esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation  
Certain symbols are first defined for the traffic flow-prediction problem formulation, where the traffic flow in the temporal dimension is frequently a continuation of the historical traffic flow states with periodicity.  
The entire city can be represented as a \(U\times V\) grid, with \(N\) regions (\(N=U\times V\)); \(S=\{s_{1},s_{2},\cdots,s_{r}\}\) denotes each region, and \(T=\{t_{1},t_{2},\cdots,t_{n}\}\) represents the entire time span of the historical observed data. The MRWG model is demonstrated in Figure 1, including an encoder and a decoder, which can be used to predict the traffic flow volume. \(Q_{out}=\left\{Q_{i}^{t^{\prime}}\middle|i\in[0,\cdots,N-1],t^{\prime}\in T, \cdots,T+l-1\right\}\) in urban areas \(l\) time stamps into the future based on historical traffic flow dataset \(\{E_{i}^{t}\in R^{T\times u\times v}|i\in[0,\cdots,N-1],t\in[0,\cdots,T-1]\}\). Here, \(E_{i}^{t}\) records the traffic flow volume in each urban area during the historical time period \(t\), and \(Q_{i}^{t}\) represents the predicted traffic flow volume for each urban area at future time stamps \(t^{\prime}\).  
When processing the spatial and temporal correlations in the historical traffic flow data, sudden occurrences of traffic accidents or congestion from one area to another would cause a rapid spread of local traffic states to the surrounding areas after they have been maintained for some time [36]. Furthermore, different types of abnormal accidents have varied effects on the traffic conditions in the surrounding regions.  
Given the historical traffic flow data of the target region and the abnormal events \(Y\), the surrounding region \(B\) subgraphs are formed based on the total abnormal event types. The historical traffic flow data are divided into \(B\) groups (the same as regions), denoted as \(\kappa=\{\kappa_{1},\kappa_{2},\cdots,\kappa_{B}\}\). Considering the historical traffic flow and transient data \(E_{in}\in R^{T\times l\times V}\) and \(Q_{in}\in R^{T\times l\times V}\), as well as the accident data \(\kappa\), the traffic flow prediction can be formalized as a learning function \(f_{\theta}\) that maps the input to the output prediction of the traffic flow \(\widehat{Y}\) at the next timestep:  
\[\widehat{Y}=f_{\theta}(E_{in},Q_{in},\kappa) \tag{1}\]  
where \(\widehat{Y}\in R^{l\times l\times V}\) and \(\theta\) represent the predicted traffic flow and learnable parameters, respectively.
## 2 Background
### Symbolic Temporal Regression  
Classification and regression trees (CART) is a term introduced in [14] to refer to decision tree algorithms that can be used for both classification and regression. A regression tree is a symbolic construct that resembles a decision tree (usually employed for classification), based on the concept of data splitting and on the following language of propositional letters (decisions):  
\[\mathcal{S}=\{A\bowtie a\mid A\text{ is an attribute and }a\in dom(A)\} \tag{7}\]  
where \(\bowtie\in\{\leq,=\}\) and \(dom(A)\) is the domain of the attribute \(A\). A regression tree \(\tau\) is obtained by the following grammar:  
\[\tau::=(S\wedge\tau)\vee(\
eg S\wedge\tau)\mid\hat{b} \tag{8}\]  
where \(S\in\mathcal{S}\) and \(\hat{b}\in Dom(B)\) (however, \(b\) is not necessarily in \(dom(B)\)). Solving a regression problem with a regression tree entails finding a tree that induces a function \(F\) defined by cases:  
\[F=\left\{\begin{array}{ll}\hat{b}_{1}&\text{ if Condition }1\\ \hat{b}_{2}&\text{ if Condition }2\\ \ldots&\ldots&\\ \hat{b}_{t}&\text{ if condition }t\end{array}\right. \tag{9}\]  
The conditions are propositional logical formulas written in the language of \(\mathcal{S}\), and intuitively, such a function can be read as if the value of these attributes is \(\ldots\), then the value of the dependent variable is, on average, this one, \(\ldots\), and so on. In other words, \(F\) is a staircase function. The main distinguishing characteristics of a staircase function obtained by a (classic) regression tree is that the conditions are not independent of each other, but they have parts in common, as they are extracted from a tree. Therefore, for example, one may have a first condition of the type if \(A_{1}\leq 5\) and \(A_{2}\leq 3\), then \(B=1\), and a second condition of the type if \(A_{1}\leq 5\) and \(A_{2}>3\), then \(B=3\). If functional regression is mainly based on the least squares method, the gold standard regression method with trees is splitting by variance, which consists of successively splitting the data set searching for smaller ones with lower variance in the observed values of the dependent variable; once the variance in a data set associated with a node is small enough, that node is converted into a leaf, and the value of the dependent variable is approximated with the average value of the data set associated with it. Such an average value labels the leaf. Regression trees are not as common as decision trees in the literature; they are usually employed in ensemble methods such as random forest. However, popular learning suites do have simple implementations of regression trees. In the suite WEKA, the to-go implementation in this case is called RepTree. Despite its name, such an implementation is a variant of the more popular J48, which is, in fact, its counterpart for classification. Regression trees can be used on both atemporal and temporal data, by using, as in the functional case, lagged variables.
## 4 Agro-Food Substrates for Anaerobic Digestion
### Livestock Manure
#### 4.1.2 Horse Manure  
The amount of organic matter (VS) in horse manure is 30-47% (Table 4). As for cow manure, horse dung has a high proportion of slowly degradable lignocellulosic organic material, related to the diet, bedding material used, and the frequency of stall cleaning. The studies on horse manure as a biogas substrate focus on solid state anaerobic digestion (SS-AD) due to the high total solids and fibrous content, which makes this manure unsuitable for continuous slurry-based biogas reactors [71]. SS-AD is suitable for lignocellulosic material but shows drawbacks concerning low VFAs formation, ammonia accumulation and difficult mixing [72]. Straw is the most widely used bedding material for horse stalls, although, other materials such as sawdust, wood pellets and flax straw are also achieving interest [73]. Methane production from horse manure is very variable and is mainly affected by the feeding intensity and by the type of the forages [45]. There are contrasting results about the effect of using different bedding material on methane yield of horse [59; 73; 74]. The highest specific methane yields for horse manure was achieved with straw pellets as bedding material. The storage of the manure resulted in significantly lower methane yields [59]. However, due to the high total solid content, horse manure may be used for mixed digestion with low solid content substrates [75]. In addition, the high C/N ratio of horse manure makes it a good co-substrate for the AD of nitrogen rich substrates such as pig and poultry manures [59].
# Evaluating Methods for the Identification of Off-Chain Transactions in the Lightning Network
## 5 Results
### Lightning Network Size  
While parsing the blockchain we have counted all unspent P2 WSH outputs for each block height, in both the mainnet and the testnet. The results of this can be found in Figure 12, as a graph showing unspent P2 WSH outputs for different block heights. We can see a distinct difference between the two networks: the mainnet has a more organic growth as a result of real-world use, while the testnet graph is characterized by the testing which the network is used for. The sharp decline in unspent outputs on the mainnet at around block 505,000 can be explained by the sharp decline in fees for doing transactions at that time [25], which incentivized users to consolidate their unspent outputs into fewer bigger ones.  
While the unspent output count provides a concrete upper limit on the number of channels and therefore the size of the LN, we also wanted to know to which degree the amount of unspent P2 WSH transactions was correlated with the size of the LN. It is clear that changes in the size of the LN will impact the number of P2 WSH outputs/inputs, but there might be many other transactions not related to the LN having a larger impact. From the data we collected from the LN itself we constructed a total channel count for each block height. Similarly, we isolated the same block interval with the P2 WSH output count from the blockchain. These two variables allowed us to compare the changes in the number of channels in the LN and number of unspent P2 WSH outputs. We used the Kendall rank correlation coefficient on this data for the mainnet, which gave us a correlation coefficient of 0.73 indicating a strong positive correlation. The scatter plot for this can be seen in Figure 13. We did the same for the interval of testnet data we collected, which resulted in a coefficient of 0.45 which is only a moderate correlation. The scatter plot for the testnet can be seen in Figure 14. Both tests have the same \(p\)-value: \(p<2.2\times 10^{-16}\). While the two are correlated naturally due to the need for channel outputs to be P2 WSH, the results here indicate there is indeed a strong statistical correlation. The testnet showing a weaker correlation can be due to the unnatural use taking place on the testnet, as was apparent from Figure 12 graph showing unspent output for different block heights. Additionally, the correlation was limited to the amount of data from our collection intervals, meaning we could only correlate the two for around 1000 blocks and less.  
Figure 11: Venn diagram of channel sets during interval two.  
Figure 12: Maximum size of the LN based on unspent P2 WSH outputs on the blockchain.  
Figure 13: Correlation between unspent P2 WSH outputs and channels open in the LN mainnet.
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 2 Related Work
### TLS-Secured MQTT and IoT  
[PERSON] et al. (2018) benchmark TLS-secured MQTT using virtualized nodes and report comparative costs across cipher suites. However, their results apply primarily to simulated environments. Our work builds upon this by experimentally deploying TLS 1.3-secured MQTT on real devices, capturing power and performance metrics in a physical setting reflective of real-world embedded deployment.  
In addressing both application-layer communication and security under resource constraints, our work fills a critical area in the existing literature. It builds upon prior studies on lightweight cryptography and MQTT-based V2X systems while also integrating an empirical evaluation of TLS overhead on real IoT devices. By combining standard-compliant security mechanisms with practical deployment scenarios, our study responds directly to the call for updated, rigorous, and grounded assessments of secure communication in constrained V2X environments.
# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 4 Results
### Performance Analysis
#### 4.2.1 Peak Signal-to-Noise Ratio  
The Peak Signal-to-Noise Ratio is a metric used to quantify the quality of a signal, such as an image or sound, by measuring the ratio of the maximum possible power of the signal to the power of its noise. It is commonly used to evaluate the quality of reconstructed or processed signals compared to their original unprocessed versions. PSNR is expressed in decibels (dB), and higher PSNR values typically indicate higher quality or fidelity in the signal.  
\[PSNR=20\log_{10}\left(\frac{MAX_{f}}{\sqrt{MSE}}\right)\]  
where:  
* \(MAX_{f}\) is the maximum possible pixel value in the image (for an 8-bit image, it is typically 255).
* \(MSE\) represents the Mean Squared Error between the original and processed/reconstructed images.  
The PSNR values measure the quality of the images produced by these modalities, where higher PSNR values generally indicate better image quality in terms of signal fidelity and lower noise.  
According to Figure 5, the X-ray modality has the highest PSNR value of 73 dB. This indicates that X-ray images have the highest quality among the listed modalities in terms of the signal-to-noise ratio. The higher PSNR value suggests that the X-ray images have less noise and better fidelity in preserving the signal details than the other modalities listed.  
Figure 4: The generated encrypted image after applying the RSA.  
Conversely, the CT modality has the lowest PSNR value of 62 dB. This suggests that CT images have relatively lower quality in terms of signal fidelity and may have more noise or less preserved signal detail compared to the other modalities mentioned in the list.
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Dendrimers  
Dendrimers, regularly branched spherical molecules, are generally used for encapsulation of functional molecules, or for their surface functionalities (active sites on the external dendrons). Dendrimers are classified by generation, i.e., the number of repeated branching cycles that are performed for their synthesis (3 repeated branching results in a third generation dendrimer, G3). For immunosensors, they mainly bring high densities of coupling groups, therefore high surface densities of probes of electroactive molecules. They participate to the research of the highest surface-to-volume ratio, which is the key factor of highly sensitive sensors.
# Iteratively Refined Multi-Channel Speech Separation
## 2 Proposed Method
### Loss Function  
The loss function is used to calculate the signal-to-distortion ratio (SDR) between the separated signal and the original signal in the first stage and at each subsequent iteration, and these SDRs are then added to calculate the total loss. The joint loss function can be expressed as follows:  
\[Loss=-SDR(\hat{y}_{q,c}^{(0)},y_{q,c})-SDR(\hat{y}_{q,c}^{(1)},y_{q,c})-SDR( \hat{y}_{q,c}^{(2)},y_{q,c}) \tag{10}\]  
where  
\[SDR(y,s)=10 log_{10}(\frac{\left\|s\right\|^{2}}{\left\|s-y\right\|^{2}}), \tag{11}\]  
denotes the \(SDR\) calculation operator, \(s\) and \(y\) denote the reference and the separated speech signals, respectively, \(y_{q,c}\) denotes the original clean signal, \(\hat{y}_{q,c}^{(0)}\) denotes the pre-separated signal, \(\hat{y}_{q,c}^{(1)}\) denotes the initially separated signal, and \(\hat{y}_{q,c}^{(2)}\) denotes the post-separation output signal after the first iteration.  
Since each iteration can improve the previous iteration result, the same loss function can be reapplied at each iteration. Here, a group of pre-determined loss functions is used to reduce complexity during the training process, making the model easier to train. Therefore, in this paper, only a three-stage loss function is used.
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 3 Results
### Performance Evaluation of URA-VLMs  
Through the training and optimization of the URA-VLMs, we achieved significant improvements based on the optimization of the inference process and prompt engineering. Next, we further evaluated the model's performance from the following three perspectives: road anomaly classification, road waterlogging estimation, and road safety levels.
## Materials and Methods
### Determination of TPC  
The phenolics substances in the extract were determined by applying the Folin-Ciocalteu phenol assay [11] with a slight modification [12], in which gallic acid played a role of a standard. The absorbance was measured at 765 nm by a spectrophotometer (UV160, Shimadzu, Kyoto, Japan). The TPC was presented as milligrams gallic acid equivalents per gram sample (mg GAE/g). The linearity range of the gallic acid standard curve was 10 to 500 \(\mu\)g/mL (R\({}_{2}\) = 0.9998).
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
###### Abstract  
This paper investigates the feasibility of redirecting the field of view (FOV) of a light-based time-of-flight (ToF) ranging device, commonly known as a pulsed lidar, using fixed mirrors and prisms for possible future use in robotics. The emphasis is on configurations where the FOV redirection element is positioned beyond the ranging device's dead zone. A custom made direct ToF ranging device with time-over-threshold (TOT)-based walk error compensation was used to evaluate the effects of the FOV redirecting optics on range measurement accuracy and precision. The tests include redirecting the FOV with a clean prism with anti-reflective (AR) coating on its legs, as well as with a regular and a first surface mirror in both a clean and dusted state. The study finds the prism to be unsuitable due to parasitic reflections, which ruin the ranging data. The clean mirrors were found to have no noticeable effect on ranging accuracy. When they are dusty, mirrors introduce a negative measurement error. This effect is the most pronounced when a mirror is positioned toward the end of the partial dead zone of the ToF rangefinder, but loses influence as the mirror is moved farther away. The error is attributed to the parasitic reflection off dust on the mirror, which reduces the time of detection of the pulse reflected off the real target, and interferes with the walk error compensation by widening the detected pulse.  
time-of-flight; lidar; field of view redirection; passive redirection; clean mirror; dusted mirror; prism +
Footnote †: journal: Applied Sciences
Following the scale model test, three unique structures--the \"main circular freezing-tube\", the \"strengthening profiled freezing-tube\", and the \"hot brine limiting-tube\"--were created for the first time. Additionally, the impact of freezing the water between the pipes was proven [20]. Based on the results of large-scale model testing, [PERSON] et al. [21] concentrated on analyzing the strengthening freezing impact of profiled freezing tubes and proposed opening them in advance during construction. [PERSON] et al. [22] studied the changes in the frozen soil curtain throughout the freezing, tunnel excavation, and thawing phases using a thermal coupling analysis approach. They emphasized that the temperature field is an important aspect in forecasting the thickness of the frozen soil curtain. [PERSON] et al. [23] investigated the bearing capacity of \"steel pipe-frozen soil\" composite constructions at four different temperatures and presented limit-state criteria for water sealing. [PERSON] et al. [24] analyzed the measured data of surface displacement, tunnel arch displacement, and horizontal convergence during the construction of the Gongbei Tunnel and proposed that when using the step method to excavate the upper part of a large cross-section tunnel, attention should be paid to the arch displacement caused by excavation construction and the frequency of on-site real-time monitoring be increased. The author's team also conducted theoretical and experimental studies on the freezing temperature field of the FSPR method [25; 26; 27], the improved design of special-shaped freezing pipes, and the mechanical properties of jacked pipe-frozen soil composite structural mechanics, and made corresponding guiding suggestions for the specific construction process. According to the research findings, the technological obstacles encountered by the FSPR approach in its use are mostly exhibited in three areas: First and foremost, it has a completely novel freezing idea. Second, the influence of frost heave on the strata, surface, and surrounding buildings (structures) must be considered. Third, there are few examples of this approach in use both at home and abroad, and there are still certain practical issues that need to be addressed and improved in building. As a result, extensive research on its freezing temperature field and frost heave displacement field is required. After analyzing the engineering geological conditions and freezing design scheme of the Gongbei Tunnel's underground section, a thermodynamic coupling two-dimensional finite element calculation model based on the mathematical model theory of the stratum freezing temperature field and frost heave displacement field of the horizontal freezing tunnel was built in this article. The construction law of frozen soil curtain and the modification of frost-heave displacement during the freezing period were researched using the calculation platform of finite element software COMSOL Multiphysics 5.6 and integrated with the project's field-observed data.
## 2 Mobile Fingerprinting Based on Semisupervised Learning  
Wi-Fi fingerprint localization estimates a location by matching the currently received Wi-Fi RSSI measurements to those in a training database. For creating this database, the conventional fingerprinting method requires a trainer to manually labels all the Wi-Fi RSSI measurements at every point of the grid. Instead of the time-consuming conventional method, this section introduces a new mobile fingerprinting data collection algorithm that allows the trainer to continue walking without the stationary calibration. In the training phase for data collection, it is common sense that data collectors recognize which floor they are located on. Thus, the proposed mobile fingerprint method aims to obtain 2D position of the unlabeled data.  
Suppose \(y=[y_{1},\ldots,y_{d}]^{\mathrm{T}}\in R^{d}\) is the set of the Wi-Fi RSSI measurements received from \(d\) Wi-Fi APs. In 2-D space, the user's location is defined as \((x_{\mathrm{X}},x_{\mathrm{Y}})\). The \(l\) number of the labeled training data points are defined as the set \(\{(y_{i},[x_{\mathrm{Xi}},x_{\mathrm{Yi}}]^{\mathrm{T}})\}_{i=1}^{l}\) with \(y_{i}\in X\subseteq\mathcal{R}^{d}\). The \(u\) number of unlabeled data set \(\{y_{i}\}_{i=l+1}^{l+u}\) comprises only the RSSI measurements.  
It is desired to find the separate mappings \(f_{\mathrm{X}}:X\rightarrow\mathcal{R}\) and \(f_{\mathrm{Y}}:X\rightarrow\mathcal{R}\), which denote the relationships between Wi-Fi signal strength and location of the smartphone, using the labeled training data \(\{(y_{i},x_{\mathrm{Xi}})\}_{i=1}^{l}\) and \(\{(y_{i},x_{\mathrm{Xi}})\}_{i=1}^{l}\), and the unlabeled data \(\{y_{i}\}_{i=l+1}^{l+u}\). Because the models \(f_{\mathrm{X}}\) and \(f_{\mathrm{Y}}\) are learned independently, we omit the subscripts of \(f_{\mathrm{X}}\), \(f_{\mathrm{Y}}\), and of \(x_{\mathrm{X}}\), \(x_{\mathrm{Y}}\), for simplification.  
In the SVM-based semisupervised learning framework, the optimization formulation is as follows,  
\[f^{*}=\arg\min_{f\in\mathcal{H}_{k}}c\sum_{i}V(y_{i},x_{i},f)+\gamma_{A}\|f\|_{ A}^{2}+\gamma_{I}\|f\|_{I}^{2}, \tag{1}\]  
where \(V\) is a loss function; \(\|f\|_{A}^{2}\) is the norm of the function in the Reproducing Kernel Hilbert Space \(\mathcal{H}_{k}\); \(\|f\|_{I}^{2}\) is the norm of the function in the low dimensional manifold; and \(c\), \(\gamma_{A}\), and \(\gamma_{I}\) are the regularization weight parameters. To represent the manifold \(\|f\|_{I}^{2}\), the method uses graph Laplacian, the so-called graph-based semisupervised learning, and it is also called a semisupervised support vector machine when we use an \(\epsilon\)-insensitive loss function. Then, the solution is achieved by iterative quadratic programming. More details about the semisupervised algorithm can be found in [37].