessing PV. The second group had PV and a device showing the moments of available electricity. The users were stimulated to change their routines of washing and dishwashing (modestly), among other things, by a timer on the washing machine. They were also engaged in energy management by checking weather forecasts. The third group was provided with PV and a water tank that automatically absorbs excess generation. This group started shifting their showering practices from the morning to the daytime or evening--a response that is rather surprising in the light of the social practices studies mentioned above.
# The Algorithms of Image Processing and Analysis in the Textile Fabrics Abrasion Assessment
## 3 Materials and Methods
### Experiment  
The tests were carried out according to following stages:  
* determination of abrasion resistance according to: EN ISO 12947-2:2016 (Fabrics 2,4,5,6), EN 14465:2003 Appendix A (fabric 1) and EN ISO 5470-2:2003 for Fabric 3,
* determination of the loss of mass during the abrasion process according to EN ISO 12947-3:1998 for all fabrics;
* acquisition of image samples during the whole abrasion process, after each interval according to an adequate standard;
* processing of images to prepare them for the next step of evaluation using the instrumental method;
* preliminary test, involving the previous mentioned algorithms: FHT transform, high- and low-pass filtering, Gabor filtering and calculations of histogram-based parameters;
* choice of algorithm, which is the most sensitive as possible to surface changes occurring during each stage of the abrasion process for all tested fabrics;
* analysis of numerical values collected from the image analysis of the instrumental method and their assessment in the scope of sensitivity to abrasion-type surface changes;
* comparative analysis of results obtained by both instrumental (the best algorithm) and standard methods.  
Presented above, the instrumental method for abrasion resistance assessment is based on simple image analysis algorithms. The source for the calculations of image features is an image histogram, which is easy to achieve, low-time consuming and an effective source of many texture indicators. In preliminary tests, more complex algorithms were applied for image feature extraction but none of them presents sensitivity to texture changes on an abraded fabric surface. More elaborate techniques and algorithms could be tested or designed for this specific purpose but the main goal of this research is to find the easiest and fastest techniques for the identification of abraded fabrics' texture. In the  
Figure 3: The scheme of the instrumental method.  
future, this tiny but efficient algorithm could be easily implemented as a part of a Martindale device, to help its operator to carry out an abrasion test.
# Overview of Mobile Communications in Colombia and Introduction to 5G
## 1 Introduction
### Objectives and Motivation  
The main objective of this article is to present the current panorama of mobile telephony in Colombia, considering state policies and the strategies of the different market players. The specific objectives of this article are:  
* To present the current state of mobile cellular systems in Colombia.
* To document the strategies to be implemented by the Colombian state and mobile operators for the classification of 4G networks and the introduction of 5G.
* To identify the situations that have prevented the implementation of 5G in Colombia.  
This document will become a reference source for decision makers involved in the planning and implementation of mobile communication systems, not only in Colombia  
Figure 4: Specific objectives and lines of action of the 5G Plan. Source: [46].  
but also in other countries with similar situations in terms of telecommunications regulation and spectrum management. It will also be a roadmap for researchers who wish to carry out projects or intend to support communication or data transmission systems over cellular networks, allowing them to identify the weaknesses and strengths of the mobile telecommunications system in Colombia.  
This document is structured as follows: Section 2 explains the methods of querying and filtering the information used as a reference in the article. Section 3 provides an analysis of the current state of 4G networks in Colombia, as well as the future of 5G and the factors that have prevented its implementation. Finally, Section 4 presents the conclusions.
## 1 Introduction
### Structure of the Paper  
This paper develops across five main sections. In Section 1, the introduction and the aims of this paper are presented, and a general overview of the role played by the University of Verona is provided.  
Section 2 focuses on the following methodologies used in the preliminary work phases for virtual modelling: the three-dimensional surveys of the archaeological sites and the application of the Extended Matrix for the scientific mapping of the reconstructive hypothesis.  
Section 3 presents a historical and structural description of the archaeological context and the different phases of use for both sites not detailed and exhaustive, but sufficient to explain how we proceeded in re-elaborating the 3D reconstructions. We then focus on the selected periods and environments for reconstruction, providing a brief explanation of the reconstruction hypotheses. Regarding the virtual reconstructions, in the case of Sirmione, we focused on the exterior of the villa to highlight the close structural and aesthetic relationship it had with the surrounding lake landscape (Figure 2). For the villa of Desenzano, where the direct connection with the lake has been lost due to the intense urbanization of the town, we instead exclusively focused on the interior views, characterized by rooms that preserve wonderful polychrome mosaics and fragments of wall paintings. In this case, in the 3D views, we also tried to imagine the furnishings of the rooms to populate these spaces where the _donimus_ welcomed his guests and celebrated and promoted himself through their splendour (Figure 3).  
Section 4 discusses e-Archeo 3D, the web app created based on the previously described work, and describes the narrative choices that characterized the stories of the two sites.  
Section 5 is dedicated to general conclusions on the role played by the University in the e-Archeo project and in new future scenarios.  
Figure 2: Virtual reconstruction of “Grotte di Catullo”, Sirmione (e-Archeo).
## 3 Materials and Methods
### Methods
#### 3.2.1 Decentralized Identity Creation and Registration  
The first step is the creation of a unique decentralized identity (DID) for the Raspberry Pi. This involves the following.  
1. Key Pair Generation: Using cryptographic functions, a pair of asymmetric keys (public and private) is generated. The public key is used to create a unique DID by hashing it to ensure immutability and uniqueness. The private key remains stored securely in IOTA Stranghold.
2. DID Document Creation: A DID Document (DDo) is created, containing metadata about the identity, including * The generated DID itself, acting as a unique identifier. * Cryptographic verification methods, specifying the public key for authenticating the device. * Access control capabilities, enabling updates to the DDo only by the legitimate owner. * A proof of the DID's authenticity, created by signing with the private key.
3. Publishing to IOTA Tangle: The DDo is then published to the IOTA Tangle, a Directed Acyclic Graph (DAG) serving as a public, immutable ledger. This process makes the identity available for verification by other devices or entities without requiring a central registry.
## 3 Material and Methods
### Chemicals  
All reference compounds were purchased from commercial sources.  
Experimental distilled water was purchased from Watson Water Company, Inc. (Guangzhou, China). Dimethyl sulfide (97%), 2-methylbutanal (\(\geq\)97%), 3-methylbutanal (\(\geq\)97%), 2-ethylfuran (\(\geq\)97%), pentanal (\(\geq\)97%), hexenal (\(\geq\)97%), dimethyl disulfide (\(\geq\)97%), 1-penten-3-ol (\(\geq\)97%), \(\beta\)-myrrene (\(\geq\)97%), 2-heptanone (\(\geq\)97%), methyl hexanoate (\(\geq\)97%), D-limonene (\(\geq\)97%), (E)-2-hexenal (\(\geq\)97%), 2-pentylfuran (\(\geq\)97%), \(\beta\)-ocimene (\(\geq\)97%), styrene (\(\geq\)97%), 2-methylpyrazine (\(\geq\)97%), 2-pentenol (\(\geq\)97%), 2,6-dimethylpyrazine (\(\geq\)97%), 2-ethylprazine (\(\geq\)97%), 6-methyl-5-hepten-2-one (\(\geq\)97%), 1-hexanol (\(\geq\)97%), (Z)-3-hexen-1-ol (\(\geq\)97%), 1-octen-3-ol (\(\geq\)97%), furfural (\(\geq\)97%), cis/trans-linaloxide (\(\geq\)97%), 2-ethyl-1-hexanol (\(\geq\)97%), (E), E-2-4-heptadienal (\(\geq\)97%), benzaldehyde (\(\geq\)97%), linaloal (\(\geq\)97%), longidene (\(\geq\)97%), (E,E)-3,5-octadien-2-one (\(\geq\)97%), isophorone (\(\geq\)97%), 5-methylfurfural (\(\geq\)97%), 4-tepinenol (97%), \(\gamma\)-butylrolactone (97%), hotrienol (97%), 1-ethyl-1H-pyrrole-2-carbaldehyde (\(\geq\)97%), beta-cyclocitral (\(\geq\)97%), (IR)-(-)-myrrenal (\(\geq\)97%), benze-neacetaldehyde (\(\geq\)97%), furfuryl alcohol (\(\geq\)97%), real (\(\geq\)97%), \(\alpha\)-terpineol (\(\geq\)97%), benzyl acetate (\(\geq\)97%), naphthalene (\(\geq\)97%), methyl salicylate (\(\geq\)97%), nerol (\(\geq\)97%), hexanoic acid (\(\geq\)97%), \(\alpha\)-methylbenzyl alcohol (\(\geq\)97%), geraniol (\(\geq\)97%), \(\alpha\)-ionone (\(\geq\)97%), \(\beta\)-ionone (\(\geq\)97%), benzyl alcohol (\(\geq\)97%), phenylethyl alcohol (\(\geq\)97%), (E)-3-hexenoic acid (\(\geq\)97%), (E)-2-hexenoic acid (\(\geq\)97%), 2-acetyl pyrrole (\(\geq\)97%), \(\beta\)-damascenone (\(\geq\)97%), 2-phenyl-2-butrenal (\(\geq\)97%), cis-jasmone (\(\geq\)97%), 2-formyl-1H-pyrrole (\(\geq\)97%), 3-methylinone-2,4-dione (\(\geq\)97%), 4-methoxybenzaldehyde (\(\geq\)97%), geranic acid (\(\geq\)97%), dihydroactinidiolide (\(\geq\)97%), benzoic acid (\(\geq\)97%), coumarin (\(\geq\)97%) were purchased from Alfa Aesar Corporation (Tianjin, China). Alkanes in solution (C5-C30) and internal standard 1,3-dichlorobenzene were purchased from ANPEL Laboratory Technologies Inc. (Shanghai, China).
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 5 Reviewing Dependable Quality
Such evaluations assume that the application is available if some requirements are fulfilled, such as target _k_-coverage [27], a minimum area coverage rate [6; 51; 52] or even a simultaneous coverage of targets and area [35]. For this, it is considered hardware failures (hardware malfunction, battery discharging), communication failures (loss of path to a sink node) and coverage failures (loss of view, occlusion, low content quality). Even without a proper dependability assessment, other metrics can be used to infer the system dependability, such as redundancy [92; 93]. In this case, availability is related to the redundancy level of coverage from the nodes, taking into account the percentage of coverage and the admissible angle of sensors orientation [92]. Occlusion can be considered as a redundancy parameter, altering how the camera's field of view is computed [93]. These works consider that an application is as available as its redundancy level. Other metric that has been associated with availability is the Effective Target Viewing (ETV), which specifies the amount of covered parts of targets' perimeters [94]. That metric can be used in WVSN monitoring applications to evaluate availability.That way, ETV is related to an binary availability, which means \"yes\" (available) or \"no\" (unavailable), regarding to the defined Minimum admissible ETV (M-ETV). Finally, the application performance reliability can be described through the required and provided Quality of Information (QoI) thresholds. QoI is defined as data suitability for a given application or a decision making process, and it can be quantified by a peak signal-to-noise ratio [78]. Table 3 summarizes the surveyed works addressing dependable quality. We verified that coverage redundancy plays a very important role when assessing dependable quality, and the majority of the works discuss dependability issues considering the impact of power consumption. It is also possible to notice a lack of attention to occlusion in this context, as well as the assessment of dependable quality in barrier coverage applications. An important remark is that some of the associated aspects of quality of visual monitoring appear in more than one category. For instance, occlusion is an aspect that affects all categories, but in different ways. Obviously, the spatial coverage quality is reduced if a camera can not cover a region blocked by an obstacle. Regarding the content quality, the amount of information in gathered images also reduces if the camera's field of view is reduced. Finally, when dependable quality is considered, a visual sensor node can become useless, incurring in a visual coverage failure in the application network. Therefore, that overlapping of different aspects among the categories is an expected scenario.
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 5 Performance Analysis  
The security performance, communication overhead, and storage overhead for DSEDA [1], PDF [19], and PKAEF are analyzed and compared in this section.
## 4 Results and Discussion
### Comparison with the Hybrid Approach  
In this section, another hybrid experiment is performed to classify brain tumors into meningioma, pituitary and glioma to assess the efficacy of the identified best TL model, i.e., Inceptionresnetv2. It has been claimed that using an SVM classifier instead of typical deep neural networks at the top of the net significantly improves classification performance [32]. Hence, we designed a hybrid approach in which we used the twelve most famous deep neural networks for in-depth feature extraction and used these features as inputs to train SVM with a linear kernel. We used Mobilnetv2, Densenet201, SqueezeNet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet in the proposed work. The dataset images were resized differently according to the image input requirements of the deep neural networks by using augmented image data stores before inserting them into the DL network for feature extraction. We applied activations on the last global average pooling layer (a deeper layer) to extract high-level features. The classification results of deep features and the SVM approach are presented in Table 4. This experiment shows that the deep features of all twelve networks and the SVM approach achieved lower accuracy results compared to the TL of Inceptionresnetv2. An accuracy of 98.91% signifies the effectiveness of the Inceptionresnetv2 deep neural network for reliable tumor classification.
## 4 Experiment
### Cross-Model Evaluation with Human Users Involved
#### 4.2.4 Good Case Study  
Considering the improvement on M-MADM-OM in real scenario, two examples compared between M-MADM-OM and Rule-M are shown in Table 3. The Rule-M may fail in the case that the user always gives irrelevant answer (e.g., system request the number of people and user inform the date of the meeting). On the other hand, the M-MADM-OM can tackle such irrelevant answer and guide the user to inform the rest of slots. This is because the Group-S may generate more user behaviors than Rule-S, and M-MADM-OM can learn more robust policy for real scenario than Rule-M.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Group-S**} & \multicolumn{2}{c}{**Rule-S**} & \multicolumn{2}{c}{**Human Users**} \\  & **SR** & **AT** & **SR** & **AT** & **SR** & **AT** \\ \hline M-MADM-OM & **0.902** & **18.86** & 0.925 & 17.28 & **0.84** & **18.04** \\ Rule-M & 0.582 & 24.94 & **0.945** & **17.04** & 0.76 & 19.56 \\ Yang2017 & 0.577 & 25.03 & 0.860 & 21.56 & 0.68 & 21.08 \\ Zhao2016 & 0.433 & 27.77 & 0.890 & 20.99 & 0.68 & 20.02 \\ Peng2018 & 0.428 & 27.86 & **0.945** & 18.44 & 0.72 & 20.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-model evaluation on Success Rate (SR) and Average Turns (AT).
## 6 Real-World Detection  
The above experiments evaluated GeneDiff's overall performance on clone detection for different ISAs. In this section, we apply GeneDiff in real-world situations to evaluate its detection performance for known IoT vulnerabilities. This evaluation searched for the Heartbleed (CVE-2014-0160) vulnerability in OpenSSL binaries from the following sources:  
(1) Self-compiled OpenSSL 1.0.1f binaries for different ISAs (i.e., AMD64, AArch64 and MIPS64 (OpenSSL 1.0.1f. Available at [[https://github.com/openssl/openssl/releases/tag/OpenSSL_1](https://github.com/openssl/openssl/releases/tag/OpenSSL_1)]([https://github.com/openssl/openssl/releases/tag/OpenSSL_1](https://github.com/openssl/openssl/releases/tag/OpenSSL_1))\(0\)1f));  
(2) The OpenWrt r39853 firmware compiled for MIPS, ARM and AArch64 (OpenWrt. Available at [[https://github.com/openwrt/openwrt/commit/c2](https://github.com/openwrt/openwrt/commit/c2) bbaf439c]([https://github.com/openwrt/openwrt/commit/c2](https://github.com/openwrt/openwrt/commit/c2) bbaf439c));  
(3) The DD-WRT r21888 firmware compiled for MIPS, ARM and AArch64 (DD-WRT. Available at [[https://svn.dd-wrt.com/changeset/21888](https://svn.dd-wrt.com/changeset/21888)]([https://svn.dd-wrt.com/changeset/21888](https://svn.dd-wrt.com/changeset/21888)));  
OpenWrt and DD-WRT are two open source projects for routers, and some industrial IoT routers and smart home routers are modified from them. We used the trained representation model to generate representation vectors for functions in binaries. Then, we treat the vulnerability functions (dtls1_process_heartbeat and tls1_process_heartbeat) as the target functions and utilize the query mode of GeneDiff to detect vulnerabilities in binaries from different ISAs. Because the two vulnerability functions are actually identical, when either of them is matched indicates that the vulnerability exists in the binaries. Table 6 shows the results of the Heartbleed vulnerability detection of IoT firmware by GeneDiff on the real-world dataset. These results indicate that using GeneDiff to detect known vulnerabilities in IoT devices is feasible.  
Figure 10: The impacts of dimensions and number of epochs on detection performance. From top to bottom, the Y-axes are the area under the curve (AUC) of the ROC score, search time and indexing time. (**a**) Dimensions; (**b**) Epoches.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Abstract Rules** & **AUC-ROC** & **Search Time** & **Index Time** \\ \hline Has Abstract Rules & 0.9491 & 0.253 & 0.831 \\ No Abstract Rules & 0.6575 & 0.576 & 1.944 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The impacts of the abstraction rules on the clone detection performance.
## 2 Experiments and Methods
### K-Means Clustering Algorithm  
K-means clustering is a fundamental unsupervised machine learning algorithm that partitions \(n\) observations into \(k\) clusters, where each observation belongs to the cluster with the nearest mean. The algorithm operates by iteratively refining cluster assignments based on the minimization of within-cluster variances [28; 29].  
The algorithm aims to minimize the objective function, commonly known as the Sum of Squared Errors (SSEs) or inertia:  
\[J(X,\mu)=\sum_{i=1}^{n}\sum_{j=1}^{k}w_{ij}\big{\|}x_{i}-\mu_{j}\big{\|}^{2}, \tag{8}\]  
where \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) represents the set of n data points, \(\mu=\{\mu_{1},\mu_{2},\ldots,\mu_{k}\}\) denotes the \(k\) cluster centroids, and \(w_{ij}\) is a binary indicator variable: \(w_{ij}=1\) if \(x_{i}\) belongs to cluster \(j\), and is 0 otherwise. The term \(\mid x_{i}-\mu_{j}\mid\mid\) represents the Euclidean distance between data point \(x_{i}\) and centroid \(\mu_{j}\).  
The cluster assignment for each data point is determined by the following:  
\[c(i)=\text{argmin}_{j}\big{\|}x_{i}-\mu_{j}\big{\|}^{2}, \tag{9}\]  
where \(c(i)\) indicates the cluster assignment for the \(i\)-th data point. This equation assigns each point to the cluster whose centroid is nearest in terms of Euclidean distance.  
After cluster assignments, the centroids are updated using the following:  
\[\mu_{j}=\Bigg{(}\frac{1}{|C_{j}|}\Bigg{)}\sum_{i}C_{j}\in x_{i}, \tag{10}\]where \(\left\lvert C_{j}\right\rvert\) represents the number of points assigned to cluster \(j\), and \(C_{j}\) is the set of all points currently assigned to cluster \(j\). This update computes the mean position of all points assigned to each cluster.  
The convergence criterion is typically defined as follows:  
\[\left\lvert J_{j}(t)-J_{j}(t-1)\right\rvert<\varepsilon, \tag{11}\]  
where \(J(t)\) represents the objective function value at iteration \(t\), and \(\varepsilon\) is a small threshold value determining when the algorithm should terminate.  
The initialization of cluster centroids significantly influences the algorithm's performance. Common initialization methods include the following:  
\[\mu_{j}=x_{i}+\delta, \tag{12}\]  
where \(x_{i}\) is randomly selected from the dataset and \(\delta\) represents a small random perturbation. The within-cluster sum of squares (WCSS) for each cluster is calculated as follows:  
\[WCSS(j)=\sum_{i}\in C_{j}\left\lVert x_{i}-\mu_{j}\right\rVert^{2}, \tag{13}\]  
This metric helps evaluate the compactness of individual clusters and can be used for determining the optimal number of clusters through techniques like the elbow method.  
The silhouette coefficient, which measures how similar a point is to its own cluster compared to other clusters, is computed as follows:  
\[\mathrm{s}(i)=\frac{b(i)-a(i)}{\max\{a(i),b(i)\}}, \tag{14}\]  
where \(a(i)\) is the mean distance between point \(i\) and all other points in its cluster, and \(b(i)\) is the minimum mean distance between point \(i\) and points in any other cluster.