esa-sceva/satcom-chunk-collection huggingface dataset:
# THz Rectennas and Their Design Rules
## 4 Theory of Operation  
As Figure 5 suggests, the categorization of different theoretical frameworks is not only according to the charge carrier transition mechanism, but also the suggested models under each category can have different application ranges. In all cases, they can be divided into three main categories; that is, tunneling, geometric and a hybrid mode, which shares some features of the previous two.
# Relationships Among Origin, Genotype, and Oenological Traits of _Brettanomyces_ Yeasts
## 2 Results  
### Biotyping of Brettanomyces spp.  
The 45 strains of _Brettanomyces_ yeasts under evaluation belonging to the DiSVA Collection were submitted to the typing process using the combination of five different primers. The results showed four main clusters, as reported in the constellation plot using Ward clustering (Figure 1).  
Figure 1: Ward clustering and constellation plot of the _Brettanomyces bruxellensis_ biotypes using M13, M14, OP C20, and OPK03 RAPD primers and the PIR1 and PIR3 minisatellite primers. The representation of the clusters is \(\square\) cluster 1; \(\square\) cluster 2; \(\square\) cluster 3; and \(\square\) cluster 4.  
The first main abundant cluster (cluster 1, red dot) was constituted by the biotypes from grape and winery environments from the winemaking area denominated Tuscany 1, apart from the biotype wF9a that was enclosed in cluster 3 (green dot) together with the biotypes from Cameroon (cocoa seeds) and belonging to B. anomalous species. Cluster 2 (orange dot) was composed of only two biotypes: biotype C2.3 _B. bruxellensis_ from Cameroon (corrosol) and biotype M6 (winery Marche region). The broad cluster 4 (blue dot) included two well-distinct sub-clusters: (i) biotypes from the winemaking area Tuscany 2; (ii) biotypes from the winemaking area Marche region and Lombardy region, as well as beer biotypes. Apart from some exceptions, the biotypes were well distinguished in clusters and sub-clusters on the basis of the environmental habitats of isolation.
# Crowd Evacuation Guidance Based on Combined Action Reinforcement Learning
## 2 Methods
### Priority Experience Playback of CA-DQN  
DQN randomly samples from the experience pool, regardless of the sample differences, and has low sample utilization efficiency. Using the preferred experience playback method [23], with the sample TD error defined by Formula (4), the sampling priority is  
\[p_{t}=(|\delta_{t}|+\varepsilon)^{\alpha} \tag{10}\]  
where \(\varepsilon\) and \(\alpha\) are constants. A sample with a more significant absolute value of the TD error implies that it contains more useful information and should be given a higher sampling priority, which can improve sample utilization and training efficiency.  
The TD error of a sample in CA-DQN is defined by Formula (8), which is a vector  
\[\delta_{t}=(\delta_{t}^{(1)},\ldots,\delta_{t}^{(d)}) \tag{11}\]  
When the preferred experience playback method is applied, the sample priority can be defined as the maximum absolute TD error of each dimension (called the CA-DQN-max method).  
\[p_{t}=\left(\max_{d}\left|\delta_{t}^{(d)}\right|+\varepsilon\right)^{\alpha} \tag{12}\]  
The maximum value can highlight samples with a greater value for training in a particular action dimension. However, the dimension with the max TD error may cover up other dimensions, thereby decreasing the training stability and efficiency.  
The better way is to define sample priority as the mean absolute value of the TD error of each dimension (called the CA-DQN-mean method).  
\[p_{t}=\left(\frac{1}{D}\sum_{d}\left|\delta_{t}^{(d)}\right|+\varepsilon\right) ^{\alpha} \tag{13}\]  
The mean value collects information on the sample importance from all dimensions, and calculates a more accurate priority under the influence of random interference. Consequently, it helps maintain the training process's stability and obtain a more effective training result.
## 3 Problem Formulation  
As discussed earlier at the end of the introduction section, the drawback of resource allocation for SR maximization in a downlink NOMA cluster is that all the lower CG users could not achieve the upper bound of IDRs and the data-rate of the highest CG gain user increases very rapidly. The drawback of other fairness index-based resource allocation techniques for data rate equalization is that the IDR of the highest CG user can not be increased beyond the lowest IDR in a NOMA downlink cluster. These two limitations can be overcome by the RIS-assisted NOMA downlink system. Hence, we see a problem statement about power and meta atom resource allocation in a downlink RIS-NOMA cluster such that all the lower CG users are achieving their upper bound of IDRs and the remaining resources are allocated to the highest CG user to enhance its IDR. The power coefficients and meta-atoms are the resources that need to be cleverly distributed among the users of the downlink RIS-NOMA cluster to achieve this goal.  
The problem is defined below under the constraints C1 to C3.  
\[max\left\{\sum_{i=1}^{n}R_{il}\right\}\ \forall\ i\ \in[1,n]\,l\ \in[1,m] \tag{9}\]  
\[\{\phi_{il},a_{il}\}\]  
Subjected to:  
\[C1:\Theta_{rl}\in[0,360)\text{ in degrees}\] \[C2:\sum_{i=1}^{n}a_{il}=1\ \forall\ l\in[1,m]\] \[C3:0<a_{il}<1\ \forall\ i\in[1,n]\text{ and }\forall\ l\in[1,m]\] \[C4:0<N_{il_{min}}<N\ \forall\ i\ \in[1,n]\,l\ \in[1,m]\]  
_Constraints Involved in Reflectivity Matrix (Phase Shifts at All the Meta-Atoms) and Power Allocation:_  
Constraint 1 (C1): Ideally, phase shifts may take any value in the range \([0,360)\) degrees. Constraint 2 (C2): The sum of all power coefficients in a cluster is equal to 1.  
\[\sum_{i=1}^{n}a_{il}=1\ \forall\ i\ \in[1,n]\,l\ \in[1,m]\]Constraint 3 (C3): All power coefficients are positive fractions, i.e.,  
\[0<a_{il}<1\;\;\forall\;i\;\in[1,n]\;,l\;\in[1,m]\]  
Constraint 4 (C4): Sufficient meta-atoms should be available in each RIS to achieve the condition of channel saturation., i.e.,  
\[0<N_{il_{min}}<N\;\;\forall\;i\;\in[1,n]\;,l\;\in[1,m]\]  
Here, \(N_{il_{min}}\) are the minimum number of meta-atoms required to bring the equivalent channel gain in the saturation condition with respect to IDR of the user.
## 4 Experiments
### Experimental Results
#### 4.5.2 Results on Chinese and Czech  
Table 6 shows the comparison of GNNSDP and previous studies on the SemEval 2015 Task 18 Chinese and Czech datasets. From the results, we have observed that:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Dm**} & \multicolumn{4}{c}{**PAS**} & \multicolumn{4}{c}{**PSD**} \\  & **ID** & **OOD** & **ID** & **OOD** & **ID** & **OOD** \\ \hline Lindemann et al. [44]: +BERT\({}_{\text{top}}\) & 94.1 & 90.5 & 94.7 & 92.8 & 82.1 & 81.6 & 90.3 & 88.3 \\ Lindemann et al. [24]: +BERT\({}_{\text{top}}\) & 93.9 & 90.4 & 94.7 & 92.7 & 81.9 & 81.6 & 90.2 & 88.2 \\ SemPointer [14]: +Char+Lemma+BERT\({}_{\text{base}}\) & 94.4 & 91.0 & 95.1 & 93.4 & 82.6 & 82.0 & 90.7 & 88.8 \\ He et al. [28]: +Char+Lemma+BERT\({}_{\text{base}}\)-filter & 94.6 & 90.8 & **96.1** & **94.4** & 86.8 & 79.5 & 92.5 & 88.2 \\ \hline GNNSDP(GCN): +Char+Lemma+BERT\({}_{\text{base}}\) & **95.1** & **91.1** & 95.7 & 93.2 & **87.7** & **87.3** & **92.8** & **90.5** \\ GNNSDP(GAT): +Char+Lemma+BERT\({}_{\text{base}}\) & **95.3** & **91.9** & 96.0 & 94.3 & **87.0** & **86.7** & **92.8** & **91.0** \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DM**} & \multicolumn{2}{c}{**PAS**} & \multicolumn{2}{c}{**PSD**} \\  & **ID** & **OOD** & **ID** & **OOD** & **ID** & **OOD** \\ \hline \# labels & 59 & 47 & 42 & 41 & 91 & 74 \\ \# frames & 297 & 172 & - & - & 5426 & 1208 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Contrastive statistics of three semantic formalisms.  
GNNSDP(GCN) and GNNSDP(GAT) outperform the previous parsers on Chinese and Czech.
* The LF1 scores of GNNSDP(GCN) and GNNSDP(GAT) are relatively close on Chinese and Czech.  
In summary, GNNSDP outperforms the previous state-of-the-art parser in three languages and three semantic dependency formalisms. Outstanding performances of GNNSDP have demonstrated that higher-order information can bring considerable accuracy gains in SDP. In addition, GNNs are capable of capturing higher-order information.
## 3 Dataset Descriptions
### Edge-ItoT  
The cybersecurity dataset for Internet of things (IoT) and industrial Internet of things (IIoT) applications is used in intrusion-detection systems based on machine learning. IoT data are collected from more than 10 different types of devices, such as low-cost digital sensors for sensing temperature and humidity, pH sensor meters, ultrasonic sensors, heartrate sensors, water-level detection sensors, soil moisture sensors, flame sensors, etc. In this database, 14 different types of attacks involving IoT and IIoT protocols are analyzed and classified into five threats, including DoS and DDoS attacks, information gathering, injection attacks, an-in-the-middle attacks, and malware attacks. Out of 1176 characteristics, 61 are highly correlated. The 20,952,648 usual attack statistics in Edge-IIoT [26] include 11,223,940 normal and 9,728,708 attacks. [27]. We split this dataset into 20% for tests and 80% for training, with a stratification option to keep the percentages static for all classes. A total of 1,909,671 samples were taken from the dataset: 1,527,736 for the training set and 381,935 for the test set; they were distributed into 15 categories, as shown in Table 4.
# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
## 5 Experiments
### Benchmark Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **MOTA** & **HOTA** & **IDF1** & **FP** & **FN** & **IDS** \\ \hline FairMOT [11] & 61.8 & 54.6 & 67.3 & 103,440 & 88,901 & 5243 \\ \hline CSTrack [62] & 66.6 & 54.0 & 68.6 & 25,404 & 144,358 & 3196 \\ \hline GSDT [43] & 67.1 & 53.1 & 67.5 & 31,913 & 135,409 & 3133 \\ \hline MPNTrack [6] & 57.6 & 46.8 & 59.1 & 16,953 & 201,384 & 1210 \\ \hline Trackformer [49] & 68.6 & 54.7 & 65.7 & 20,348 & 140,373 & 1532 \\ \hline CorrTracker [64] & 65.2 & 57.2 & 69.1 & 79,429 & 95,855 & 5183 \\ \hline RelationTrack [65] & 67.2 & 56.5 & 70.5 & 61,134 & 104,597 & 4243 \\ \hline GNNMatch [66] & 54.5 & 40.2 & 49.0 & 9522 & 223,611 & 2038 \\ \hline JDTHM & 69.3 & 56.7 & 70.9 & 29,716 & 127,135 & 2288 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with modern methods on MOT20 benchmark with the provided public detections, showing the quantitative results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **MOTA** & **HOTA** & **IDF1** & **FP** & **FN** & **IDS** \\ \hline GMTracker [3] & 59.0 & 51.1 & 65.9 & 20,395 & 209,553 & 1105 \\ \hline MPNTrack [6] & 58.8 & 49.0 & 61.7 & 17,413 & 213,594 & 1185 \\ \hline FairMOT [11] & 73.7 & 59.3 & 72.3 & 27,507 & 117,477 & 3303 \\ \hline LPC [27] & 59.0 & 51.7 & 66.8 & 23,102 & 206,947 & 1122 \\ \hline CTTrack [44] & 61.5 & 48.2 & 59.6 & 14,076 & 200,672 & 2583 \\ \hline TransCenter [45] & 71.9 & 54.1 & 62.3 & 17,378 & 137,008 & 4046 \\ \hline Trackformer [49] & 74.1 & 57.3 & 68.0 & 34,602 & 108,777 & 2829 \\ \hline Lif\_T [59] & 60.5 & 51.3 & 65.6 & 14,966 & 206,619 & 1189 \\ \hline CSTrack [62] & 74.9 & 59.3 & 72.3 & 23,847 & 114,303 & 3567 \\ \hline PermaTrack [63] & 73.1 & 54.2 & 67.2 & 24,577 & 123,508 & 3571 \\ \hline JDTHM & 73.9 & 59.6 & 71.7 & 25,639 & 117,756 & 3733 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with different methods on MOT17 benchmark with the provided public detections, showing the quantitative results. Figure 6 displays the qualitative analysis results of our hypergraph architecture on the MOT20. Figure 5: Visualization of our results on four sequences of the MOT17. Each row shows the results of sampled frames in chronological order of a video sequence. Bboxes and identities are marked in the images. Bboxes with different colors represent different identities. Best viewed in color.
# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
## 3 Algorithms  
### Baseline Algorithm  
We present the baseline algorithm, a privacy-preserving variant of CWC. In this subsection, we consider a two-party protocol, in which a party B has its private data and outsources CWC computation to another party A, where this protocol can be extended to more than two data owners using the multi-key homomorphic encryption [22]. During the computation, party A should not gain other information than the number \(n\) of positive data, the number \(m\) of negative data, and the number \(k\) of features. It should be noted that party B can hide the actual number of data by inserting dummy data and telling A the inflated numbers \(n\) and \(m\). Dummy data can be distinguished by adding an extra bit that indicates that the datum is a dummy if the bit is 1. The values of features and dummy bits of data in each class are encrypted by B's public key and sent to A.  
The baseline algorithm consists of three tasks: computing encrypted bit string \(E[\textbf{B}_{i}]\), sorting \(E[\textbf{B}_{i}]\)s, and executing feature selection on \(E[\textbf{B}_{i}]\)s. In the baseline algorithm, all inputs are encrypted and they are not decrypted until the computation is completed. Thus, for simplicity, we omit the notation \(E\) in the following presentation.
## 7 Adaptive Single-Point and Multi-Point Equalization  
The room is generally a time-varying environment (a _\"weakly non-stationary\"_ system as defined in [2]) that changes as a function of several parameters, such as the position of physical objects in the room, the opening of doors, as well as the movement of people and other obstacles in the enclosure [6; 175]. Additionally, temperature variations can lead to large variations in the RIR, as reported by [176]. Furthermore, variations of the source and receiver positions, and of loudspeaker and microphone characteristics may occur as reported in [6]. Thus, adaptive solutions suitable to track and correct slow variations in the room response should be adopted. Different adaptive RRE techniques have been proposed in the literature. The approaches are here classified considering the number of input and output channels as SISO/SIMO, and MISO/MIMO, where input refers to the number of loudspeakers and output to the number of microphones, since these classes share similar problems in the identification procedure.
# The Rebound Effect of the COVID-19 Pandemic on Clinically Significant Prostate Cancer Diagnosis
## 1 Introduction  
During the COVID-19 pandemic, a radical change in medical priorities was enforced across all health systems, particularly in Italy where the pandemic hit the hardest. Healthcare professionals have indeed faced the challenging task of prioritizing the treatment of non-deferrable conditions over the deferrable ones [1, 2, 3]. Policy has focused all human and economic resources on the management of the pandemic rather than on the diagnosis and management of cancer patients. People also neglected chronic diseases for fear of possible COVID-19 infections in hospitals.  
While the effects of postponing the treatment of deferrable conditions are yet to be defined, previous studies have shown a decrease in the cancer diagnoses at different sites during the COVID-19 pandemic [4, 5]. Specifically, during the period 1 March-31 December 2020, all non-screenable cancers experienced statistically significant disruptions in both early and late stages across the US [6]. The pandemic also had a relevant impact on the timely diagnosis of screenable cancers, such as prostate cancer (PCa) [7]. Indeed, the diagnosis of early-stage PCa was 20.8% lower than expected (95% prediction interval [PI], 19.1-22.5%) [8, 9], with a negative impact on the quality of life of prostate cancer patients and increasing rates of advanced or metastatic cases [10].  
Accordingly, a notable shortfall was observed in the number of cancer treatments delivered during the first year of the COVID-19 pandemic due to the reduction in the number of cancer diagnoses [11].  
Although the World Health Organization (WHO) has declared the end of the pandemic [12], hospitals are still experiencing the consequences of that period, resulting in longer waiting lists and disrupted follow-ups. This establishes the relevance and timeliness of our research. It emphasizes that even though the pandemic is officially over, its effects on healthcare, particularly for sensitive diagnoses such as PCa, are ongoing.  
Even though PCa has a relatively slow progression, its early detection is crucial to guarantee optimal cancer control and functional outcomes [13].  
In this perspective, we aimed to evaluate the impact of the COVID-19 pandemic on PCa diagnosis by comparing it with the pre-pandemic and post-pandemic period in terms of the number of prostate biopsies performed and the number and type of PCa diagnosed.
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 4 Proposed Architecture for C2B Hybrid Model
### The Main Building Blocks of the Proposed C2B Hybrid Model
#### 4.1.1 Data Preprocessing  
This section describes the preprocessing steps applied to all the programs in the CodeSearchNet dataset. During inference, the preprocessing steps are applied to every query code to convert it into a standard form to be input to the architecture. Preprocessing is required to clean and normalize the source codes to improve the model performance. We employ the following steps to preprocess the codes.  
* Removal of unnecessary code: The irrelevant parts of the source code such as unnecessary whitespaces and redundant comments are discarded. However, essential comments that aid in interpreting complex code are retained. This process leaves us with the crucial parts of the code including the function names, parameters, user-defined identifiers, variable names values, and comments.
* Tokenization: Tokenization is the process of splitting the code fragments into smaller units called tokens. It is one of the crucial steps involved in improving the model performance by handling out-of-the-vocabulary (OOV) words. Similar to BERT and GPT, we employ CodeT5's pretrained byte-pair-encoding (BPE) tokenizer [84], which is trained as proposed by [PERSON] et al. [85]. The BPE tokenizer reduces the sequence length and has been determined to work better on the understanding and generation tasks [18].
* Code-specific features: As proposed in CodeT5, we extract the code-specific features by leveraging the token-type (identifier) information in the source code. The identifiers are code tokens that are common to many programming languages capturing rich code semantics. The CodeT5 tokenizer uses the identifier information and converts the code into features to be utilized during training.
* Encoding programs: The original CodeT5 architecture is pretrained on natural language (NL) and programming language (PL) description pairs (NL-PL) or PL-PL pairs. It concatenates the tokens of the bimodal inputs by putting a [SEP] token between them. The [SEP] token acts as a separation between the PL-PL or NL-PL pairs. Since we modify the architecture to take as input a single code, we use [P AD] tokens after the code tokens to pad the tokens to their maximum length and ignore the [SEP] token. Our input sequence can be represented as \(([CLS],c_{1},c_{2},c_{3},...,c_{n},[PAD],[PAD],...,[PAD])\), where  
Figure 2: Proposed architecture for C2B hybrid model.  
\([CLS]\) represents the classification token and \(c\) and \(n\) represent the individual code tokens and number of tokens in the program, respectively.
# Three-Dimensional Formation Control for Robot Swarms
## 5 Covering the Sensing Holes Inside the User-Specified Shape
### MATLAB Simulation of Algorithm 1
#### 6.1.1 Change the Sensing Range  
One next presents the effect of changing \(r_{sc}\). The sensing range \(r_{sc}\) varies from 50 to 70 m. This implies that the radius of a senseSphere is 70 m.  
Under the rendezvous controls in Section 4.2, we first make all ARs rendezvous at the leader located at the origin. Thereafter, Algorithm 1 is applied to make the ARs cover the 3D sphere, while setting \(r_{sc}\) as 70 m. Figure 4 shows the path of each maneuvering AR until the 3D sphere is entirely covered. The path of a maneuvering AR is depicted with green line segments. The final position of a maneuvering AR is depicted as a blue diamond. See that blue diamonds are marked at end points of each line segment.  
Figure 5 presents the final position of each AR under Algorithm 1. Among 100 ARs, only 34 ARs maneuver for covering the user-specified sphere, while (100-34) ARs do not maneuver at all. Compared to the case in which \(r_{sc}=50\) m, increasing \(r_{sc}\) to 70 decreases the number of maneuvering ARs. The ARs consume 2323 s until the user-specified sphere is entirely covered. A total of 17 s are consumed to perform the MATLAB simulation.  
Figure 3: The ARs\({}^{\prime}\) final positions under our formation controls (Algorithm 1) for covering the user-specified sphere shape (\(r_{sc}=50\) m).
## 5 Discussion  
Timeliness is the most important factor for the damage assessment of buildings after an earthquake. Therefore, the applicability of different MVS models for post-earthquake building damage assessment is discussed from the point of view of the network structures and the robustness. As shown in Figure 7, similar to CasMVSNet, deep-learning-based multi-view stereo networks, such as \(D^{2}\)HC-MVSNet and AA-RMVSNet, have mostly been improved on the basis of MVSNet. The initial MVSNet uses the 3D CNN method to regularize the cost volume and generate the probability. The soft argmin [38] operation calculates the depth value for each pixel in a winner-take-all manner and estimates the initial pixel-level depth map. Finally, the reference image is used to refine the depth map to improve the accuracy of the boundary region and complete refined pixel-level depth map estimation. However, as the input image size increases, the parameters of the model increase exponentially, so the method requires higher memory consumption.