esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 3 Anomaly Detection and Clustering
### Nark-Scapa  
The detection component of the NARX-SCAPA framework uses a NARX neural network to provide a one-step-ahead prediction of the target time series in real time and then deploys SCAPA to identify anomalies in the prediction errors (residual) series. We have implemented this component via NARX from Neural Net Time Series App in Matlab R2021a[32] and SCAPA from R package anomaly in CRAN[31].  
Figure 1 shows the example structure of the NARX neural network used to form a prediction \(\hat{y}_{t+1}\) of the next point \(y_{t+1}\) based on the \(n\) previous points and their corresponding time of day: \(y_{t-n},\ldots,y_{t}\) and \(x_{t-n},\ldots,x_{t}\). The units in the hidden layer are based on the sigmoid function and the output layer uses a linear function of all hidden unit outputs. We use 1 hidden layer with 10 neurons and softmax for the output node, which follows the default setting of conventional neural networks that can balance the complexity and accuracy of the model in most cases. The operations within the hidden layer can be expressed as:  
\[Y_{j}^{h}=\sigma\left(\sum_{i=1}^{L^{x}}x_{t-i+1}w_{ij}^{x}+\sum_{i=1}^{L^{y}} y_{t-i+1}w_{ij}^{y}+b_{j}^{h}\right), \tag{3}\]  
where \(\sigma(x)=(1+e^{-x})^{-1}\), \(j\in\{1,\ldots,J\}\) indexes the neurons, and \(L^{x}\) and \(L^{y}\) are the maximum lags considered in the model. \(w^{y}\) and \(w^{x}\) are the weights for the target time series and the exogenous series, respectively, and \(b^{h}\) represents the constant intercept. The output \(Y_{j}^{h}\)s are passed through the final layer to form a prediction:  
\[\hat{y}_{t+1}=\sum_{j=1}^{J}Y_{j}^{h}w_{j}+b_{j}^{o}. \tag{4}\]  
Once \(y_{t+1}\) is observed, a residual \(r_{t+1}=\hat{y}_{t+1}-y_{t+1}\) is computed and reported as the next real-time observation to SCAPA.  
Weights are estimated from an initial period of (assumed non-anomalous and complete) training data. We replace any missing observations and SCAPA-identified anomalous  
Figure 1: Structure of NARX neural network model.  
data from the (continuously evolving) input set with the prediction of the trained model. Figure 2 summarises the structure of the detection component.  
The following steps summarise the overall detection mechanism:  
1. Initialise a NARX neural network model with the training data;
2. Calculate the estimated value for the current time based on the actual observation values of the previous 10 s;
3. Calculate the current residual value between the estimated value and the actual observation when it is obtained;
4. Pass this residual as the next streaming observation to SCAPA to detect anomalies;
5. Label any points in the observation time series as anomalous if the corresponding residuals are identified by SCAPA;
6. Repeat steps 2 to 5 until the observation stops.
# End-to-End Delay Model for Train Messaging over Public Land Mobile Networks
## 7 Conclusions  
We have presented an original two states model for the generation of the E2E delay of messages transmitted to/from train for control (ERTMS/ETCS) and communications. The model structure has been derived from the behavioral analysis of the E2E delay profiles obtained from experimental data collected during the 3 InSat EU project campaign. The model allows for simply reproducing the  
Figure 14: Comparison of E2E delay CDF generated from the model with the corresponding experimental CDF: Two different measured samples on the same railway section (all available radio access technologies).  
Figure 15: Comparison of E2E delay CDF generated from the model after collecting E2E delays of each generated sample: Expanded time scale up to 2 s, two different experimental samples of the same railway section (all available radio access technologies).  
E2E delay behavior and its first order statistics observed from experimental data. Model parameters have been related to the received power, the serving radio technology and handover events along the line. In principle and in the absence of further data, the statistics of the random variables driving the model could be assumed to be independent of the MNO. This allows for applying the model to other railway scenarios, provided the line has been adequately characterized in terms of the received power, handover points and radio technologies. An example of application of the model to a realistic railway scenario has been presented in this paper. Finally, the model can be easily re-adapted to analyse performance on any local/regional railway line possibly served by the same radio technologies available on the test rail line.  
As a future roadmap, it would be of interest to repeat the measurement campaign to include LTE technology not available during the first campaign. Since the proposed model explicitly accounts for the selected radio technology, it is not difficult to integrate the statistics of the random variables for the LTE case in the proposed model.  
This work has been done in the frame of the 3 InSat project; Radiolabs has been responsible for the Telecom test campaign. The authors wish to thank ESA (Paris, France), RRT (Rome, Italy), Trentitalia (Rome, Italy), Vodafone Italia (Italy), Telespazio (Rome, Italy), Triagnosys (Wessling, Germany) and DLR (Cologne, Germany). These authors contributed equally to this work. The authors declare no conflict of interest.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Two Malicious Receiver Nodes
Let \(i\geq 5\); then, \(i\) belongs to either \(F_{1}\) or \(F_{1}^{\prime}\), either \(F_{2}\) or \(F_{2}^{\prime}\), and either \(F_{3}\) or \(F_{3}^{\prime}\). It can be seen that there are eight possible choices for \(j\geq 5\) as to which of the sets \(F_{1}\), \(F_{1}^{\prime}\), \(F_{2}\), \(F_{2}^{\prime}\), \(F_{3}\), \(F_{3}^{\prime}\) element \(j\) belongs to. Such a choice is represented by a 3-bit number: if the \(\ell\)th bit is 0, then \(F_{\ell}\) is chosen; if the \(\ell\)th bit is one, then \(F_{\ell}^{\prime}\) is chosen. At most, we can choose four 3-bit strings such that no two of them differ by exactly one bit. Consequently, there are \(i,i^{\prime}\geq 5\) such that they belong to the same sets \(F_{j}\), \(F_{j}^{\prime}\) except for a different choice between \(F_{a},F_{a}^{\prime}\). Now, we consider separating \(i\) from \(i^{\prime}\) and \(a\). No \(F_{b}\), \(F_{b}^{\prime}\) for \(b\
eq a\) can be used, as \(i\) and \(i^{\prime}\) belong to the same sets, while \(U\) and \(V^{\prime}\) cannot be used, as \(a\in U,V^{\prime}\). Finally, \(F_{a},F_{a}^{\prime}\) cannot be used, as \(a\in F_{a}\), \(F_{a}^{\prime}\). This results in a contradiction. As all cases for \(r\leq 9\) lead to a contradiction, there is no 2-separating family with less than \(r\) sets and \(r\leq 9\). This concludes the proof of Theorem 3. Although general resistance against two malicious receivers turns out to be harder than against one malicious receiver, (1-)separating families can provide limited protection. For example, the family from (7) ensures that for any pair of malicious receivers, at least one tag \(t_{i}\) cannot be created by the malicious nodes. Thus, a pair of malicious receivers cannot cheat all receivers. Note that this is not true for the separating family from (5) and certain pairs of malicious receivers.
## 5 Computational Implementation and Results
### Daily Operation of the D-STATCOMs  
To verify the dynamic behavior of the D-STATCOMs in the reactive power compensation in electric distribution systems considering the daily active and reactive power demand curves presented in Figure 2, we present the D-STATCOMs' behavior in Figure 4a,b for the IEEE 33- and 69-buses test feeders, respectively.  
From results in Figure 4 it may be noted that: (i) for the IEEE 33-bus test feeder all the D-STATCOMs work in their nominal capacities from the periods 16 to 47, while for the other periods, the amount of reactive power varies as a function of the total active and reactive power demand because, for these periods (see period 1 to period 16 in Figure 2), the total active and reactive power consumption is lower than the 50%. This implies that excessive injections of reactive power can worsen the grid performance in terms of energy losses; (ii) with the daily operation of the D-STATCOMs presented in Figure 4 the annual energy costs also reduced from US $90, 527.63 to US $89, 314.22, i.e., an additional reduction of about USD 1231.41 per year of operation; (iii) in the IEEE 69-bus test feeder the dynamic behavior of the D-STATCOMs shows that during the periods 1 to 17 and in the last, i.e., the 47 th period, the amount of the reactive power injection was lower than the nominal rates of the D-STATCOMs. This was caused by the low demand in these periods of time (see Figure 2); and (iv) if the D-STATCOMs are operated throughout the day at their nominal values (see Table 9) the cost of annual energy losses is about US $94, 618.71. If, however, these are operated with the dynamic curves presented in Figure 4b, then, the cost of annual energy losses is about US $93027.84, i.e., an additional reduction of US $1590.87 per year of operation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sol.**} & \multirow{2}{*}{**Location and Size Node (MVAr)**} & \multirow{2}{*}{\(\mathbf{A_{\text{cost}}}\)**} & \multirow{2}{*}{**Rep.**} \\  & & & \\ \hline
1 & \(\{21(0.0839),\ 61(0.4601),\ 64(0.1139)\}\) & 102,990.80 & 49 \\
2 & \(\{17(0.0862),\ 61(0.4597),\ 64(0.1139)\}\) & 103,022.77 & 1 \\
3 & \(\{21(0.0695),\ 26(0.0143),\ 61(0.5741)\}\) & 103,101.25 & 3 \\
4 & \(\{21(0.0704),\ 27(0.0134),\ 61(0.5741)\}\) & 103,101.31 & 1 \\
5 & \(\{21(0.0687),\ 25(0.0152),\ 61(0.5741)\}\) & 103,101.52 & 1 \\
6 & \(\{22(0.0695),\ 26(0.0143),\ 61(0.5741)\}\) & 103,101.66 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Lists of solutions found by the proposed DCVSA in the IEEE 69-bus.  
It is also important to mention that the dynamic behavior of the reactive power injections in Figure 4 in the IEEE 33- and IEEE 69-bus test feeder confirms the advantages of using D-STATCOMs instead fixed capacitor banks because the former can vary their reactive power injections as function of the demand demeanor, which is not possible when fixed capacitors are installed.
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 2 Sizing Ratio in GCPV Systems
### Simulation Models
#### 2.2.1 Photovoltaic Generator Model  
A high-level energetic model for a generic PVG is presented in [28], and it can be expressed by Equation (3), where  
* \(P_{DC}(t)\) is the PVG output power (W).
* \(\eta_{PVG}\) is the PVG efficiency related to the material used in PV cell construction.
* \(G(t)\) indicates the irradiance incident on the PVG plane (W/m\({}^{2}\)).
* \(\beta\) represents the thermal power coefficient of the PVG material (1/\({}^{\circ}\)C).
* \(T_{PVG}(t)\) is the PVG operating temperature (\({}^{\circ}\)C).
* \(T_{R}\) represents the reference temperature (25 \({}^{\circ}\)C).
* \(S_{PVG}\) is the PVG surface (m\({}^{2}\)).  
\[P_{DC}(t)=\eta_{PVG}\cdot G(t)\cdot[1-\beta\cdot(T_{PVG}(t)-T_{R})]\cdot S_{ PVG} \tag{3}\]  
Figure 2: Block-diagram of the simulation procedure utilized to obtain the \(R_{S\,OPT}\) value.  
The value of the PVG operating temperature (\(T_{PVG}(t)\)) used in the previous model can be estimated using the expression proposed in [29], and shown in Equation (4), where  
* \(T_{A}(t)\) is the ambient temperature (\({}^{\circ}\)C).
* \(G(t)\) shows the irradiance incident on the PVG plane (W/m\({}^{2}\)).
* \(\alpha\) represents the PVG thermal coefficient called the coefficient of Ross (\({}^{\circ}\)C\(\cdot\)m\({}^{2}\)/W).  
\[T_{PVG}(t)=T_{A}+\alpha\cdot G(t) \tag{4}\]  
This thermal model was first introduced by [PERSON] in 1976 [30] and has been very popular due to its simplicity, with the main drawback that wind effects are not considered [31].  
According to [32], the typical values of the Ross coefficient are fixed to \(\alpha=0.025\) for PVG on a flat surface (flat roof) with good ventilation, and \(\alpha=0.050\) for PVG integrated in buildings (habitually on a facade or sloped roof), where the ventilation is usually worse.  
The [PERSON] model has been widely used in applications related to building-integrated photovoltaics (BIPV) and building-aggregate photovoltaics (BAPV) but is also considered a suitable approach in applications related to PV plants [33]. In [34] the value of the Ross coefficients for roof-mounted and ground-mounted PV installations is determined experimentally and, under the same operating conditions, ground-mounted installations have a slightly lower value of this coefficient, being 0.0399 the obtained value.  
The value \(\alpha=0.025\) is used in the simulation and emulations performed in this work, assuming a PVG is installed on a flat roof. This assumption does not represent a loss of generality, since the difference in energy produced by two PV systems will be compared.  
If the readers are interested, a complete review of the models proposed for estimating the operating temperature of solar modules can be found in [35; 36].
## 4 Discussion  
### Study Area  
Naoussa Valey in northern Greece, with its established and stable, very low-density medfly population, provides a convenient setting for both in silico simulations and empirical studies of the initial invasion processes and the possibilities of using SIT for local control of low-density medfly populations. The Valey constitutes the northern part of the medfly range in Greece. Although weather conditions are favorable in summer, cold winters significantly reduce the survival of overwintering individuals, reducing the spring cohort to only a few females per hectare, which keeps the annual population low compared to more climatically suitable sites in central or southern Greece [39]. The situation in which the medfly population regenerates from a few spring individuals resembles the early stage of invasion, where a small invasive propagule initiates the development and establishment of the medfly in a new area. The extremely low density of the seasonal medfly population (below 0.5 female per trap per day during the peak season) creates an opportunity to use SIT as one of the IPM options, or even consider using it as the sole method of medfly control.  
To demonstrate the model's performance and illustrate its sensitivity, we assumed only small variations in the topography of the three hypothetical locations, but maintained the same, near-optimal seasonal fruit assortment, ensuring an abundant and continuous supply of attractive fruit for egg-laying females and larval development. The assumed seasonal fruit composition, with 14-20% very early apricots, 50-76% mid-season peaches and 10-30% late apples, reflects the typical fruit structure in the Naoussa Valley. To mimic the seasonal patterns of medfly population in the Naoussa Valey, all simulations assumed that initial cohorts of only 10 female flies arrived in early April at each of the three hypothetical locations.
## 4 Methods
### Model Architecture  
The core of our classification system is a Siamese network architecture, employing vision transformer (ViT) branches to process the feature vectors extracted by SigLIP 2. This Siamese configuration, consistent with our prior work [2], is designed to learn a similarity metric between pairs of inputs. The key distinction from our previous work is the use of SigLIP 2-derived features, rather than BiomedCLIP features.  
The Siamese network operates as follows:  
1. The network receives two input sequences: * One sequence consists of the \(K\) ultrasound feature vectors (\(\text{Features}_{\text{ultrasound}}^{1}\) to \(\text{Features}_{\text{ultrasound}}^{K}\)), each 512-dimensional, extracted from the ultrasound video chunks. * The other sequence consists of the \(K\) spectrogram feature vectors (\(\text{Features}_{\text{spectrogram}}^{1}\) to \(\text{Features}_{\text{spectrogram}}^{K}\)), also 512-dimensional, extracted from the spectrogram images.
2. Each sequence is fed into a separate, but identical, branch of the Siamese network. These branches are composed of ViT encoders.
3. A crucial aspect of the Siamese architecture is that the two ViT branches share the same weights. This ensures that both ultrasound and spectrogram features are processed using the same learned transformations, projecting them into a common embedding space.
4. Each ViT branch processes its input sequence (either ultrasound or spectrogram features). The ViT consists of six transformer encoder layers followed by a pooling layer. This processes the sequence of \(K\) feature vectors and produces a single, 128-dimensional embedding vector.
5. The Siamese network outputs two 128-dimensional embedding vectors, one representing the ultrasound sequence and one representing the spectrogram sequence.  
The training of this Siamese network is driven by a contrastive loss function. This loss function aims to minimize the distance between the embedding vectors of samples belonging to the same CL/P class (positive pairs) and maximize the distance between embeddings of samples from different CL/P classes (negative pairs). Mathematically, the contrastive loss function is defined as follows:  
\[L(\theta)=\sum_{(x_{1},x_{2},y)}\text{ContrastiveLoss}(f_{\theta}(x_{1}),f_{ \theta}(x_{2}),y) \tag{3}\]  
where:  
* \(x_{1}\) and \(x_{2}\) represent a pair of input sequences (either two ultrasound sequences or two spectrogram sequences).
* \(y\) is a binary label: 1 if \(x_{1}\) and \(x_{2}\) belong to the same CL/P class, and 0 otherwise.
* \(\theta\) represents the trainable parameters of the Siamese network (including the ViT branches).
* \(f_{\theta}(x)\) is the embedding function learned by the network. This function encapsulates the entire process: SigLIP 2 feature extraction followed by ViT processing, resulting in a 128-dimensional embedding vector.
## 6 Conclusions  
### Significance of Using Automatic Textual Analysis  
We first measured the efficiency of the proposed automatic framework in terms of how it accelerates the process of analyzing students' responses to the open-ended questions. Specifically, at the stage of assigning the responses (10,328 sentences) into the 10 categories, our classification model required 2000 sentences to be manually processed, saving 80% of the human effort (roughly two weeks' work); at the subsequent stage of further summarizing the key issues within each category, we used topic modeling to weight the top-10 topics for each category, involving only a trivial amount of human effort compared to reviewing and prioritizing the whole dataset manually. Assuming a similar time for human review, using our automatic analysis saved four weeks' work. Furthermore, future studies can apply the trained classification model directly, obviating the need for initial manual categorization.  
Second, the efficiency of the proposed automatic analysis can be measured by the quality of the identified issues. As we did not have the resources (two weeks for each stage, as estimated above) to go through the entire dataset, we used an indirect measurement tovalidate the identified issues against the existing literature and the authors' local teaching experience (as the responses were from students at the same institute), as discussed in the section 'Discussion on issues identified'. The outcomes were meaningful and were developed into teaching interventions. The survey results show that the students' learning experience improved as a result; it should be noted that, although the average accuracy of classification was relatively low, topic modeling was able to tolerate and produce validated outputs, suggesting that the two-stage analysis framework that was designed is resilient. Topic modeling is designed to identify salient features in a text and is robust to noise introduced by misclassifications.  
Third, automatic analysis becomes more efficient on a large scale. This is because the requirement for training data does not increase linearly. In other words, human analysis requires 10 times the hours to handle 10 times the amount of data; however, in text mining, while more labeled samples lead to better performance, the effect of increasing the extent of the training data becomes less apparent when the samples are large enough. In this study, we manually labeled 20% of data and the net number is 2000 sentences. While it is hard to estimate how many samples are required to achieve an optimal performance in auto-analyzing students' open response, it is not difficult to predict that, to analyze a million student responses, there is no need to manually label 20% of the data (0.2 million). This is because the trained model has already achieved a reasonable outcome with 2000 sentences.
# Efficient Processing of \(k\)-Hop Reachability Queries on Directed Graphs
## 2 Related Work
As a comparison, our approach does not need to list all the paths; we focus on checking the existence of a path between two vertices with a length smaller than a value specified in advance, which is a totally different problem from that of [22]. **Two-hop Distance Label:** In [15], the authors proposed a two-hop distance labeling scheme to answer distance queries; the index construction cost was as high as \(O(|V||E|\log{(|V|^{2}/|E|)})\). To tackle this problem, [PERSON] et al. [16] proposed an ordering strategy to rank vertices so that the index could be constructed efficiently. By this labeling scheme, every vertex, \(v\), has two labels: one is in-label \(L_{in}(v)\), and the other is out-label \(L_{out}(v)\). \(L_{in}(v)(L_{out}(v))\) contains a set of tuples, where each one, \([u,d(u,v)]([u,d(v,u)])\), conveys the information of a target vertex, \(u\), and the distance from \(u\) to \(v\) (\(v\) to \(u\)). Here, \(u\) in each tuple is called a hop vertex. Given the two-hop label, the distance between \(u\) and \(v\) can be computed with Equation (1), where \(C_{in}(v)=\{u|\left[u,d(u,v)\right]\in L_{in}(v))\}\) and where \(C_{out}(v)=\{u|\left[u,d(v,u)\right]\in L_{out}(v))\}\). \[d(u,v)=\min_{w\in C_{out}(u)\cap C_{in}(v)}(d(u,w)+d(w,v)) \tag{1}\]  
Then, for a given kHRQ, \(u\overset{2v}{\rightarrow}v\), we can answer TRUE if \(d(u,v)\leq k\), and, otherwise, we can answer FALSE. The two-hop distance labels can be constructed in the following way [16]: First, all the vertices are sorted by their degrees in descending order. Second, we process one vertex, \(v\), in each iteration to obtain two-hop labels. We perform forward _BFS_ from \(v\) to obtain a set, \(D\), of vertices that \(v\) can reach. Second, we perform backward _BFS_ from \(v\) to obtain a set, \(A\), of vertices that can reach \(v\). For each vertex \(u\in A\), we add \(v\) and \(d(u,v)\) to \(u\prime\)'s out-label, i.e., \(L_{out}(u)=L_{out}(u)\cup\{\left[v,d(u,v)\right]\}\), denoting that \(u\) can reach \(v\) with a distance of \(d(u,v)\). For each vertex \(w\in D\), we add \(v\) to \(w\prime\)'s in-label, i.e., \(L_{in}(w)=L_{in}(w)\cup\{\left[v,d(v,w)\right]\}\), denoting that \(v\) can reach \(w\) with a distance of \(d(v,w)\). In practice, such a two-hop distance label suffers from a bigger index size, longer indexing time, and longer query processing time due to the fact that it takes all the vertices as hop vertices to construct the index.